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1
Zhuang, Jin Song, Yi Jian Huang, and Fu Sen Wu. "AR Bispectrum Characteristics of Block Forming Machine’s Vibration Driven by Hydraulic Exciter." Advanced Materials Research 295-297 (July 2011): 2249–53. http://dx.doi.org/10.4028/www.scientific.net/amr.295-297.2249.
Повний текст джерелаАнотація:
Block forming machine, as a kind of automatic equipments, can quickly compact blocks. Higher-order spectrum analysis emerges as a new effective method in signal processing, which can describe nonlinear coupling, restrain Gaussian noise and reserve phase components. In the paper, a hydraulic exciter applying to block forming machine will be introduced. Then block forming machine’s random vibration signals during the compacting process would be collected, in order to make use of the sample data to build up a time series autoregressive model and bispectrum of three-order accumulation, to analyze AR bispectrum characteristics of the machine’s vibrate signals under different work conditions.
2
Qu, Shan, Zhe Guan, Eric Verschuur, and Yangkang Chen. "Automatic high-resolution microseismic event detection via supervised machine learning." Geophysical Journal International 222, no. 3 (June 20, 2020): 1881–95. http://dx.doi.org/10.1093/gji/ggaa193.
Повний текст джерелаАнотація:
SUMMARY Microseismic methods are crucial for real-time monitoring of the hydraulic fracturing dynamic status during the development of unconventional reservoirs. However, unlike the active-source seismic events, the microseismic events usually have low signal-to-noise ratio (SNR), which makes its data processing challenging. To overcome the noise issue of the weak microseismic events, we propose a new workflow for high-resolution microseismic event detection. For the preprocessing, fix-sized segmentation with a length of 2*wavelength is used to divide the data into segments. Later on, 191 features have been extracted and used as the input data to train the support vector machine (SVM) model. These features include 63 1-D time/spectral-domain features, and 128 2-D texture features, which indicate the continuity, smoothness, and irregularity of the events/noise. The proposed feature extraction maximally exploits the limited information of each segment. Afterward, we use a combination of univariate feature selection and random-forest-based recursive feature elimination for feature selection to avoid overfitting. This feature selection strategy not only finds the best features, but also decides the optimal number of features that are needed for the best accuracy. Regarding the training process, SVM with a Gaussian kernel is used. In addition, a cross-validation (CV) process is implemented for automatic parameter setting. In the end, a group of synthetic and field microseismic data with different levels of complexity show that the proposed workflow is much more robust than the state-of-the-art short-term-average over long-term-average ratio (STA/LTA) method and also performs better than the convolutional-neural-networks (CNN), for this case where the amount of training data sets is limited. A demo for the synthetic example is available: https://github.com/shanqu91/ML_event_detection_microseismic.
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Krivosheya, Anatoliy, Yevhen Pashchenko, Volodymyr Melnyk, and Kyryl Shcherbyna. "Investigation of the influence of the process of gear honing by diamond worm honing tools on the roughness factor of gear wheels." Strength of Materials and Theory of Structures, no. 106 (May 24, 2021): 296–311. http://dx.doi.org/10.32347/2410-2547.2021.106.296-311.
Повний текст джерелаАнотація:
In the presented article, a new method of finishing is considered in more detail - gear honing of cylindrical gears. Analysis of literature sources shows that the most problematic technological operation is the finishing of gear wheels and gear honing in particular. The difference between the traditional honing of cylindrical gears with disc abrasive honing and the new method of processing with diamond worm honing is shown. The main advantage of this method is that it can be implemented on milling machines. New tools are proposed - diamond worm gears and the technology of their manufacture is described. The modes of processing cylindrical gears with various diamond worm gears are given and the processing method itself is described. The gear wheels that were processed are used in hydraulic pumps and in hydraulic motors. Roughness parameters Rmax (total height of profile), Rz (irregularity height at 10 points), Rq (root mean square deviation of the assessed profile), which correspond to the Ukrainian and European DSTU ISO 4287 standard, were used as a criterion for assessing the quality of gear processing: 2012. As you know, the strength, wear resistance, durability and other parameters depend on the roughness of the working surfaces of the teeth of the gear wheels. Roughness affects the wear of contact surfaces and noise during operation when running in gears, as well as at the time of their starting. The surfaces were compared before and after treatment. Distribution curves were plotted to visualize the experimental data. When using the new processing method, the correction of defects of the previous processing methods is shown. Based on the results of the studies carried out, it can be concluded that the roughness parameters Rmax, Rz, Rq improve on average by 1.5-2 times. This method can be recommended for the finishing of cylindrical gears, as effective and not requiring new equipment, replacing the traditional methods of honing gears, which can be implemented without significant costs at most Ukrainian enterprises.
4
Huang, Weilin. "Seismic signal recognition by unsupervised machine learning." Geophysical Journal International 219, no. 2 (August 7, 2019): 1163–80. http://dx.doi.org/10.1093/gji/ggz366.
Повний текст джерелаАнотація:
SUMMARY Seismic signal recognition can serve as a powerful auxiliary tool for analysing and processing ever-larger volumes of seismic data. It can facilitate many subsequent procedures such as first-break picking, statics correction, denoising, signal detection, events tracking, structural interpretation, inversion and imaging. In this study, I propose an automatic technique of seismic signal recognition taking advantage of unsupervised machine learning. In the proposed technique, seismic signal recognition is considered as a problem of clustering data points. All the seismic sampling points in time domain are clustered into two clusters, that is, signal or non-signal. The hierarchical clustering algorithm is used to group these sampling points. Four attributes, that is, two short-term-average-to-long-term-average ratios, variance and envelope are investigated in the clustering process. In addition, to quantitatively evaluate the performance of seismic signal recognition properly, I propose two new statistical indicators, namely, the rate between the total energies of original and recognized signals (RTE), and the rate between the average energies of original and recognized signals (RAE). A large number of numerical experiments show that when the signal is slightly corrupted by noise, the proposed technique performs very well, with recognizing accuracy, precision and RTE of nearly 1 (i.e. 100 per cent), recall greater than 0.8 and RAE about 1–1.3. When the signal is moderately corrupted by noise, the proposed technique can hold recognizing accuracy about 0.9, recognizing precision nearly to 1, RTE about 0.9, recall around 0.6 and RAE about 1.5. Applications of the proposed technique to real microseismic data induced from hydraulic fracturing and reflection seismic data demonstrate its feasibility and encouraging prospect.
5
Yang, Xiao Qiang, Ya Ming Gao, Ying Liu, and Jun Han. "Study on Universal Testing Platform of Engineering Machinery." Applied Mechanics and Materials 33 (October 2010): 544–48. http://dx.doi.org/10.4028/www.scientific.net/amm.33.544.
Повний текст джерелаАнотація:
Due to the multiple types and complexity of fault diagnosis, the general-purpose testing platform of engineering machinery’s hydraulic system is developed for the maintenance of military equipment. The general function and structure of the testing platform is presented. The hardware system consists of modular circuit, integrates control computer of embedded controller with PXI-interfaced modular instrument, program-controlled device, connector and adapter hardware. And the software program comprises data management module, fault diagnosis module coupled to the data acquisition module, signal processing module, experiment condition control module, database access module, system configuration and self-test as well as help module. Further, the hardware characteristics are showed and the principle of hydraulic testing platform is presented. The universal testing platform offers enormous benefits for fault diagnosis and condition monitoring of military equipment and machinery.
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Skryabin, Vladimir A. "Manufacturing Parts for Hydraulic Systems of Agricultural Machinery under Conditions of Ultrasonic Cutting." Engineering Technologies and Systems 30, no. 4 (December 30, 2020): 624–36. http://dx.doi.org/10.15507/2658-4123.030.202004.624-636.
Повний текст джерелаАнотація:
Introduction. The article deals with the problem of reducing the efforts when processing thin-walled bushings for hydraulic systems of agricultural machines with the ultrasonically activated cutting tool to achieve the specified processing accuracy and surface roughness of parts. Materials and Methods. The article describes the technological standards for ultrasonic cutting. To assess the change in the tangential cutting force, a special device was developed to activate ultrasonically the tool for tangential cutting and corresponding experiments were carried out. Results. An upgrading of a screw-cutting lathe equipped with a special device for ultrasonic cutting of low rigidity thin-walled parts is currently being carried out. The upgraded lathe consists of blocks for processing and measuring experimental research data connected to a personal computer. The upgraded lathe allows evaluating the change in cutting forces under traditional turning and ultrasonic cutting to achieve the specified accuracy and roughness of the part surface during the processing process. Discussion and Сonclusion. Processing low rigidity parts on the modernized equipment has shown that providing the effective conditions of manufacturing thin-walled bushings for agricultural machinery (cutting depth and cutting speed) decreases radial and tangential components of the cutting force that helped to reduce the energy consumption of the cutting process and to stabilize quality of the processing.
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Zhou, Xin, and Xianqing Lei. "Fault Diagnosis Method of the Construction Machinery Hydraulic System Based on Artificial Intelligence Dynamic Monitoring." Mobile Information Systems 2021 (July 15, 2021): 1–10. http://dx.doi.org/10.1155/2021/1093960.
Повний текст джерелаАнотація:
This paper aims to study the fault diagnosis method of the mechanical hydraulic system based on artificial intelligence dynamic monitoring. According to the characteristics of functional principal component analysis (FPCA) and neural network in the fault diagnosis method in the feature extraction process, the fault diagnosis method combining functional principal component analysis and BP neural network is studied and it is applied to the fault of the coordinator hydraulic system diagnosis. This article mainly completed the following tasks: analyzing the structure and working principle of the mechanical hydraulic system, studying the failure mechanism and failure mode of the mechanical hydraulic system, summarizing the common failures of the hydraulic system and the individual failures of the mechanical hydraulic system, and establishing the mechanical hydraulic system. Description of failure mode and effects analysis (FMEA): then, a joint simulation model of the mechanical hydraulic system was established in ADAMS and AMESim, and the fault detection signal of the hydraulic system was determined and compared with the experimental data. At the same time, the simulation data of the cosimulation model were compared with the simulation data of the hydraulic model in MATLAB to further verify the correctness of the model. The functional principal component analysis is used to perform functional processing on sample data, feature parameters are extracted, and the BP neural network is used to train the mapping relationship between feature parameters and fault parameters. The consistency is verified, and the fault diagnosis method is finally completed. The experimental results show that the diagnostic accuracy rates are 0.9848 and 0.9927, respectively, the reliability is significantly improved, close to 100%, and the uncertainty is basically 0, which significantly improves the accuracy of fault diagnosis.
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Yang, Huichen, Rui Hu, Pengxiang Qiu, Quan Liu, Yixuan Xing, Ran Tao, and Thomas Ptak. "Application of Wavelet De-Noising for Travel-Time Based Hydraulic Tomography." Water 12, no. 6 (May 27, 2020): 1533. http://dx.doi.org/10.3390/w12061533.
Повний текст джерелаАнотація:
Travel-time based hydraulic tomography is a promising method to characterize heterogeneity of porous-fractured aquifers. However, there is inevitable noise in field-scale experimental data and many hydraulic signal travel times, which are derived from the pumping test’s first groundwater level derivative drawdown curves and are strongly influenced by noise. The required data processing is thus quite time consuming and often not accurate enough. Therefore, an effective and accurate de-noising method is required for travel time inversion data processing. In this study, a series of hydraulic tomography experiments were conducted at a porous-fractured aquifer test site in Goettingen, Germany. A numerical model was built according to the site’s field conditions and tested based on diagnostic curve analyses of the field experimental data. Gaussian white noise was then added to the model’s calculated pumping test drawdown data to simulate the real noise in the field. Afterward, different de-noising methods were applied to remove it. This study has proven the superiority of the wavelet de-noising approach compared with several other filters. A wavelet de-noising method with calibrated mother wavelet type, de-noising level, and wavelet level was then determined to obtain the most accurate travel time values. Finally, using this most suitable de-noising method, the experimental hydraulic tomography travel time values were calculated from the de-noised data. The travel time inversion based on this de-noised data has shown results consistent with previous work at the test site.
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Castro-Puma, Jose, Miguel Castro-Puma, Verónica More-Sánchez, Juana Marcos-Romero, Elio Huamán-Flores, Claudia Poma-Garcia, and Rufino Alejos-Ipanaque. "Automatic learning algorithm for troubleshooting in hydraulic machinery." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (January 1, 2022): 535. http://dx.doi.org/10.11591/ijeecs.v29.i1.pp535-544.
Повний текст джерелаАнотація:
<span>In Peru, there are many companies linked to the category of heavy machinery maintenance, in which, on the one hand, although it is true they generate a record of events linked to equipment maintenance indicators, on the other hand they do not make efficient use of these data generating operational patterns, through machine learning, that contribute to the improvement of processes linked to the service. In this sense, the objective of this article is to generate a tool based on automatic learning algorithms that allows predicting the location of faults in hydraulic excavators, in order to improve the management of the maintenance service. When developing the research, it was obtained that the algorithm that assembles bagged trees presents an accuracy of 97.15%, showing a level of specificity of 99.04%, an accuracy of 98.56% and a sensitivity of 97.12%. Therefore, the predictive model using the ensemble bagged trees algorithm shows significant performance in locating the system where failures occur in hydraulic excavator fleets. It is concluded then that it was possible to improve aspects associated with the planning and availability of supplies or components of the maintenance service, also optimizing the continuity and response capacity in the maintenance process.</span>
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Xiao-Xia Guo, Xiao-Xia Guo, Rui-Qi Zhang Xiao-Xia Guo, Shu-Hao Liu Rui-Qi Zhang, Chen Wan Shu-Hao Liu, Zhen-Yu Wang Chen Wan, and Rong-Rong Han Zhen-Yu Wang. "Visualization of Rotating Machinery Noise Based on Near Field Acoustic Holography." 電腦學刊 33, no. 4 (August 2022): 215–23. http://dx.doi.org/10.53106/199115992022083304018.
Повний текст джерелаАнотація:
<p>In order to solve the problem of fast identification of the noise source of rotating machinery, the time-space complex envelope model of monopole sound source is studied, and a modulation method of the complex envelope is proposed. A method combining near-field acoustic holography technology and complex envelope information is proposed to reconstruct the sound field and realize the identification of rotating machinery noise sources. Using the overall fluctuation of the signal to identify the noise source of the rotating machinery greatly reduces the amount of calculation, and speeds up the positioning speed while ensuring the positioning accuracy. According to the sound field radiation characteristics of rotating machinery noise, different measurement distances, different sampling points numbers and different reconstruction distances are selected to reconstruct the sound field. The simulation data analysis results show that the near-field acoustic holography technology can still obtain high sound field reconstruction accuracy under the condition of large reconstruction distance, and does not require high sampling points numbers. Using the envelope information extracted by envelope modulation technology to reconstruct the sound field can accurately identify the number and geometric distribution of sound sources. This technology not only speeds up data processing, but also ensures the accuracy of sound field reconstruction.</p> <p> </p>
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Li, Fengmin, Yaoquan Yang, and Tao Gao. "Study on Image Processing Algorithms for Data Matrix in Dotted Domain." International Journal of Advanced Pervasive and Ubiquitous Computing 7, no. 2 (April 2015): 17–26. http://dx.doi.org/10.4018/ijapuc.2015040102.
Повний текст джерелаАнотація:
Dotted Data Matrix two-dimensional bar code is widely used in the field of machinery and electronics, automobile manufacturing, pharmaceutical and medical, military firearm management etc. Compared with the standard Data Matrix two-dimensional bar code, Dotted Data Matrix bar code is composed of solid dots, which has no obvious characteristics of “L” shaped seek border region. The gaps between dotted data matrix modules are too large which increase the difficulty of identification. To solve the problem of the low recognition rate of dotted Data Matrix code, this paper gives the specific processing method which has certain degree of adaptability. This method obtains the size of bar code dotted module mainly by the spot detection algorithm that provides the reference of fixed value for the subsequent processing. Experimental results show that the algorithm can overcome the effects of large clearance, uneven illumination and noise interference in the recognition, and increase the recognition rate.
12
Zhou, Jing Ling, Shu Qian Wu, Guo Qing Wu, Chun Shu Zhai, Mei Mei, Shu Yun Yang, Zhi Ming Chen, and Peng Peng Lu. "Power Spectrum Analysis Based on VB Language." Applied Mechanics and Materials 607 (July 2014): 727–30. http://dx.doi.org/10.4028/www.scientific.net/amm.607.727.
Повний текст джерелаАнотація:
Power spectrum analysis is one of the important part of digital signal processing, various characteristics of the main signal in the frequency domain, is designed based on the limited data and extract the useful signal is submerged in the noise in the frequency domain. In this paper, the VB language to realize the power spectrum analysis can extract the useful signal was drowned in noise in frequency domain based on power spectrum, frequency structure and physical meaning analysis reflected the more obvious, monitoring and diagnosis in rotating machinery fault, power spectrum analysis is very wide.
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Ma, Li Ying, Nai Xing Liang, Yuan Wen Cao, and Shao Xiong Gui. "Research on Test and Simulation of New Type Steering System for Construction Machinery." Applied Mechanics and Materials 241-244 (December 2012): 1974–77. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1974.
Повний текст джерелаАнотація:
This paper briefly analyzed the steering principle and electro-hydraulic control system of four wheel steering (4WS) test platform for construction machinery, and then performed a series of tests in various steering conditions. The experimental data acquisition, processing and analysis were achieved by the testing system so that transfer function of the system has been determined. And then with MATLAB/SIMULINK software the system simulation was given out. The results show that the turning radius of 4WS decreases about 20% than that of traditional two wheel steering (2WS). What’s more, the steering stability of 4WS is greatly improved. The result of this paper has certain theoretical value and good application prospect.
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Makowski, Ryszard, and Radoslaw Zimroz. "Parametric Time-Frequency Map and its Processing for Local Damage Detection in Rotating Machinery." Key Engineering Materials 588 (October 2013): 214–22. http://dx.doi.org/10.4028/www.scientific.net/kem.588.214.
Повний текст джерелаАнотація:
The detection of local damage in rotating machinery (gears, bearings) via vibration signal analysis is one of the most powerful techniques in condition monitoring. However, in some cases, especially in heavy industrial machinery, it is difficult to detect damage because of the poor signal-to-noise ratio of the measured vibration. Therefore it is necessary to use unconventional advanced techniques to enhance the signal. In this paper, a novel approach based on parametric time-frequency analysis and further processing for: i) time-varying spectral content modelling, ii) the identification of informative frequency bands by statistical analysis, iii) local damage detection and iv) cycle identification via cepstral analysis, is presented. The proposed procedure is validated using real vibration data from bearings and gearboxes. It is worth noting that this methodology can be also successfully used in time-varying speed conditions (with limited fluctuation).
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Aristodemo, Lauria, Tripepi, and Rivera-Velasquéz. "Smoothing of Slug Tests for Laboratory Scale Aquifer Assessment—A Comparison Among Different Porous Media." Water 11, no. 8 (July 29, 2019): 1569. http://dx.doi.org/10.3390/w11081569.
Повний текст джерелаАнотація:
A filtering analysis of hydraulic head data deduced from slug tests injected in a confined aquifer with different porous media is proposed. Experimental laboratory tests were conducted in a large-scale physical model developed at the University of Calabria. The hydraulic head data were deduced from the records of a pressure sensor arranged in the injection well and subjected to a processing operation to filter the high-frequency noise. The involved smoothing techniques are the Fourier transform and two types of wavelet transform. The performances of the filtered hydraulic heads were examined for different slug volumes and four model layouts in terms of optimal fitting of the Cooper’s analytical solution. The hydraulic head variations in the confined aquifer were analyzed using wavelet transform in order to discover their energy contributions and frequency oscillations. Finally, the raw and smoothed hydraulic heads were adopted to calculate the hydraulic conductivity of the aquifer.
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Tang, Shengnan, Shouqi Yuan, Yong Zhu, and Guangpeng Li. "An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump." Sensors 20, no. 22 (November 18, 2020): 6576. http://dx.doi.org/10.3390/s20226576.
Повний текст джерелаАнотація:
A hydraulic axial piston pump is the essential component of a hydraulic transmission system and plays a key role in modern industry. Considering varying working conditions and the implicity of frequent faults, it is difficult to accurately monitor the machinery faults in the actual operating process by using current fault diagnosis methods. Hence, it is urgent and significant to investigate effective and precise fault diagnosis approaches for pumps. Owing to the advantages of intelligent fault diagnosis methods in big data processing, methods based on deep learning have accomplished admirable performance for fault diagnosis of rotating machinery. The prevailing convolutional neural network (CNN) displays desirable automatic learning ability. Therefore, an integrated intelligent fault diagnosis method is proposed based on CNN and continuous wavelet transform (CWT), combining the feature extraction and classification. Firstly, CWT is used to convert the raw vibration signals into time-frequency representations and achieve the extraction of image features. Secondly, a new framework of deep CNN is established via designing the convolutional layers and sub-sampling layers. The learning process and results are visualized by t-distributed stochastic neighbor embedding (t-SNE). The results of the experiment present a higher classification accuracy compared with other models. It is demonstrated that the proposed approach is effective and stable for fault diagnosis of a hydraulic axial piston pump.
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Roy, Jonathan, Claude Bazin, and Faïçal Larachi. "Simulation Algorithm for Water Elutriators: Model Calibration with Plant Data and Operational Simulations." Minerals 12, no. 3 (March 1, 2022): 316. http://dx.doi.org/10.3390/min12030316.
Повний текст джерелаАнотація:
A dynamic simulation algorithm based on 1-D transient convection/diffusion transport per particle size class is proposed to simulate a hydraulic classifier operated to selectively remove quartz from an iron oxide concentrate produced by processing the ore from an iron ore mine in northeastern Canada. The calibrated model is used to simulate the operation of dense bed hydraulic classifiers of different sizes and/or under different operating conditions. The simulator predicts the behavior and characteristics of the pulp at different depths within the classifier as a function of time. The simulator is validated by confronting the simulation results to experimental data obtained from sampling industrial and laboratory classifiers. The simulator is then used to assess the role of the fluidization or teeter water and of bed density on the quality of the produced separation of quartz from the valuable iron oxide of the processed ore. The knowledge acquired in the noise-free environment of simulation provides clues on the way to manipulate the hydraulic classifier operating variables in a process control strategy for an industrial scale unit.
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SHIH, MING-HSIANG, and WEN-PEI SUNG. "PREDICTIVE CONTROL AND SIGNAL ON NOISE REDUCTION FOR SEMI-ACTIVE HYDRAULIC DAMPER." International Journal of Structural Stability and Dynamics 07, no. 01 (March 2007): 129–49. http://dx.doi.org/10.1142/s0219455407002198.
Повний текст джерелаАнотація:
In the implementation of active or semi-active control systems, it is necessary to process the measured signals because they are not perfect in reality. At present, the current energy-dissipating method for controlling semi-active dampers is flawed because of some restrictions on processing and measuring the signals. Thus, a detection methodology of signal control is proposed in this research based on the direction of structural motion; a velocity estimating calculator is developed by using the least-square polynomial regression. Comparison of the analytical results and experimental data confirms that the proposed calculator is effective in predicting when to switch the moving direction of a semi-active damper. It can detect when the direction of the structure motion reverses as well as when to compensate the poor influence on the performance of a semi-active damper caused by the delayed response. Additionally, the noise of displacement signal will not affect the phase difference of predictive signals.
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Kim, Seul-Gi, Donghyun Park, and Jae-Yoon Jung. "Evaluation of One-Class Classifiers for Fault Detection: Mahalanobis Classifiers and the Mahalanobis–Taguchi System." Processes 9, no. 8 (August 20, 2021): 1450. http://dx.doi.org/10.3390/pr9081450.
Повний текст джерелаАнотація:
Today, real-time fault detection and predictive maintenance based on sensor data are actively introduced in various areas such as manufacturing, aircraft, and power system monitoring. Many faults in motors or rotating machinery like industrial robots, aircraft engines, and wind turbines can be diagnosed by analyzing signal data such as vibration and noise. In this study, to detect failures based on vibration data, preprocessing was performed using signal processing techniques such as the Hamming window and the cepstrum transform. After that, 10 statistical condition indicators were extracted to train the machine learning models. Specifically, two types of Mahalanobis distance (MD)-based one-class classification methods, the MD classifier and the Mahalanobis–Taguchi system, were evaluated in detecting the faults of rotating machinery. Their performance for fault detection on rotating machinery was evaluated with different imbalanced ratios of data by comparing with binary classification models, which included classical versions and imbalanced classification versions of support vector machine and random forest algorithms. The experimental results showed the MD-based classifiers became more effective than binary classifiers in cases in which there were much fewer defect data than normal data, which is often common in the real-world industrial field.
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Shi, Zhong Pan, Chang Tao Ding, Yan Zhi Zhao, and Tie Shi Zhao. "Error Factors Analysis of Large Range Flexible Jionts Six-Axis Force Sensor." Applied Mechanics and Materials 130-134 (October 2011): 4232–35. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.4232.
