Academic literature on the topic 'Hydraulic machinery Noise Data processing'
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Journal articles on the topic "Hydraulic machinery Noise Data processing"
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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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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.
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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.
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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.
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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.
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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.
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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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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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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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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.
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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.
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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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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.
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<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>
Books on the topic "Hydraulic machinery Noise Data processing"
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Kleman, Alan. Interfacing microprocessors in hydraulic systems. New York: M. Dekker, 1989.
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Haje, D. Entwicklung eines Informationssystems zur Konstruktion lärmarmer Produkte. Bremerhaven: Wirtschaftsverlag NW, Verlag für neue Wissenschaft GmbH, 1997.
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ZnO bao mo zhi bei ji qi guang, dian xing neng yan jiu. Shanghai Shi: Shanghai da xue chu ban she, 2010.
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Use of CAD/CAM for Fluid Machinery Design and Manufacture. Professional Engineering Publishing, 1988.
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Institution of Mechanical Engineers (Great Britain). Fluid Machinery Committee. and Institution of Mechanical Engineers (Great Britain), eds. Use of CAD/CAM for fluid machinery design and manufacture: Papers presented at a seminar organized by the Fluid Machinery Committee of the Power Industries Division of the Institution of Mechanical Engineers and held at the Institution of Mechanical Engineers on 21 January 1988. London: Published by Mechanical Engineering Publications Limited for the Institution of Mechanical Engineers, 1988.
Find full textBook chapters on the topic "Hydraulic machinery Noise Data processing"
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Little, Max A. "Optimization." In Machine Learning for Signal Processing, 41–70. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.003.0002.
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Decision-making under uncertainty is a central topic of this book. A common scenario is the following: data is recorded from some (digital) sensor device, and we know (or assume) that there is some “underlying” signal contained in this data, which is obscured by noise. The goal is to extract this signal, but the noise causes this task to be impossible: we can never know the actual underlying signal. We must make mathematical assumptions that make this taskp possible at all. Uncertainty is formalized through the mathematical machinery of probability, and decisions are made that find the optimal choices under these assumptions. This chapter explores the main methods by which these optimal choices are made in DSP and machine learning.
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Maradey Lázaro, Jessica Gissella, and Carlos Borrás Pinilla. "Detection and Classification of Wear Fault in Axial Piston Pumps." In Pattern Recognition Applications in Engineering, 286–316. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1839-7.ch012.
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Variable displacement axial piston hydraulic pumps (VDAP) are the heart of any hydraulic system and are commonly used in the industrial sector for its high load capacity, efficiency, and good performance in the handling of high pressures and speeds. Due to this configuration, the most common faults are related to the wear and tear of internal components, which decrease the operational performance of the hydraulic system and increase maintenance costs. So, through data acquisition such as signals of pressure and the digital processing of them, it is possible to detect, classify, and identify faults or symptoms in hydraulic machinery. These activities form the basis of a condition-based maintenance (CBM) program. This chapter shows the developed methodology to detect and classify a wear fault of valve plate taking into account six conditions and the facilities providing by wavelet analysis and ANNs.
Conference papers on the topic "Hydraulic machinery Noise Data processing"
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Ramirez, Alberto, Damir Basic, Josh Merritt, Karen Olson, and Mary Van Domelen. "Automating a Breakthrough Technology for Real-Time Diagnostics: Sealed Wellbore Pressure Monitoring." In SPE Hydraulic Fracturing Technology Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/209161-ms.
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Abstract The upstream oil and gas industry has seen its share of innovation over the past several decades. The driving force behind these changes has always been a relentless push toward operational and capital efficiency. A breakthrough patented pressure diagnostic technique using offset sealed wellbores as monitoring sources was introduced at the 2020 Hydraulic Fracturing Technology Conference (Haustveit et al. 2020). This technique quantifies various hydraulic fracture parameters using only a surface gauge mounted on the sealed wellbore. The authors successfully automated the Sealed Wellbore Pressure Monitoring (SWPM) analysis procedure using a cloud-based analytical platform (CBAP) designed to ingest, process, and analyze high-frequency hydraulic fracturing data (Iriarte et al. 2021b). The minimum data for the analysis consists of the standard frac treatment data combined with the high-resolution pressure gauge data for each sealed wellbore. The team developed machine learning algorithms to identify the key events required by a sealed wellbore pressure analysis: the start, end, and magnitude of each pressure response detected in the sealed wellbore while actively fracturing offset wells. The result is a rapid, repeatable SWPM analysis that minimizes individual interpretation biases. Since then, over 10,000 stages have been analyzed with SWPM in every major North and South American unconventional basin. The next logical step in the process was to move from post-treatment to real-time analyses. This required an extensive data set to train the real-time models. The training data set includes two types of data: active well data including treating pressures and slurry rates for 1000+ stages from all major North American basins; and 2500+ hours of monitoring well pressure and temperature data streams. The authors use signal processing techniques to mitigate noise, easily accommodate business rules, and follow the subject matter experts’ decision logic. The data is combined with high-resolution pressure gauge data. Machine learning algorithms were developed to identify the start, end, and magnitude of each pressure response detected in the sealed wellbores while actively fracturing offset wells. The model updates its predictions as new data are collected, generating predictions every few seconds on average. The length of the streaming window analyzed by the model and the frequency of the analysis can be modified to accommodate a variety of internet and streaming conditions. This approach provides a robust, automated, and extremely performant model that easily accommodates operating constraints. Real-time cloud-based streaming paired with machine learning allows much easier decision making on-the-fly. Moreover, the proposed methods are designed so that real-time updates can be done efficiently. One of the benefits of real-time data is the ability to manage by exception. Using alerts that are triggered by customizable thresholds, remote engineers can be aware of any operational issues. Closely evaluating sealed wellbore pressure responses to changing completion designs in an active well allows further optimization of the completion process along with creating opportunities for saving costs.
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Zhang, Hongbao, Baoping Lu, Lulu Liao, Hongzhi Bao, Zhifa Wang, Xutian Hou, Amol Mulunjkar, and Xin Jin. "Combining Machine Learning and Classic Drilling Theories to Improve Rate of Penetration Prediction." In SPE/IADC Middle East Drilling Technology Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/202202-ms.
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Abstract Theoretically, rate of penetration (ROP) model is the basic to drilling parameters design, ROP improvement tools selection and drill time & cost estimation. Currently, ROP modelling is mainly conducted by two approaches: equation-based approach and machine learning approach, and machine learning performs better because of the capacity in high-dimensional and non-linear process modelling. However, in deep or deviated wells, the ROP prediction accuracy of machine learning is always unsatisfied mainly because the energy loss along the wellbore and drill string is non-negligible and it's difficult to consider the effect of wellbore geometry in machine learning models by pure data-driven methods. Therefore, it's necessary to develop robust ROP modelling method for different scenarios. In the paper, the performance of several equation-based methods and machine learning methods are evaluated by data from 82 wells, the technical features and applicable scopes of different methods are analysed. A new machine learning based ROP modelling method suitable for different well path types was proposed. Integrated data processing pipeline was designed to dealing with data noises, data missing, and discrete variables. ROP effecting factors were analysed, including mechanical parameters, hydraulic parameters, bit characteristics, rock properties, wellbore geometry, etc. