Journal articles on the topic 'Hydraulic machinery Vibration Data processing'
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1
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.
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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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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.
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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.
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Benčat, Ján, Daniel Papán, and Mária Stehlíková. "Dynamic Response of Buildings and Structures due to Microtremor Part 1: Industrial Machines Effects." Advanced Materials Research 969 (June 2014): 125–32. http://dx.doi.org/10.4028/www.scientific.net/amr.969.125.
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In this paper a dynamic characterization and vibration analysis has been used for the detection and identification of the machine processing condition and the effect of the production machinery vibration on building complex structures and production process. For sensitive process machines and structures dynamic response due to production machinery calculation procedures was applied using experimental input data via spectral analysis. The dynamic analysis of the cutting production machinery with extreme vibration level is described, too.
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Liu, Hai Sheng, Jie Zhang, Kai Fu Mi, and Jun Xia Gao. "Simulation on Hydraulic-Mechanical Coupling Vibration of Cold Strip Rolling Mill Vertical System." Advanced Materials Research 694-697 (May 2013): 407–14. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.407.
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Based on vibration problems of a six roller cold rolling mill, hydraulic-machinery coupling vibration system dynamic model of the 2180mm 4-stand tandem cold rolling mills was built integrated the software of MATLAB and ADAMS and simulated. The simulation result was consistent with that of the field test data revealed by rolling mill vibration. Through the comparison of the vertical systems motion displacement, velocity, acceleration under the different work condition, coupling vibration causes and evolution mechanism was analyzed, that had practical value to further control mill motor behavior.
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Liu, Xin, Baoquan Jin, Hongjuan Zhang, and Xuefeng Bai. "Remote Monitoring System for Machinery-electric-hydraulic Coupling Vibration of Food Processing Rolling Mill Screw-down System." Advance Journal of Food Science and Technology 10, no. 5 (February 15, 2016): 360–64. http://dx.doi.org/10.19026/ajfst.10.2083.
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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.
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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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Zhang, Liaojun, Guojiang Yin, Shuo Wang, and Chaonian Guan. "Study on FSI Analysis Method of a Large Hydropower House and Its Vortex-Induced Vibration Regularities." Advances in Civil Engineering 2020 (October 27, 2020): 1–13. http://dx.doi.org/10.1155/2020/7596080.
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The working principle of a large hydropower station is to guide the high-pressure water flow to impact the turbine to rotate and generate electricity. The high-pressure water flow impacts the turbine blades, which forms complex high-speed eddy currents in the spiral case and the draft tube and causes complicated vortex-induced vibration problems. Traditionally used harmonic response methods and dynamic time-history analysis methods are difficult to reflect this complex fluid-solid dynamic coupling problem. In this paper, the bidirectional fluid-structure interaction (FSI) simulation analysis theory for a large hydropower house is studied, and the analysis methods of geometric simulation, mechanical simulation, and vibration energy transmission path simulation are presented. A large-scale 3D fluid-hydraulic machinery-concrete structure coupled model of a hydropower house is established to study the vortex-induced vibration mechanism and coupled vibration law during transient unit operation. A comparison of the fluid results against the in-site data shows good agreement. Structural responses of vibration displacement, velocity, and acceleration reveal coupled regularity of hydraulic machinery-concrete structure-fluid during blades rotating periods, and it comes to the conclusion that the turbine blade rotation is the main vibration source of the hydropower house. The research results can provide a scientific basis for the design and safe operation of the hydropower house.
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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.
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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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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.
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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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Yao, Wei Hao, and Ke Gang Zhao. "The Measurement and Analysis of Torsional Vibration for Rotating Machinery." Applied Mechanics and Materials 365-366 (August 2013): 750–53. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.750.
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In this paper, the virtual instrument technology is used in torsional vibration study to create a set of torsional vibration natural frequency detection scheme for rotating machinery which is based on the characteristics of controllable one-way overrunning clutch. Also, a test system that is highly sampling frequency, good accuracy and fast data processing capability is developed. This system consists of high-precision dynamic torque sensor, microprocessor-based high-speed data relay module, IPC-based workbench. Finally, the measured results and the calculated results were compared, and it shows that the measured and the calculated results come very close.
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Rani, Meenu, Sanjay Dhok, and Raghavendra Deshmukh. "A Machine Condition Monitoring Framework Using Compressed Signal Processing." Sensors 20, no. 1 (January 6, 2020): 319. http://dx.doi.org/10.3390/s20010319.
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The vibration monitoring of ball bearings of a rotating machinery is a crucial aspect for smooth functioning and sustainability of plants. The wireless vibration monitoring using conventional Nyquist sampling techniques is costly in terms of power consumption, as it generates lots of data that need to be processed. To overcome this issue, compressive sensing (CS) can be employed, which directly acquires the signal in compressed form and hence reduces power consumption. The compressive measurements so generated can easily be transmitted to the base station and the original signal can be recovered there using CS reconstruction algorithms to diagnose the faults. However, the CS reconstruction is very costly in terms of computational time and power. Hence, this conventional CS framework is not suitable for diagnosing the machinery faults in real time. In this paper, a bearing condition monitoring framework is presented based on compressed signal processing (CSP). The CSP is a newer research area of CS, in which inference problems are solved without reconstructing the original signal back from compressive measurements. By omitting the reconstruction efforts, the proposed method significantly improves the time and power cost. This leads to faster processing of compressive measurements for solving the required inference problems for machinery condition monitoring. This gives a way to diagnose the machinery faults in real-time. A comparison of proposed scheme with the conventional method shows that the proposed scheme lowers the computational efforts while simultaneously achieving the comparable fault classification accuracy.