Повний текст джерелаАнотація:
In this article, a new large range flexible jionts 6-UPUR six-axis force sensor is proposed, its structure model, measuring principle and structure parameters are given, and error factors are analyzed. The influences of system noise, calibration matrix errors, processing and installation errors, structural deformation on the platform, hydraulic loading system and data acquisition system errors are dicussed and related improvement measures are suggested.
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Chen, Yue Dong, and Chang Zhong Yu. "The Research on the Low Clutch’s Noise Detection Technology which Based on the Wavelet Transform." Advanced Materials Research 433-440 (January 2012): 4082–86. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4082.
Повний текст джерелаАнотація:
The essay introduce the hardware Design based on the Line detection system, and apply the wavelet analysis theory to the low clutch’s fault signal processing to fulfill the low clutch’s noise detection which based on the wavelet transform. Practice shows that the continuous wavelet signal has a strong ability of fault detection, if reasonable choice of wavelet function and various parameters among the fault detection, the local feature of the fault signal can be intuitively got, thus supply the products with a effective tool. The current washing machine clutch all have a washing deceleration function, so it is called as low clutch. As one of the most common parts of rotating machinery, low clutch is also one of the easily damaged parts among the rotating machinery. According to statistics, thirty percent of the rotating machinery’s operational problems caused by the bearing faults[1]. Bearing defects can cause severely machine vibration and generation noise, or even cause damage to the equipment[4]. This article is mainly detect the low clutch’s vibration noise in operation by accelerometer, and deal with the collected data through wavelet transform, thus realize the On-line condition monitoring to the low clutch.
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Ma, Shangjun, Wei Cai, Wenkai Liu, Zhaowei Shang, and Geng Liu. "A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery." Sensors 19, no. 10 (May 24, 2019): 2381. http://dx.doi.org/10.3390/s19102381.
Повний текст джерелаАнотація:
To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spectral distribution features) and deep convolutional neural networks (good classification performance, data-driven design and high transfer-learning ability). First, a vibration signal is subjected to pyramid wavelet packet decomposition, and each sub-band coefficient is used as the input for each channel of a deep convolutional network (DCN). Then, based on the lightweight modeling requirements and techniques, a new DCN structure is designed for the fault diagnosis. The proposed algorithm is compared with the support vector machine algorithm and the published DL algorithms based on a bearing dataset produced by Case Western Reserve University. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of accuracy, memory space, computational complexity, noise resistance, and transfer performance, producing good results.
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Zhang, Yuwei, Jiaju Hong, Haotian Shi, Yucai Xie, Hongpeng Zhang, Shuyao Zhang, Wei Li, and Haiquan Chen. "Magnetic Plug Sensor with Bridge Nonlinear Correction Circuit for Oil Condition Monitoring of Marine Machinery." Journal of Marine Science and Engineering 10, no. 12 (December 3, 2022): 1883. http://dx.doi.org/10.3390/jmse10121883.
Повний текст джерелаАнотація:
Diesel engines in marine power systems often work in extreme environments. Oil monitoring technology can guarantee the operational safety of diesel engines. In this paper, a magnetic plug sensor for oil debris monitoring is proposed to improve sensitivity and accuracy. Through finite element analysis, absolute deviation is reduced by optimizing the sensor structure. A bridge nonlinear correction circuit is designed to make sensitivity consistent over the entire scale range, which can facilitate calibration and data processing. In order to reduce noise and amplify the signal effectively, a signal post-processing circuit is adopted as well, which consists of a first stage filter circuit, a second stage filter, an active filter module, and an instrumentation amplifier. Therefore, this magnetic plug sensor exhibits better sensitivity and accuracy. Furthermore, a void test and a dynamic test are carried out to investigate its performance. There is a linear relationship between the voltage and the particle mass for the sensor with a bridge nonlinear correction circuit. The results illustrate a minimum of 0.033 mg iron debris with a 1.647 signal-to-noise ratio. Additionally, it can capture and detect 47 μm particles with a debris capture rate of over 90%, which allows it to excel in early fault diagnosis as well.
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Zhang, Rui, Jiyan Yi, Hesheng Tang, Jiawei Xiang, and Yan Ren. "Fault Diagnosis Method of Waterproof Valves in Engineering Mixing Machinery Based on a New Adaptive Feature Extraction Model." Energies 15, no. 8 (April 11, 2022): 2796. http://dx.doi.org/10.3390/en15082796.
Повний текст джерелаАнотація:
Due to the complex working medium of oil in construction engineering, the waterproof valve in mixing machinery can easily cause different degrees of failure. Moreover, under adverse working conditions and complicated noise backgrounds, it is very difficult to detect the fault of waterproof valves. Thus, a fault diagnosis method is proposed, especially for the fault detection of waterproof valves as a key component in the construction of mixing machinery. This fault diagnosis method is based on a new adaptive feature extraction model, with multi-path signals to the improved deep residual shrinkage network–stacked denoising convolutional autoencoder (named DRSN–SDCAE). Firstly, the noisy vibration signals collected by the two vibration sensors are preprocessed, and then transmitted to the parallel structure improved DRSN–SDCAE for adaptive denoising and feature extraction. Finally, these results are fused through the feature fusion strategy to realize the effective fault diagnosis of the waterproof valve. The effectiveness of this method was verified through theory and experiments. The experimental results show that the proposed fault diagnosis method based on the improved DRSN–SDCAE model can automatically and effectively extract fault features from noise for fault diagnosis without relying on signal processing technology and diagnosis experiences. When compared with other intelligent fault diagnosis methods, the features extracted from multi-path inputs were more comprehensive than those extracted from single-path inputs, and contained more complete features of hidden data, which significantly improved fault diagnosis accuracy based on these fault features. The contribution of this paper is to learn fault features autonomously in signals with strong and complex noise through a deep network structure, which extends the fault diagnosis method to the field of construction machinery to improve the safe operation and maintainability of engineering machinery.
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Cai, An Jiang, Yan Jun Guo, Shi Hong Guo, and Ming Wei Ding. "The Construction of the Management System of CNC Milling Parameters." Applied Mechanics and Materials 220-223 (November 2012): 385–88. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.385.
Повний текст джерелаАнотація:
CNC Milling Process for the complex characteristics, in order to solve how to quickly and effectively to provide a reasonable and optimal machining parameters of CNC milling process for technologist. The paper takes horizontal machining center DMC60H as a test platform and technological data of shell aluminum alloy hydraulic aircraft engine parts as a study. the paper establish management system of CNC milling parameters which was suitable for machinery manufacturing enterprises. The results showed that: The established parameters of CNC milling system can be better management of various CNC milling technology to effectively manage information and improve the CNC milling process information efficiency, has certain promotion effect to the production of the numerical control milling processing and the development of the manufacturing enterprises.
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Wu, Peng, Gongye Yu, Naiji Dong, and Bo Ma. "Acoustic Feature Extraction Method of Rotating Machinery Based on the WPE-LCMV." Machines 10, no. 12 (December 6, 2022): 1170. http://dx.doi.org/10.3390/machines10121170.
Повний текст джерелаАнотація:
Fault diagnosis plays an important role in the safe and stable operation of rotating machinery, which is conducive to industrial development and economic improvement. However, effective feature extraction of rotating machinery fault diagnosis is difficult in the complex sound field with characteristics of reverberation and multi-dimensional signals. Therefore, this paper proposes a novel acoustic feature extraction method of the rotating machinery based on the Weighted Prediction Error (WPE) integrating the Linear Constrained Minimum Variance (LCMV). The de-reverberation signal is obtained by inputting multi-channel signals into the WPE algorithm using an adaptive optimal parameters selection function with the sound field changes. Then, the incident angle going from the fault source to the center of the microphone array is calculated from the full-band sound field distribution, and the signal is de-noised and fused using the LCMV. Finally, the fault feature frequency is extracted from the fused signal envelope spectrum. The results of fault data analysis of the centrifugal pump test bench show that the Envelope Harmonic Noise Ratio (EHNR) is more than twice that of the original signal after the WPE-LCMV processing. Compared to the Recursive Least Squares and the Resonance Sparse Signal Decomposition (RLS-RSSD) and the parameter optimized Variational Mode Decomposition (VMD), the EHNR has a higher value for all types of faults after applying the WPE-LCMV processing. Furthermore, the proposed method can effectively extract the frequency of bearing faults.
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Yang, Jian Xin, Dong Xiang Shao, Ru Lin, and Guang Lin Wang. "Development of Stiffness Measurement Instrument for Precise Elastic Elements in Electro-Hydraulic Servo Valve Based on Charge-Coupled Device." Key Engineering Materials 579-580 (September 2013): 488–92. http://dx.doi.org/10.4028/www.scientific.net/kem.579-580.488.
Повний текст джерелаАнотація:
According to stiffness measurement of feedback rod and spring tube in electro-hydraulic servo valve, this paper proposed the measured piece displacement method based on CCD image sensors. On this basis, developing a stiffness measurement instrument based on CCD and giving the overall program design. Because ambient noise affect the image quality seriously, reduce measurement accuracy, image pre-processing is the core of software systems. The system can achieve automatic loading, measure, evaluate and output the result. This article select three types measured piece to experiment and calculation, then, calculate and analysis of experimental data.
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Chmiel, Małgorzata, Philippe Roux, and Thomas Bardainne. "High-sensitivity microseismic monitoring: Automatic detection and localization of subsurface noise sources using matched-field processing and dense patch arrays." GEOPHYSICS 84, no. 6 (November 1, 2019): KS211—KS223. http://dx.doi.org/10.1190/geo2018-0537.1.
Повний текст джерелаАнотація:
Recent advancements in seismic data acquisition and computational power have enhanced the deployment of dense seismic monitoring networks. The growing volume of recorded data requires the development of automated techniques to monitor and image zones of seismicity. We have developed an automatic detection and localization method that demands minimal a priori information for retrieval of the spatial distribution of subsurface noise sources (including, but not limited to, microseismic activity), in a reservoir and in the near vicinity during a hydraulic fracturing treatment. This method is based on matched-field processing (MFP), which takes advantage of the phase coherence that is recorded at dense arrays of sensors to localize noise sources. MFP is applied with a distributed set of patch arrays in the context of geophysics exploration. The MFP approach is applied to ambient noise recordings, and it provides results that are consistent with the classic localization methods applied to high-amplitude microseismic signals (in particular, using the relative template-based method). Furthermore, MFP provides enhanced sensitivity of detection and spatially extended information about structural heterogeneities. MFP opens a route to continuous, automatic, statistics-based, and high-sensitivity reservoir monitoring and imaging for geophysics exploration. Potential applications can also be envisaged for seismic monitoring of volcanic and geyser activities, and for other types of hydrothermal activity.
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Wang, Yu, Dejun Ning, and Songlin Feng. "A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis." Applied Sciences 10, no. 10 (May 25, 2020): 3659. http://dx.doi.org/10.3390/app10103659.
Повний текст джерелаАнотація:
In the prognostics health management (PHM) of rotating machinery, the accurate identification of bearing fault is critical. In recent years, various deep learning methods can well identify bearing fault based on monitoring data. However, facing changing operating conditions and noise pollution, the accuracy of these algorithms decreases significantly, which makes the algorithms difficult in practical applications. To solve this problem, a novel capsule network based on wide convolution and multi-scale convolution (WMSCCN) is proposed for fault diagnosis. The proposed WMSCCN algorithm takes one-dimensional vibration signal as an input and no additional manual processing is required. In addition, the adaptive batch normalization (AdaBN) algorithm is introduced to further enhance the adaptability of WMSCCN under noise pollution and load changes. On generalization experiments under different loads, the proposed WMSCCN and WMSCCN-AdaBN algorithms achieve average accuracy rates of 96.44% and 97.44%, respectively, which is superior to other advanced algorithms. In the noise resistance experiment, the proposed WMSCCN-AdaBN can maintain a 92.3% diagnostic accuracy in a strong noise environment with a signal to noise ratio (SNR) = −4 dB, showing a very strong anti-noise ability. When the SNR exceeds 4 dB, the accuracy reaches 100%, indicating that the proposed algorithm has a very good accuracy at low noise levels. Two experiments have effectively verified the validity and generalizability of the proposed model.
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Zhang, Zixin, Tong Yin, Xin Huang, Fan Zhang, Yeting Zhu, and Wei Liu. "Identification and Visualization of the Full-Ring Deformation Characteristics of a Large Stormwater Sewage and Storage Tunnel Using Terrestrial Laser Scanning Technology." Energies 12, no. 7 (April 4, 2019): 1304. http://dx.doi.org/10.3390/en12071304.
Повний текст джерелаАнотація:
Constructing deeply-buried stormwater sewage and storage tunnels is an effective method to mitigate the waterlogging and sewer overflow problems in modern cities. Prior to construction of such tunnels, a structural loading test is essential for acquiring the mechanical responses under complex loading conditions, such as cyclic inner hydraulic head, during which capturing the full-ring deformation of the tunnel lining is significant for a comprehensive understanding of the tunnel’s mechanical behaviors. This paper introduces the application of terrestrial laser scanning (TLS) technology in the full-scale structural loading tests of a large stormwater sewage and storage tunnel, which gives the full-ring deformation throughout the tests. A data processing methodology was developed to extract the key data points of the lining segments from the original data cloud by removing noise points and mitigating data jump, based on which the deformation of testing the lining segments at arbitrary locations can be calculated. Furthermore, a post-processing software was developed to visualize the full-ring deformation. The full-ring deformation at different loading conditions and its evolution under cyclic loading were captured. It is shown that the lining’s convergence deformation is more sensitive to the inner hydraulic head than to the external soil-water pressure, and the deformation cannot fully recover in a water-inflow-and-drainage cycle due to the presence of joints.
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Cui, Hongjiang, Ying Guan, Huayue Chen, and Wu Deng. "A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection." Applied Sciences 11, no. 12 (June 10, 2021): 5385. http://dx.doi.org/10.3390/app11125385.
Повний текст джерелаАнотація:
In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the SNR is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery.
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Rasul, Abdullah, Jaho Seo, and Amir Khajepour. "Development of Sensing Algorithms for Object Tracking and Predictive Safety Evaluation of Autonomous Excavators." Applied Sciences 11, no. 14 (July 9, 2021): 6366. http://dx.doi.org/10.3390/app11146366.
Повний текст джерелаАнотація:
This article presents the sensing and safety algorithms for autonomous excavators operating on construction sites. Safety is a key concern for autonomous construction to reduce collisions and machinery damage. Taking this point into consideration, our study deals with LiDAR data processing that allows for object detection, motion tracking/prediction, and track management, as well as safety evaluation in terms of potential collision risk. In the safety algorithm developed in this study, potential collision risks can be evaluated based on information from excavator working areas, predicted states of detected objects, and calculated safety indices. Experiments were performed using a modified mini hydraulic excavator with Velodyne VLP-16 LiDAR. Experimental validations prove that the developed algorithms are capable of tracking objects, predicting their future states, and assessing the degree of collision risks with respect to distance and time. Hence, the proposed algorithms can be applied to diverse autonomous machines for safety enhancement.
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Dong, Xintong, Hong Jiang, Sheng Zheng, Yue Li, and Baojun Yang. "Signal-to-noise ratio enhancement for 3C downhole microseismic data based on the 3D shearlet transform and improved back-propagation neural networks." GEOPHYSICS 84, no. 4 (July 1, 2019): V245—V254. http://dx.doi.org/10.1190/geo2018-0621.1.
Повний текст джерелаАнотація:
As the seismic responses of unconventional hydraulic fracturing, downhole microseismic signals play an essential role in the exploitation of unconventional oil and gas reservoirs. In geologic structure interpretation and reservoir development, high-quality downhole microseismic data are necessary. However, the characteristics of downhole microseismic signals, such as weak energy and high frequency, bring great difficulty to signal-to-noise ratio enhancement. How to suppress the random noises in 3C downhole microseismic signals becomes problematic. To solve this problem, the 3D shearlet transform is introduced into downhole microseismic data processing. Different from the 2D shearlet transform, the correlation among the 3C of downhole microseismic signals is fully considered in the 3D shearlet transform, which enables the 3D shearlet transform to suppress random noise more effectively. In addition, for accurate selection of 3D shearlet coefficient, the back-propagation (BP) neural network is applied to the selection of coefficients. Unlike conventional threshold functions, BP neural networks can achieve optimal results by repeated training. At the same time, a new weight factor is proposed to improve the misconvergence of BP neural networks. Experimentally our method has been used to process synthetic and real 3C downhole microseismic signals, with results indicating that, compared with conventional methods, our new algorithm exhibits better performance in valid signal preservation and random noise suppression.
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Fawwaz, Dzaky Zakiyal, and Sang-Hwa Chung. "Real-Time and Robust Hydraulic System Fault Detection via Edge Computing." Applied Sciences 10, no. 17 (August 27, 2020): 5933. http://dx.doi.org/10.3390/app10175933.