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Widodo, Panut, Gunawan Dwi Haryadi, and Achmad Widodo. "Induction Motor Centrifugal Blower Health Diagnostic Based on Color Segmentation of Thermal Image and Vibration Signal Feature." MATEC Web of Conferences 159 (2018): 02002. http://dx.doi.org/10.1051/matecconf/201815902002.
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The rotating machinery requires condition monitoring which its measurement without being intrusive operation, especially on the equipment needed to continue running. One such machinery is a centrifugal blower induction motor. Infrared thermography and vibration are important and effective technologies to diagnose of health condition it without destructive or disturb of operations. The diagnostics of induction motor are based on the analysis results data onto vibration and processing thermal image. This paper focused on thermography image processing based on color segmentation which it will produce ROI (region of interest) images. The ROI image is extracted based on HSV color and shape feature. Feature extraction is intended to determine value of mean, standard deviation, kurtosis, skewness and entropy HSV and shape features (area, perimeter, metric, and eccentricity). The highest RMS (root mean square) vibration data is used as reference to classify data into normal and abnormal. Parameters that can be used to classify normal and abnormal conditions based on data analysis are standard deviation Hue, kurtosis HS, skewness HSV, entropy HSV and metric.
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Wang, Li Yong, Yu Hai Gu, and Guo Xin Wu. "Research on Reciprocating Machinery Fault Diagnosis and Testing Method." Advanced Materials Research 588-589 (November 2012): 147–51. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.147.
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Based on characteristics and operating principle of reciprocating machinery, we presented fault diagnosis and data testing method of reciprocating machinery, which features the testing based on crank phase signal, cylinder pressure, temperature, vibration and ultrasonic signal, developed a embedded data acquisition system, and described system composition and module functions. We also realized a CAN bus-based data transmission and communication mode, introduced the slave computer software working flow of the testing system, and developed the data processing and fault analysis software with LABVIEW. The practical tests show that crank phase signal-based data testing can meet requirements on fault diagnosis and data acquisition of reciprocating machinery.
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Hou, Jiaoyi, Hongyu Sun, Aoyu Xu, Yongjun Gong, and Dayong Ning. "Fault diagnosis of synchronous hydraulic motor based on acoustic signals." Advances in Mechanical Engineering 12, no. 4 (April 2020): 168781402091610. http://dx.doi.org/10.1177/1687814020916107.
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Synchronous hydraulic motors are used in high load conditions. Therefore, the failure of such motors must be promptly detected to avoid severe accidents and economic loss. The automation of signal processing and diagnostic processes in practical engineering applications can help improve engineering efficiency and reduce hazards. As a non-contact acquisition signal, an acoustic signal has easier acquisition than a vibration signal. This article proposes an automatic fault detection method for synchronous hydraulic motors, which uses acoustic signals. The proposed method includes the automatic calculation and pattern recognition of the parameters of fault feature vectors. The automatic calculation of the fault feature vector is based on the combination of wavelet packet energy and the Pearson correlation coefficient. Then, the nearest-neighbor classifier is used for fault diagnosis. This study verifies that the proposed method can effectively identify the normal state, gear wear, gear rust, and barrier block wear. This method provides a solution for the automatic fault diagnosis of synchronous hydraulic motors and other types of quasi-period rotating machinery.
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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.
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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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Wu, Chuan Hui, Yan Gao, and Yu Guo. "Study on Remote Condition Monitoring and Fault Diagnosis System for Rotating Machinery Based on LabVIEW." Advanced Materials Research 430-432 (January 2012): 1939–42. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1939.
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In order to suit the demand of monitoring and fault diagnosis of modern small and medium machinery devices better, this paper discusses the development of machinery condition monitoring and fault diagnosis system of good universality and strong expansibility using LabVIEW. Mainly illuminates vibration signal, temperature signal and electric current signal acquisition module using NI data acquisition hardware; signal analysis module in time domain, frequency domain and joint time–frequency domain using signal processing technology. DataSocket, database and fuzzy diagnosis technique have been utilized enabling this system to monitor and diagnose machinery fault remotely.
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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.
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<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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Zhao, Wu, and Dan Huang. "Experiment and Test Signal Analysis Based on Speed Fluctuation of Torsional Vibration of Large-Scale Rotary Machinery." Advanced Materials Research 346 (September 2011): 501–7. http://dx.doi.org/10.4028/www.scientific.net/amr.346.501.