Повний текст джерелаАнотація:
We consider fault detection in a hydraulic system that maintains multivariate time-series sensor data. Such a real-world industrial environment could suffer from noisy data resulting from inaccuracies in hardware sensing or external interference. Thus, we propose a real-time and robust fault detection method for hydraulic systems that leverages cooperation between cloud and edge servers. The cloud server employs a new approach that includes a genetic algorithm (GA)-based feature selection that identifies feature-to-label correlations and feature-to-feature redundancies. A GA can efficiently process large search spaces, such as solving a combinatorial optimization problem to identify the optimal feature subset. By using fewer important features that require transmission and processing, this approach reduces detection time and improves model performance. We propose a long short-term memory autoencoder for a robust fault detection model that leverages temporal information on time-series sensor data and effectively handles noisy data. This detection model is then deployed at edge servers that provide computing resources near the data source to reduce latency. Our experimental results suggest that this method outperforms prior approaches by demonstrating lower detection times, higher accuracy, and increased robustness to noisy data. While we have a 63% reduction of features, our model obtains a high accuracy of approximately 98% and is robust to noisy data with a signal-to-noise ratio near 0 dB. Our method also performs at an average detection time of only 9.42 ms with a reduced average packet size of 179.98 KB from the maximum of 343.78 KB.
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Huang, Weichao, Ganggang Zhang, Shangbin Jiao, and Jing Wang. "Bearing Fault Diagnosis Based on Stochastic Resonance and Improved Whale Optimization Algorithm." Electronics 11, no. 14 (July 12, 2022): 2185. http://dx.doi.org/10.3390/electronics11142185.
Повний текст джерелаАнотація:
In light of the problem of difficult model parameter selection and poor resonance effects in traditional bearing fault detection, this paper proposes a parameter-adaptive stochastic resonance algorithm based on an improved whale optimization algorithm (IWOA), which can effectively detect bearing fault signals of rotating machinery. First, the traditional WOA was improved with respect to initial solution distribution, global search ability and population diversity generalization, effectively improving the global convergence of the WOA. Then, the parameters of the bistable stochastic resonance model were optimized using the improved WOA, and adaptive adjustment of the stochastic resonance parameters was realized. Finally, the Case Western Reserve University bearing data set and the XJTU-SY bearing data set were used as fault data for the actual bearing to be tested for experimental verification. The signal-to-noise ratios of the detected fault frequencies for the above two data sets were −20.5727 and −21.1289, respectively. Among the algorithms compared, the IWOA proposed in this paper had the highest signal-to-noise ratio of the detected fault frequencies. The experimental results show that the proposed method can effectively detect a weak bearing fault signal in enhanced noise.
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Stańczykiewicz, Arkadiusz, Dariusz Kulak, Krzysztof Leszczyński, Grzegorz Szewczyk, and Paweł Kozicki. "Effectiveness and Injury Risk during Timber Forwarding with a Quad Bike in Early Thinning." Forests 12, no. 12 (November 24, 2021): 1626. http://dx.doi.org/10.3390/f12121626.
Повний текст джерелаАнотація:
Within the majority of forest areas where timber is harvested for industrial and energy purposes, working technologies using highly efficient multi-operational machinery and equipment are employed. The situation is different in fragmented, privately owned forests. In such forests, timber harvesting is mainly based on motor-manual technologies with a high proportion of manual labor, both at the stage of felling and timber processing and at the stage of its transport. The study aimed to characterize the work time structure of the ATV unit driver and his helper, to determine the productivity of this team, and to estimate the risk of injury during manual loading and unloading. Based on the data collected during the field research, the theoretical work time structure, work productivity and costs, and injury risk were estimated as a result of using a professional small trailer equipped with a hydraulic crane for timber forwarding, designed for aggregation with the ATV. The average, calculated productivity of timber forwarding (over an average distance of about 500 m) with manual loading and unloading was almost twice as low as the estimated average productivity of forwarding with mechanical loading and unloading using a hydraulic crane. The total unit costs (including labor costs) of forwarding with manual loading and unloading were almost threefold higher than those of forwarding using a trailer with a hydraulic crane. The use of small forest trailers equipped with a hydraulic crane not only ensures higher productivity and cost effectiveness but also allows reducing (even by several percent) the inconvenience of manual timber handling and the risk of strain of the musculoskeletal system.
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Sabbaghian-Bidgoli, F., and J. Poshtan. "Fault Detection of Broken Rotor Bar Using an Improved form of Hilbert–Huang Transform." Fluctuation and Noise Letters 17, no. 02 (May 2, 2018): 1850012. http://dx.doi.org/10.1142/s0219477518500128.
Повний текст джерелаАнотація:
Signal processing is an integral part in signal-based fault diagnosis of rotary machinery. Signal processing converts the raw data into useful features to make the diagnostic operations. These features should be independent from the normal working conditions of the machine and the external noise. The extracted features should be sensitive only to faults in the machine. Therefore, applying more efficient processing techniques in order to achieve more useful features that bring faster and more accurate fault detection procedure has attracted the attention of researchers. This paper attempts to improve Hilbert–Huang transform (HHT) using wavelet packet transform (WPT) as a preprocessor instead of ensemble empirical mode decomposition (EEMD) to decompose the signal into narrow frequency bands and extract instantaneous frequency and compares the efficiency of the proposed method named “wavelet packet-based Hilbert transform (WPHT)” with the HHT in the extraction of broken rotor bar frequency components from vibration signals. These methods are tested on vibration signals of an electro-pump experimental setup. Moreover, this project applies wavelet packet de-noising to remove the noise of vibration signal before applying both methods mentioned and thereby achieves more useful features from vibration signals for the next stages of diagnosis procedure. The comparison of Hilbert transform amplitude spectrum and the values and numbers of detected instantaneous frequencies using HHT and WPHT techniques indicates the superiority of the WPHT technique to detect fault-related frequencies as an improved form of HHT.
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Yang, Daoguang, Hamid Reza Karimi, and Len Gelman. "A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks." Sensors 22, no. 2 (January 16, 2022): 671. http://dx.doi.org/10.3390/s22020671.
Повний текст джерелаАнотація:
Some artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some information or incomplete use of the information in the signal. To address this problem, three kinds of popular signal processing methods, including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and directly slicing one-dimensional data into the two-dimensional matrix, are used to create four different datasets from raw vibration signal as the input data of four enhancement Convolutional Neural Networks (CNN) models. Then, a fuzzy fusion strategy is used to fuse the output of four CNN models that could analyze the importance of each classifier and explore the interaction index between each classifier, which is different from conventional fusion strategies. To show the performance of the proposed model, an artificial fault bearing dataset and a real-world bearing dataset are used to test the feature extraction capability of the model. The good anti-noise and interpretation characteristics of the proposed method are demonstrated as well.
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Obuchowski, Jakub, Agnieszka Wylomanska, and Radoslaw Zimroz. "Stochastic Modeling of Time Series with Application to Local Damage Detection in Rotating Machinery." Key Engineering Materials 569-570 (July 2013): 441–48. http://dx.doi.org/10.4028/www.scientific.net/kem.569-570.441.
Повний текст джерелаАнотація:
Raw vibration signals measured on the machine housing in industrial conditions are complex and can be modeled as an additive mixture of several processes (with different statistical properties) related to normal operation of machine, damage related to one (or more) of its part, some noise, etc. In the case of local damage in rotating machines, contribution of informative process related to damage is hidden in the raw signal so its detection is difficult. In this paper we propose to use the statistical modeling of vibration time series to identify these components. Building the model of raw signal may be ineffective. It is proposed to decompose signal into set of narrowband sub-signals using time-frequency representation. Next, it is proposed to model each sub-signal in the given frequency range and classify all signals using their statistical properties. We have used several parameters (called selectors because they will be used for selection of sub-signals from time-frequency map for further processing) for analysis of sub-signals. They have base in statistics theory and can be useful for example in testing of normality of data set (vibration time series from machine in good condition is close to Gaussian, damaged not). Results of such modeling will be used in the sub-signals classification procedure but also in defects detection. We illustrate effectiveness of novel technique using real data from heavy machinery system.
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Chen, Zhili, Peng Wang, Zhixian Gui, and Qinghui Mao. "Three-Component Microseismic Data Denoising Based on Re-Constrain Variational Mode Decomposition." Applied Sciences 11, no. 22 (November 19, 2021): 10943. http://dx.doi.org/10.3390/app112210943.