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A new mode of fault monitoring and controling methods on rotating speed fluctuation was proposed in this study. Torsional vibration model and identification equation of speed fluctuation of large-scale rotary machinery was established based on a two-mass motor driving model. Tachogenerator was adopted to measure speed fluctuation in torsional vibration experiment of large-scale rotary machinery. According to the short time fourier transforms method, the non-steady cyclical or quasi-cyclical characteristics signal of rotating speed fluctuation on elastic shafts were transformed into steady signal to study in a fixed time window function. The methods of monitoring rotating speed fluctuation developed nonlinear stable state signal processing into linear short time fourier transforms signal. The real rotating speed fluctuation solution could be obtained after the data of signal acquisition post-processing by the methods of frequency spectrum analysis and modal analysis. Based on data of signal acquisition, using the methods of fourier phase frequency spectrum, logarithm amplitude frequency spectrum, and self-power spectrum, the quantitative expression under the quantitative analysis stable state was obtained. Through the introduction of realtime signal on rotating speed fluctuation to feedback control system, it is easy to program to realize the real-time on-line torsional vibration monitor of the complex mechanism transmission system on the large scale rotating machinery.
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Li, Zhiming. "Fault Diagnosis of Subway Mechanical Equipment Based on 5G Intelligent Sensor Network Signal Processing." Journal of Sensors 2022 (September 17, 2022): 1–11. http://dx.doi.org/10.1155/2022/3266205.
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The application of traditional feature extraction methods in subway mechanical fault diagnosis has attracted the attention of researchers. Based on the 5G intelligent sensor network signal processing theory, this paper constructs a fault diagnosis model for subway machinery and equipment and analyzes the effectiveness of the information redundancy calculation method proposed in this paper by using the on-site vibration data of subway units. The model is obtained by analyzing the entropy characteristics of the vector vibration signal in each bearing section of the subway machinery and equipment. The entropy value quantitatively reflects the vibration complexity of the rotor in the section from different angles and solves the problem of data quantitative analysis. In the simulation process, based on the 5G intelligent sensor network signal and the fuzzy mean clustering information fusion method, in the process of identifying the fault state of the subway unit, a relatively optimistic identification result was obtained. The experimental results show that, no matter whether the Gaussian kernel or the polynomial kernel is selected, the number of kernel principal components whose cumulative contribution rate is greater than 0.85 decreases with the increase of the kernel parameter, and the fault identification support rate is 85%, that is, the diagnosis results of 3 sets of sample data are nonrolling element faults, which significantly improves the performance of the bearing.
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Zhu, Jia Lin, and Li Li. "Monitoring Mechanical Vibration Amplitude System Design Based on the PVDF Piezoelectric Film Sensor." Applied Mechanics and Materials 556-562 (May 2014): 2110–13. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2110.
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Vibration monitoring of machinery and equipment plays a very important role in judging the equipment maintenance and operational status. This paper discussed the mechanical vibration amplitude measurement system scheme based on PVDF piezoelectric sensors. The entire test system is mainly composed of PVDF piezoelectric film sensors, charge amplifiers, data acquisition and processing and display of valid values. It can display the amplitude of vibration signals in real time and alarm. The measurement system has the advantages of a simple structure and convenient operation.
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Du, Yong Ying, Yu Ning Wang, and Ming Ang Yin. "Application of Virtual Instrument on Condition Monitoring and Fault Diagnosis System of the Rotating Machinery." Advanced Materials Research 542-543 (June 2012): 161–64. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.161.
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In the paper it can be easier to realize the acquisition of the rotating machinery vibration signal and condition monitoring through the configuration the platform of virtual instrumentation. For the data acquisition it is enough to be plus with two acceleration sensors and a counter. The system is divided into parameter setting module, data acquisition, storage and display module, amplitude domain analysis module, time-domain analysis module, frequency domain analysis module, time-frequency domain analysis module and fault diagnosis module. The signal acquisition is got by using the PCI-6024E data acquisition card. And it is can be saved as binary data stream files and waveform data file according to the requirements of the sequence data processing. Signal analysis is conducted by using LabVIEW software and draw out the vibration spectrum diagram in order to achieve fault diagnosis of rotating machinery.
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Verhoeven, J. "Excitation Force Identification of Rotating Machines Using Operational Rotor/Stator Amplitude Data and Analytical Synthesized Transfer Functions." Journal of Vibration and Acoustics 110, no. 3 (July 1, 1988): 307–14. http://dx.doi.org/10.1115/1.3269518.
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Hydraulic excitation forces of rotating machinery are normally determined by direct measurement techniques like strain gages, load cells, etc. This paper presents an indirect method, in which frequency response transfer functions are analytically generated, using linear rotor/stator models. Inverse transfer function matrices are multiplied with operational vibration data to yield fourier transformed operational excitation forces. Analytical excitation techniques and numerical inversion methods of the system transfer function matrix are evaluated. External error sources and guidelines for an error sensitivity analysis of the predicted forces are described. Experimental verification is presented on a large horizontal centrifugal pump, with reasonable results. Typical application is shown on multistage hydro carbon and boilerfeed pumps.
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Zhang, Hai Peng, Hong Wu Chen, Bin Hu, and Cheng Tian. "Signal Acquisition and Analysis of Shaft Unbalance Vibration Test." Advanced Materials Research 945-949 (June 2014): 1090–93. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1090.
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This paper simulated the unbalance vibration conditions by the vibration test platform, measuring some common characteristic parameters of unbalance vibration fault diagnosis. This paper chose the time-domain analysis method, processing the characteristic parameters of the test, so as to achieve the purpose of vibration diagnosis. Through a large number of experimental data, this paper verified the feasibility and the effectiveness of the proposed approach to the unbalance fault diagnosis. The method proposed in this paper not only can be applied to unbalance fault diagnosis, but also can be promoted to apply to the fault diagnosis of other rotating machinery.
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Jiang, Wanlu, Chenyang Wang, Jiayun Zou, and Shuqing Zhang. "Application of Deep Learning in Fault Diagnosis of Rotating Machinery." Processes 9, no. 6 (May 24, 2021): 919. http://dx.doi.org/10.3390/pr9060919.
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The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.
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Sokolovsky, Artur, David Hare, and Jorn Mehnen. "Cost-Effective Vibration Analysis through Data-Backed Pipeline Optimisation." Sensors 21, no. 19 (October 8, 2021): 6678. http://dx.doi.org/10.3390/s21196678.
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Vibration analysis is an active area of research, aimed, among other targets, at an accurate classification of machinery failure modes. The analysis often leads to complex and convoluted signal processing pipeline designs, which are computationally demanding and often cannot be deployed in IoT devices. In the current work, we address this issue by proposing a data-driven methodology that allows optimising and justifying the complexity of the signal processing pipelines. Additionally, aiming to make IoT vibration analysis systems more cost- and computationally efficient, on the example of MAFAULDA vibration dataset, we assess the changes in the failure classification performance at low sampling rates as well as short observation time windows. We find out that a decrease of the sampling rate from 50 kHz to 1 kHz leads to a statistically significant classification performance drop. A statistically significant decrease is also observed for the 0.1 s time window compared to the 5 s one. However, the effect sizes are small to medium, suggesting that in certain settings lower sampling rates and shorter observation windows might be worth using, consequently making the use of the more cost-efficient sensors feasible. The proposed optimisation approach, as well as the statistically supported findings of the study, allow for an efficient design of IoT vibration analysis systems, both in terms of complexity and costs, bringing us one step closer to the widely accessible IoT/Edge-based vibration analysis.
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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.
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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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Anwarsha, A., and T. Narendiranath Babu. "Artificial Intelligence-based Fault Diagnosis Procedure for a Sustainable Manufacturing Industry." IOP Conference Series: Earth and Environmental Science 1055, no. 1 (July 1, 2022): 012012. http://dx.doi.org/10.1088/1755-1315/1055/1/012012.
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Abstract All industries are fast transforming into smart industries as part of the sustainable developments in the fourth industrial revolution. Predictive maintenance is one of the most important aspects of such smart industries, to avoid unanticipated machine breakdowns and catastrophic failures. Machine vibration analysis is a common tool for predicting the state of machinery. Vibration analysis involves analysing vibration data collected from machinery and determining whether or not a fault exists. Despite the fact that different methods are utilized to handle data, artificial intelligence is capable of processing such data without the need for human intervention. Every day, a substantial amount of study is carried out in this field. New strategies, on the other hand, that yield greater classification accuracy have yet to be developed. With the use of artificial intelligence approaches, this research article attempts to offer an effective defect detection method for rolling element bearings. To illustrate the practical applications, the technique is used on real datasets which were developed by Case Western Reserve University, which is regarded as a gold standard for testing diagnostic algorithms.
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Mogal, S. P., and D. I. Lalwani. "A Brief Review on Fault Diagnosis of Rotating Machineries." Applied Mechanics and Materials 541-542 (March 2014): 635–40. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.635.
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Vibration in any rotating machines is due to faults like misalignment, unbalance, crack, mechanical looseness etc. Identification of these faults in rotor systems, model and vibration signal based methods are used. Signal processing techniques such as Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Wigner-Ville Distribution (WVD) and Wavelet Transform (WT) are applied to vibration data for faults identification. The intent of the paper is to present a review and summarize the recent research and developments performed in condition monitoring of rotor system with the purpose of rotor faults detection. In present paper discuss the different signal processing techniques applied for fault diagnosis. Vibration response measurement has given information concerning any fault within a rotating machine and many of the methods utilizing this technique are reviewed. A detail review of the subject of fault diagnosis in rotating machinery is presented.
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Fan, Qiyuan. "Design and Realization of Rotating Machinery Conditions Monitoring System Based on LabVIEW." Modern Electronic Technology 2, no. 2 (July 27, 2018): 49. http://dx.doi.org/10.26549/met.v2i2.852.
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Abstract: Nonlinear dynamic analysis of rotating machinery system has always been the hot spot of the rotational dynamics research. This article sets up a rotating machinery condition monitoring system to realize the measurement of system dynamic characteristic parameters based on NI(National Instruments) virtual instruments technology. The measurement of vibration signal of rotating machinery system is achieved by using NI company general data acquisition module of NI Company. Meanwhile, by analyzing and processing the acquired data using LabVIEW 2012, the dynamic characteristics, such as .the speed of the rotating machinery system, the axis trajectory, spectrum parameters, are attained. The measurement results show that the rotating machinery condition monitoring system based on LabVIEW is easy to operate, easy to realize the function extension and maintenance, and that it can be used in the industrial engineering projects with rotation characteristics. LabVIEW as the development tools used by virtual instrument function is very powerful data acquisition software products support is one of the features of it, so using LabVIEW programming and data acquisition is simple and convenient.