Повний текст джерелаАнотація:
Microseismic monitoring is an important technology used to evaluate hydraulic fracturing, and denoising is a crucial processing step. Analyses of the characteristics of acquired three-component microseismic data have indicated that the vertical component has a higher signal-to-noise ratio (SNR) than the two horizontal components. Therefore, we propose a new denoising method for three-component microseismic data using re-constrain variational mode decomposition (VMD). In this method, it is assumed that there is a linear relationship between the modes with the same center frequency among the VMD results of the three-component data. Then, the decomposition result of the vertical component is used as a constraint to the whole denoising effect of the three-component data. On the basis of VMD, we add a constraint condition to form the re-constrain VMD, and deduce the corresponding solution process. According to the synthesis data analysis, the proposed method can not only improve the SNR level of three-component records, it also improves the accuracy of polarization analysis. The proposed method also achieved a satisfactory effect for field data.
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Wang, Xiao-Jing, Qi-Zheng Zhang, and Chun-Hui Li. "Research on the Finite Time Compound Control of Continuous Rotary Motor Electro-Hydraulic Servo System." Electronics 11, no. 10 (May 10, 2022): 1515. http://dx.doi.org/10.3390/electronics11101515.
Повний текст джерелаАнотація:
Aiming at the influence of friction, leakage, noise and other nonlinear factors on the performance of the electro-hydraulic servo system of a continuous rotary motor, a finite-time composite controller for the aforementioned servo system is proposed. First, a mathematical model of the electro-hydraulic servo system was analyzed, and the input and output angle data of the motor were collected for system identification. Subsequently, the ARMAX identification model of the continuous rotary motor system was obtained. Then, according to the observed advantages, namely faster capability of the finite-time controller (FTC) to converge the system, and ability of the finite-time observer to reduce the steady-state error of the system, the finite-time controller and finite-time state observer of a continuous rotary electro-hydraulic servo motor were respectively designed. Finally, comparison with PID control simulation shows that the compound controller could effectively improve the control accuracy and performance of the system.
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Wamriew, Daniel, Roman Pevzner, Evgenii Maltsev, and Dimitri Pissarenko. "Deep Neural Networks for Detection and Location of Microseismic Events and Velocity Model Inversion from Microseismic Data Acquired by Distributed Acoustic Sensing Array." Sensors 21, no. 19 (October 5, 2021): 6627. http://dx.doi.org/10.3390/s21196627.
Повний текст джерелаАнотація:
Fiber-optic cables have recently gained popularity for use as Distributed Acoustic Sensing (DAS) arrays for borehole microseismic monitoring due to their physical robustness as well as high spatial and temporal resolutions. As a result, the sensors record large amounts of data, making it very difficult to process in real-/semi-real-time using the conventional processing routines. We present a novel approach, based on deep learning, for handling the large amounts of DAS data in real-/semi-real-time. The proposed neural network was trained on synthetic microseismic data contaminated with real-ambient noise from field data and was validated using field DAS microseismic data obtained from a hydraulic fracturing operation. The results indicate that the trained network is capable of detecting and locating microseismic events from DAS data and simultaneously update the velocity model to a high degree of precision. The mean absolute errors in the event locations and the velocity model parameters are 2.04, 0.72, 2.76, 4.19 and 0.97 percent for distance (x), depth (z), P-wave velocity, S-wave velocity and density, respectively. In addition to automation and computational efficiency, deep learning reduces human expert data handling during processing, thus preserving data integrity leading to more accurate and reproducible results.
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Walenna, M. A., A. Royal, I. Jefferson, and G. Ghataora. "Evaluating the Non-Erodible Cement-Bentonite Barrier Material Samples via Hole Erosion Test Apparatus." IOP Conference Series: Earth and Environmental Science 1117, no. 1 (December 1, 2022): 012020. http://dx.doi.org/10.1088/1755-1315/1117/1/012020.
Повний текст джерелаАнотація:
Abstract Because of the damage that dams, levees, and other water-retaining structures cause, their failure has long been a source of concern. Despite the limitations of the testing apparatus (e.g., HET) used to study barrier material erodibility, they are still widely used. In previous studies, there was very little discussion of the hydraulic parameters (e.g., fluid pressure, flow rate, etc.) obtained from the HET test, particularly how they relate to the varying eroded hole. Therefore, the purpose of this paper was to look into that aspect of the relationship as well as the practical aspects of the HET apparatus that must be considered. Fluctuations in pressure and flow rate data collected during testing were the primary issue discovered. The noise from the pump machinery caused the data collected to fluctuate within a specific range, but the fluctuations were controlled and consistent. Throughout the test, the pressure recorded fluctuated ± 10 kPa below the target pressure, while the flow rate fluctuated approximately 0.3 L/min. The test was performed on a smooth-surfaced, uniform-diameter hole; however, this is not always the case for holes in eroded samples. The non-uniform surface can affect the friction between the eroded surface and the eroding fluid, which in turn affects the pressure loss across the erosion pipe. A non-invasive measurement method for determining the diameter of an eroded hole is suggested for future research, and this study provided an empirical relationship between pressure difference, flow rate, and eroded hole diameter. Despite the limitations, the results provided an overview of the expected fluctuation range from the apparatus as well as the relationship between hydraulic parameters.
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Ichikawa, Masaru, Shinnosuke Uchida, Masafumi Katou, Isao Kurosawa, Kohei Tamura, Ayato Kato, Yoshiharu Ito, Mike de Groot, and Shoji Hara. "Case study of hydraulic fracture monitoring using multiwell integrated analysis based on low-frequency DAS data." Leading Edge 39, no. 11 (November 2020): 794–800. http://dx.doi.org/10.1190/tle39110794.1.
Повний текст джерелаАнотація:
Distributed acoustic sensing (DAS) is an effective technique for hydraulic fracture monitoring. It can potentially constrain fracture propagation direction and time while monitoring strain perturbation, such as stress shadowing. In this study, we acquired passive DAS and distributed temperature sensing (DTS) data throughout the entire fracturing operations of adjacent production wells with varying offset lengths from the fiber-optic cable in the Montney tight gas area. We applied data processing techniques to the DAS data to extract low-frequency components (less than 0.5 Hz) and to construct the strain rate and cumulative strain maps for detecting responses related to fracture hits along the fiber-optic cable. We used low-frequency DAS (LF-DAS) results to estimate the fracture hit position and time, and in certain cases, to additionally estimate the fracture connection. By integrating LF-DAS results with DTS results, we detected the temperature changes around the compression response near the fracture hit position and time. Furthermore, we observed that timing of the fracture hit can be constrained more precisely by using high-frequency DAS data (greater than 10 Hz). We estimated the fracture propagation direction and speed from the estimated fracture hit position and time. The fracture propagation direction deviated slightly from a perpendicular line to the fiber direction. In addition, as estimated from the first fracture hit time, the fracture length and fluid injection volume had a proportional relationship. Due to challenges associated with the data, it is important to design data acquisition geometry and fracturing operations on the premise of acquiring LF-DAS data. It is also important to apply an additional noise reduction process to the data.
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Francese, Roberto G., Zoltan Hajnal, and Arnfinn Prugger. "High‐resolution images of shallow aquifers—A challenge in near‐surface seismology." GEOPHYSICS 67, no. 1 (January 2002): 177–87. http://dx.doi.org/10.1190/1.1451490.
Повний текст джерелаАнотація:
A near‐surface multifold high‐resolution seismic reflection experiment was conducted in the vicinity of the waste management area of a potash mine in western Canada. A buried channel was identified in the data, and the stratigraphy of the Quaternary infill of this structure was mapped. The spatial extent of several prominent gravel‐sandy aquifers, which represent the hydrogeologic framework of the region, was outlined by the survey. The seismic signatures also established the hydraulic independence of three major aquifers along the survey line. The complex heterogeneous lithology of the surface cover limited effective elastic‐wave generation to surface sources. This geologic framework also caused propagation of strong diverse coherent‐noise patterns which severely degraded reflected signal. The suppression of those overhelming interfering events required the design of noise‐specific filters and their sequential multistep implementations. Results of forward modeling of background geologic information were crucial factors in the design of the data acquisition program and preliminary choices of the processing parameters, and (along with borehole data) were the primary guidance in the geologic interpretation of the final seismic section. Fundamental procedures were developed for mapping of glacial tills in the Western Canadian Basin, techniques that can be applied in other regions with similar near‐surface glacial stratigraphy. The experiment revealed that even closely spaced borehole information could never duplicate the detail of the subsurface images of the seismic data.