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Kolar, Davor, Dragutin Lisjak, Martin Curman, and Michał Pająk. "Condition Monitoring of Rotary Machinery Using Industrial IOT Framework." Tehnički glasnik 16, no. 3 (June 23, 2022): 343–52. http://dx.doi.org/10.31803/tg-20220517173151.
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Modern maintenance strategies, such as predictive and prescriptive maintenance, which derived from the concept of Industry and Maintenance 4.0, involve the application of the Industrial Internet of Things (IIoT) to connect maintenance objects enabling data collection and analysis that can help make better decisions on maintenance activities. Data collection is the initial step and the foundation of any modern Predictive or Prescriptive maintenance strategy because it collects data that can then be analysed to provide useful information about the state of maintenance objects. Condition monitoring of rotary equipment is one of the most popular maintenance methods because it can distinguish machine state between multiple fault types. The topic of this paper is the presentation of an automated system for data collection, processing and interpretation of rotary equipment state that is based on IIoT framework consisting of an IIoT accelerometer, edge and fog devices, web API and database. Additionally, ISO 10816-1 guidance has been followed to develop module for evaluation of vibration severity. The collected data is also visualized in a dashboard in a near-real time and shown to maintenance engineering, which is crucial for pattern monitoring. The developed system was launched in laboratory conditions using rotating equipment failure simulator to test the logic of data collection and processing. A proposed system has shown that it is capable of automated periodic data collection and processing from remote places which is achieved using Node RED programming environment and MQTT communication protocol that enables reliable, lightweight, and secure data transmission.
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Peeters, Cédric, Andreas Jakobsson, Jérôme Antoni, and Jan Helsen. "Improved Time-Frequency Representation for Non-stationary Vibrations of Slow Rotating Machinery." PHM Society European Conference 7, no. 1 (June 29, 2022): 401–9. http://dx.doi.org/10.36001/phme.2022.v7i1.3363.
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The short-time Fourier transform (STFT) is a staple analysis tool for vibration signal processing due to it being a robust, non-parametric, and computationally efficient technique to analyze non-stationary signals. However, despite these beneficial properties, the STFT suffers from high variance, high sidelobes, and a low resolution. This paper investigates an alternative non-parametric method, namely the sliding-window iterative adaptive approach, to use for time-frequency representations of non-stationary vibrations. This method reduces the sidelobe levels and allows for high resolution estimates. The performance of the method is evaluated on both simulated and experimental vibration data of slow rotating machinery such as a multi-megawatt wind turbine gearbox. The results indicate significant benefits as compared to the STFT with regard to accuracy, readability, and versatility.
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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.
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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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Chaoang, Xiao, Tang Hesheng, and Ren Yan. "Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model." Measurement and Control 53, no. 3-4 (January 25, 2020): 649–61. http://dx.doi.org/10.1177/0020294019898725.
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Aiming at the mechanical equipment in the fault diagnosis process, the traditional Shannon–Nyquist sampling theorem is used for data collection, which faces main problems of storage, transmission, and processing of mechanical vibration signals. This paper presents a novel method of compressed sensing reconstruction for axial piston pump bearing vibration signals based on the adaptive sparse dictionary model. First, vibration signals were divided into blocks, and an energy sequence was produced in accordance with the energy of each signal block. Second, the energy sequence of each signal block was classified by the quantum particle swarm optimization algorithm. Finally, the reconstruction of machinery vibration signals was carried out using the K-SVD dictionary algorithm. The average relative error of the reconstructed signal obtained by the proposed algorithm is 4.25%, and the reconstruction time decreases by 43.6% when the compression ratio is 1.6.
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Wang, Chenyang, Wanlu Jiang, Yi Yue, and Shuqing Zhang. "Research on Prediction Method of Gear Pump Remaining Useful Life Based on DCAE and Bi-LSTM." Symmetry 14, no. 6 (May 28, 2022): 1111. http://dx.doi.org/10.3390/sym14061111.
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As a hydraulic pump is the power source of a hydraulic system, predicting its remaining useful life (RUL) can effectively improve the operating efficiency of the hydraulic system and reduce the incidence of failure. This paper presents a scheme for predicting the RUL of a hydraulic pump (gear pump) through a combination of a deep convolutional autoencoder (DCAE) and a bidirectional long short-term memory (Bi-LSTM) network. The vibration data were characterized by the DCAE, and a health indicator (HI) was constructed and modeled to determine the degradation state of the gear pump. The DCAE is a typical symmetric neural network, which can effectively extract characteristics from the data by using the symmetry of the encoding network and decoding network. After processing the original vibration data segment, health indicators were entered as a label into the RUL prediction model based on the Bi-LSTM network, and model training was carried out to achieve the RUL prediction of the gear pump. To verify the validity of the methodology, a gear pump accelerated life experiment was carried out, and whole life cycle data were obtained for method validation. The results show that the constructed HI can effectively characterize the degenerative state of the gear pump, and the proposed RUL prediction method can effectively predict the degeneration trend of the gear pump.
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Jiang, Yu, Li Qin, Yuelei Zhang, and Jingping Wu. "Vibration Signal Processing for Gear Fault Diagnosis Based on Empirical Mode Decomposition and Nonlinear Blind Source Separation." Noise & Vibration Worldwide 42, no. 10 (November 2011): 55–61. http://dx.doi.org/10.1260/0957-4565.42.10.55.
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Gear failures happen frequently in the gear mechanisms, and an unexpected serious gear fault may cause severe damage on the machinery. Hence, precise gear fault detection at the early stage is imperative to ensure the normal operation of the machinery. Independent component analysis (ICA) has been paid more and more attention for its powerful ability of separating the useful vibration source from the multi-sensor observations to enhance the fault feature extraction. This is the so called blind source separation (BSS) procedure. However, the popular ICA model may suffer from two limitations. One is the linear mixture assumption, and the other is the lack of sensor channels. Up to now, only limited research considered the nonlinear ICA model in the field of mechanic fault diagnosis, and techniques for the situation where the number of sensor channels is less than the number of independent sources for gear defect detection are scarce. In order to extract the useful source involved with the gear fault characteristics in single-channel vibration signal processing, this work presents a new method based on the empirical mode decomposition (EMD) and nonlinear ICA. The EMD was firstly employed to decompose the vibration signal into a number of intrinsic mode functions (IMFs), and then these IMFs were taken as the multi-channel observations. The post-nonlinear (PNL) ICA model based on the radial basis function (RBF) neural network was applied to the nonlinear BSS procedure on the IMFs. The experimental vibration data acquired from the gear fault test-bed were processed for the validation of the proposed method. The nonlinear ICA method has been compared with the linear ICA and non-ICA based approaches. The analysis results show that the sensitive characteristics of the gear meshing vibration can be separated from the single channel measurement by the proposed method, and the fault diagnosis precision can be enhanced significantly. The detection rate can be increased by 3.75% or better when the ICA based preprocessing is carried out, and the proposed nonlinear ICA outperforms the linear ICA detection model.
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Pennacchi, P., A. Vania, and N. Bachschmid. "Bivariate analysis of complex vibration data: An application to condition monitoring of rotating machinery." Mechanical Systems and Signal Processing 20, no. 8 (November 2006): 2340–74. http://dx.doi.org/10.1016/j.ymssp.2005.05.008.
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Zhang, Chang Hao, and Yun Qi Chen. "Analysis and Exploration for Automotive Engine Vibration Signal." Applied Mechanics and Materials 401-403 (September 2013): 1230–33. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1230.
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In Modern society, most of car engines are multi cylinder four stroke engines, rotate speed is an important parameter of the engine, engine running status is a comprehensive expression of engine operation condition. It is also the result of the interaction by the gas torque, load torque and inertia moment. So the speed measurement is of great significance. Car engine speed measurement method has a lot of kinds, this article is based on the vibration method to measure, different methods used in vibration signal acquisition, analysis, processing and implementation. The vibration of the automobile engine output signals are continuous changes over time, we can say is a continuous signal. The vibration of the automobile engine output signals are continuous changes over time, we can say at this time is a continuous signal, when we use vibration sensor to gather the signals, a certain number of sampling points that are in different time, same time interval the vibration data resulting from the sampling theorem. At this time we deal with discrete time signals [1, 3]. Because of various vibration interference, The useful information we want to extract has been hidden in a lot of vibration under the disturbance signal, therefore, we carried out on the vibration signal analysis and processing, converting vibration wave in the frequency domain analysis, combining the new method of machinery vibration signal feature extraction, using short time Fourier transform, multiple correlation theory and Hilbert Huang transform combined with the application, making us in post-processing can extract the characteristic signal under the strong noise background [4]. The original signal frequency is obtained, based on related formulas to calculate car engine speed.
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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.
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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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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.
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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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Khalimov, Rustam, and Nikolay Ayugin. "Method for the determination of the processing quality of repair parts of agricultural machinery." BIO Web of Conferences 27 (2020): 00139. http://dx.doi.org/10.1051/bioconf/20202700139.
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The dependence of the technological system state of repair equipment during its operation on the quality of repair of machine parts is revealed. The authors proposed a method to determine the quality of processing of repair parts, including ones of transfer function of the movable friction unit along the sliding guides of the metal-cutting machine under conditions of semi-fluid (mixed) friction in the form of an equivalent oscillatory link. The method is based on the integrated method for calculating and determining the technical state of a typical repair equipment based on the proposed integral criterion for the assessment of its vibration resistance. Using the theoretical developments and experimental data, the transfer functions of the machine support system for various designs were determined. The proposed complex method to calculate and determine the technical state of standard repair equipment will make it possible to develop new design solutions and re-equip the existing machine park of repair enterprises in agro-industrial complex.
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Ye, Dechao, Fajie Duan, Jiajia Jiang, Guangyue Niu, Zhibo Liu, and Fangyi Li. "Identification of Vibration Events in Rotating Blades Using a Fiber Optical Tip Timing Sensor." Sensors 19, no. 7 (March 27, 2019): 1482. http://dx.doi.org/10.3390/s19071482.