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Brainin, Boris P., Alexey A. Veselov, Vladimir O. Lomakin, Konstantin G. Mikheev, and Alexey I. Petrov. "The Study of the Sound Waves Transmission along the Fluid Line through the TsN-2 Electric Pump." Izvestiya MGTU MAMI 16, no. 2 (January 18, 2023): 149–59. http://dx.doi.org/10.17816/2074-0530-109243.
Повний текст джерелаАнотація:
BACKGROUND: Noise at production site or at any other place where technical equipment operates is a huge issue. It has a strong negative effect on human nervous system, reduces average lifespan and causes a number of severe diseases. That is why reduction of noise, produced by pumps, is one of the current priorities of hydraulic engineering.
AIMS: In this study, the experimental research of sound transmission through an operating pump and a non-operating pump was carried out. The aim of the research is to find out, whether the last stage of a multistage pump is the main source of hydrodynamic noise (HDN) in pressure line (or the first stage in suction line), or all stages somehow contribute to HDN.
METHODS: The experiment was carried out on the TsN-2 two-stage impeller pump. In order to generate a sinusoidal signal, an imbedded generator, a vibration test rig and a power amplifier were used. Data acquisition for measurement of HDN and vibrations was performed with use of a conditioning amplifier, a hydrophone and an accelerometer. A 4-channel spectrum analyzer served as a device for processing the studied signal. In addition, a theoretical calculation, considering some physical assumptions, was carried out in order to obtain a more general and accurate concept.
RESULTS: After completing the experiment, hydrodynamic noise levels and differences for three cases were obtained. These cases are for the switched-on pump, the switched-off pump and for the pump with the removed stage. The data obtained with hydrophones (hydrodynamic noise levels) was correlated with the data obtained with accelerometers (vibration levels). As the correlated data analysis result, the sound insolation distribution over the spectrum was obtained.
CONCLUSIONS: According to the study results, it can be concluded that the absence of one of two stages ambiguously affected on the sound-insolation properties of the pump. Moreover, no firm conclusions can be drawn about the pump operation influence on the change in its sound-insolation properties.
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Liu, Ziqi, Ce Zhang, Bo Jin, Shuo Zhai, and Junkui Dong. "Design and Development of an Embedded Controller for a Hydraulic Walking Robot WLBOT." Applied Sciences 11, no. 12 (June 8, 2021): 5335. http://dx.doi.org/10.3390/app11125335.
Повний текст джерелаАнотація:
In order to meet the requirement for the real-time of the hydraulic walking robot (WLBOT) and the stability of its movement, an embedded controller is proposed, which takes charge of multi-sensor information processing and signal output of the servo valve. The controller is capable of receiving control command and sending processed information while communicating with an embedded single board computer PCM-3365 via Control Area Network (CAN) bus at a 200 Hz frequency. In this paper, an appropriate interrupt cycle is selected and a 2 kHz high-speed control loop is run after we research the relationship between analog-to-digital converter direct memory access (ADC–DMA) interrupt cycle, data volume, and sampling rate. Significantly, the control strategy of WLBOT joint is introduced and a proportional-integral-derivative (PID) compound controller with velocity feedforward compensation (VFC) is realized. Meanwhile, the Chebyshev filtering algorithm is utilized to attenuate the vibration noise of joint signals. What’s more, an impedance controller is designed to gain better locomotion behavior and compliance in joint force control. Finally, the joint angle tracking and robot walking experiments are implemented, where the feasibility of the design and the validity of the control algorithm is verified. The results show that the PID velocity feedforward compensation controller can reduce the maximum tracking error by 39.13% and 71.31% in the knee and hip joint and the impedance control can reduce the standard deviation (SD) of the foot force by 36.06% and 72.79%.
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Ghahfarokhi, Payam Kavousi, Thomas H. Wilson, Timothy Robert Carr, Abhash Kumar, Richard Hammack, and Haibin Di. "Integrating distributed acoustic sensing, borehole 3C geophone array, and surface seismic array data to identify long-period long-duration seismic events during stimulation of a Marcellus Shale gas reservoir." Interpretation 7, no. 1 (February 1, 2019): SA1—SA10. http://dx.doi.org/10.1190/int-2018-0078.1.
Повний текст джерелаАнотація:
Microseismic monitoring by downhole geophones, surface seismic, fiber-optic distributed acoustic sensing (DAS), and distributed temperature sensing (DTS) observations were made during the hydraulic fracture stimulation of the MIP-3H well in the Marcellus Shale in northern West Virginia. DAS and DTS data measure the fiber strain and temperature, respectively, along a fiber-optic cable cemented behind the casing of the well. The presence of long-period long-duration (LPLD) events is evaluated in the borehole geophones, DAS data, and surface seismic data of one of the MIP-3H stimulated stages. LPLD events are generally overlooked during the conventional processing of microseismic data, but they represent significant nonbrittle deformation produced during hydraulic fracture stimulation. In a single stage that was examined, 160 preexisting fractures and two faults of suboptimal orientation are noted in the image logs. We identified two low-frequency ([Formula: see text]) events of large temporal duration (tens of seconds) by comparing the surface seismic data, borehole geophone data, and DAS amplitude spectra of one of the MIP-3H stages. Spectrograms of DAS traces in time and depth reveal that the first low-frequency event might be an injection noise that has footprints on all DAS channels above the stimulated stage. However, the surface seismic array indicates an LPLD event concurrent with the first low-frequency event on DAS. The second LPLD event on DAS data and surface seismic data is related to a local deformation and does not have footprints on all DAS channels. The interpreted events have duration less than 100 s with frequencies concentrated below 10 Hz, and are accompanied by microseismic events.
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Zhang, Tian Jun, Sheng Hong Yu, Jin Hu Ren, and Wei Cui. "The EMD Analysis AE Signals of Rock Failure under Uniaxial Compression." Applied Mechanics and Materials 571-572 (June 2014): 845–52. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.845.
Повний текст джерелаАнотація:
The wavelet packet basis is difficult to be extracted by wavelet analysis at present. To solve this problem, an experiment of Acoustic Emission under uniaxial compression is conducted by SAEU2S acoustic Emission system and Electro-hydraulic servo universal testing machine and the method of empirical mode analysis is adopted to explore the acoustic emission signal in this paper. Firstly with the method of empirical mode decomposition, the acoustic emission signal is decomposed into the forms of intrinsic mode function with several local time scale and residual components, and then these data is analyzed. After the noise-reducing IMF and residual components are refactored, the error between the final and the initial reconstruction signals is less than 10-6. The experiment indicates that the EMD method is effective in processing the local rock acoustic emission signals. The EMD method also provides an efficient way to predict deformation trend of rock damage through deformation of waveform analysis.
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Mantini, Giulia, Laura L. Meijer, Ilias Glogovitis, Sjors G. J. G. In ‘t Veld, Rosita Paleckyte, Mjriam Capula, Tessa Y. S. Le Large, et al. "Omics Analysis of Educated Platelets in Cancer and Benign Disease of the Pancreas." Cancers 13, no. 1 (December 29, 2020): 66. http://dx.doi.org/10.3390/cancers13010066.
Повний текст джерелаАнотація:
Pancreatic ductal adenocarcinoma (PDAC) is traditionally associated with thrombocytosis/hypercoagulation and novel insights on platelet-PDAC “dangerous liaisons” are warranted. Here we performed an integrative omics study investigating the biological processes of mRNAs and expressed miRNAs, as well as proteins in PDAC blood platelets, using benign disease as a reference for inflammatory noise. Gene ontology mining revealed enrichment of RNA splicing, mRNA processing and translation initiation in miRNAs and proteins but depletion in RNA transcripts. Remarkably, correlation analyses revealed a negative regulation on SPARC transcription by isomiRs involved in cancer signaling, suggesting a specific ”education” in PDAC platelets. Platelets of benign patients were enriched for non-templated additions of G nucleotides (#ntaG) miRNAs, while PDAC presented length variation on 3′ (lv3p) as the most frequent modification on miRNAs. Additionally, we provided an actionable repertoire of PDAC and benign platelet-ome to be exploited for future studies. In conclusion, our data show that platelets change their biological repertoire in patients with PDAC, through dysregulation of miRNAs and splicing factors, supporting the presence of de novo protein machinery that can “educate” the platelet. These novel findings could be further exploited for innovative liquid biopsies platforms as well as possible therapeutic targets.