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The blade tip timing (BTT) technique has been widely used in rotation machinery for non-contact blade vibration measurements. As BTT data is under-sampled, it requires complicated algorithms to reconstruct vibration parameters. Before reconstructing the vibration parameters, the right data segment should first be extracted from the massive volumes of BTT data that include noise from blade vibration events. This step requires manual intervention, is highly dependent on the skill of the operator, and has also made it difficult to automate BTT technique applications. This article proposes an included angle distribution (IAD) correlation method between adjacent revolutions to identify blade vibration events automatically in real time. All included angles of the rotor between any two adjacent blades were accurately detected by only one fiber optical tip timing sensor. Three formulas for calculating IAD correlation were then proposed to identify three types of blade vibration events: the blades’ overall vibrations, vibration of the adjacent two blades, and vibration of a specific blade. Further, the IAD correlation method was optimized in the calculating process to reduce computation load when identifying every blade’s vibration events. The presented IAD correlation method could be used for embedded, real-time, and automatic processing applications. Experimental results showed that the proposed method could identify all vibration events in rotating blades, even small events which may be wrongly identified by skillful operators.
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Narendiranath Babu, T., T. Manvel Raj, and T. Lakshmanan. "A Review on Application of Dynamic Parameters of Journal Bearing for Vibration and Condition Monitoring." Journal of Mechanics 31, no. 4 (August 2015): 391–416. http://dx.doi.org/10.1017/jmech.2015.6.
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AbstractThe journal bearings are used to support high-speed rotors in turbo machinery which often operate above the rotor first bending critical speed. This bearing provide both lateral support and dynamic coefficients: Stiffness, damping, and mass terms, related to machine vibrations. The various methods of identifying journal bearing dynamic characteristics, from measured data, obtained from different measurement systems, are reviewed. The various approaches to the bearing identification problem are discussed. The various data processing methods in the time and frequency domains are presented. Also, vibration and condition monitoring techniques are presented. In this review, the relative strengths and weaknesses of bearing are presented and developments and trends in improving bearing measurements are documented. Future trends of journal bearing are discussed.
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Mi, Zhe, Tiangang Wang, Zan Sun, and Rajeev Kumar. "Vibration signal diagnosis and analysis of rotating machine by utilizing cloud computing." Nonlinear Engineering 10, no. 1 (January 1, 2021): 404–13. http://dx.doi.org/10.1515/nleng-2021-0032.
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Abstract Vibration signal diagnosis and analysis plays an important role in the industrial machinery since it enhances the machinery performance under supervision. The information regarding the future condition is given by vibration diagnosis techniques which is growing interest for the scientific and industrial communities. Information for failure diagnostic and prediction are provided by the motor vibration through signal processing. The development of mechanical systems fault prognosis and in the last decades, research is done at a very rapid rate. The examination of vibration signal monitoring is done in this paper with the aid of Cyber-Physical Systems (CPS) and Cloud Technology (CT). The machines maintenance strategies are implemented by using the data collected from machines which are based on the fault prognosis. The cloud computing platform is presented in this paper which is having three layers and the unlabelled data is received to generate an interpreted online decision. Feature extraction of the vibration signal is obtained in terms of range, mean value, root mean square value, and standard deviation and crest values. The performance of the model is evaluated by utilizing the classical statistical metrics such as RMSE Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) of the vibration signal. It is obtained that the proposed technique is 25% and 90% better than the Adaptive Neurofuzzy Inference System and the Single Modeling System respectively in terms of RMSE. The performance in terms of MAPE, then the proposed technique outperforms the existing Adaptive Neurofuzzy Inference System and the Single Modeling System by 8 % and 60% respectively. The presented technique is better than the existing Adaptive Neurofuzzy Inference System and the Single Modeling techniques by average of 15% and 30 % respectively.
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Li, Bin, Le Kui, Jingdong Luo, and Shiyong Chen. "Life Prediction Method of Remanufactured Machinery Equipment Based on Vibration Signal Feature Extraction." Advances in Mathematical Physics 2021 (December 20, 2021): 1–11. http://dx.doi.org/10.1155/2021/1962896.
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Mechanical equipment is a key component of mechanical equipment, and its working condition is directly related to the overall performance of mechanical equipment. Accurate evaluation and prediction of the performance degradation trend of mechanical equipment is of great significance to ensure the reliability and safety of the mechanical equipment system. Based on the data of typical faulty equipment, this paper analyzes the energy characteristic parameters of mechanical equipment under different types and degrees of failure in the time domain. Using amplitude spectrum analysis, Hilbert envelope demodulation and wavelet packet decomposition method, and other vibration signal processing methods, preliminary extraction of multiple statistical feature parameters are given. Secondly, in view of the irrelevant and redundant components of multiple statistical parameters, a new method for extracting fault features of mechanical equipment based on variance value and principal component analysis is proposed. This method can effectively classify the fault status of mechanical equipment. The effectiveness of the method is verified by actual equipment signals. After that, the value extracted from the vibration signal of the double-row roller equipment is used as the degradation feature. In order to reduce the influence of irregular characteristics in the vibration signal and simplify the complexity of the vibration signal, the wavelet transform and the support vector machine model are combined, according to the degradation after decomposition. The 95% confidence interval of the predicted value is also given. The SVM model is established based on data characteristics, and single-step and multistep prediction of equipment degradation trends are carried out. The prediction result shows that, according to the mapping position formula, the distribution of equipment degradation prediction points is obtained, and a 95% confidence interval based on the distribution of the prediction points is given. Finally, on the basis of completing feature extraction, this paper applies an unsupervised feature selection method. The sensitive characteristics of life prediction and the prediction results of a single SVM model and a neural network model are compared and analyzed at the same time.
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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.
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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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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.
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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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de la Torre, Oscar, Ignazio Floris, Salvador Sales, and Xavier Escaler. "Fiber Bragg Grating Sensors for Underwater Vibration Measurement: Potential Hydropower Applications." Sensors 21, no. 13 (June 22, 2021): 4272. http://dx.doi.org/10.3390/s21134272.
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The present paper assesses the performance and characteristics of fiber Bragg grating sensors, with a special interest in their applications in hydraulic machinery and systems. The hydropower industry is turning to this technology with high expectations of obtaining high quality data to validate and calibrate numerical models that could be used as digital twins of key assets, further strengthening the sector’s relevant position within industry 4.0. Prior to any validation, fiber Bragg grating sensors’ ability to perform well underwater for long periods of time with minimal degradation, and their ease of scalability, drew the authors´ attention. A simplified modal analysis of a partially submerged beam is proposed here as a first step to validate the potential of this type of technology for hydropower applications. Fiber Bragg grating sensors are used to obtain the beam’s natural frequencies and to damp vibrations under different conditions. The results are compared with more established waterproof electric strain gauges and a laser vibrometer with good agreement. The presence of several sensors in a single fiber ensures high spatial resolution, fundamental to precisely determine vibration patterns, which is a main concern in this industry. In this work, the beam’s vibration patterns have been successfully captured under different excitations and conditions.
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Wang, Ze Wen, Wei Li, Gong Bo Zhou, and Bo Wu. "Application of Random Average Method in Remain Useful Life Prediction of Rolling Bearing." Applied Mechanics and Materials 615 (August 2014): 335–40. http://dx.doi.org/10.4028/www.scientific.net/amm.615.335.
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Remain useful life (RUL) prediction technology which is significant in the condition based maintenance (CBM) is a hot research topic nowadays. Rolling bearing is a basic component widely used in the mechanical industry, and its reliability affects the operation of rotating machinery. On the basis of traditional RUL technology for rolling bearing, a method named random average method (RAM) is introduced into RUL prediction and the implementation of it is instructed in detail via the processing of vibration data in full life of rolling bearing. Compared to traditional method, the proposed method based on RAM is better in both accuracy and timeliness.
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BAKSHTANIN, A. M., and M. A. SHIRYAEVA. "THE DYNAMIC FREQUENCIES AND SHAPES OF VIBRATIONS RESEARCH OF THE DAM OF THE CHIRKEYSKAYA HPP." Prirodoobustrojstvo, no. 4 (2021): 75–84. http://dx.doi.org/10.26897/1997-6011-2021-4-75-84.
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The purpose of the research is to monitor the amplitude-frequency characteristics on the example of the Chirkeyskaya HPP dam for further assessment of the state of hydraulic structures and hydroelectric units of the HPP, as well as to develop an automated system for seismometric control of hydraulic structures of the Chirkeyskaya HPP. The article presents the results of modeling and calculations of dynamic test data of the Chirkeyskaya HPP dam using the primary processing of measurement results to identify the quality of information processing by existing methods. The paper presents the results of measurements to create a mathematical model of the dam of the Chirkeyskaya HPP. Methods were used based on the study of the statistical characteristics of vibrations arising under the infl uence of the dynamic effects of equipment and the external environment for the experimental determination of the frequencies and modes of natural vibrations of the hydraulic structures of the Chirkeyskaya HPP. Spatial-frequency diagrams of the vibration intensity distribution in the radial direction were constructed, an algorithm was implemented to use data for both reference points when identifying natural frequencies and dam shapes, a spatial-frequency distribution diagram was constructed, averaged over two reference points, smoothed interpolated along the dam with a uniform step the conversion factor at the ridge level.
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Safizadeh, M. S., and A. Golmohammadi. "Ball bearing fault detection via multi-sensor data fusion with accelerometer and microphone." Insight - Non-Destructive Testing and Condition Monitoring 63, no. 3 (March 1, 2021): 168–75. http://dx.doi.org/10.1784/insi.2021.63.3.168.
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Early detection of defects in bearings is essential to avoid the complete failure of machinery and the associated costs. This study presents a novel method for fault diagnosis of bearings using sensor fusion with a microphone and an accelerometer. The system has five modules, namely data acquisition, signal processing, feature extraction, classification and decision-making. A test-rig is designed to collect acoustic and vibration signals. Then, for each signal, indices are calculated in the time and frequency domains. After using principal component analysis (PCA) for feature extraction, the k-nearest neighbours (kNN) method is used in the classification module. Finally, a decision on the kind of fault and its size is made based on the decision fusion module. The aim of this study is to propose a fusion method to improve the effectiveness and reliability of bearing defect diagnosis compared to what can be achieved with vibration or acoustic measurements alone. The results obtained from this preliminary study show that condition monitoring using the accelerometer is the more effective technique for determining the type of fault, while the microphone is effective for classifying the size of fault. Experimental results also confirm these findings.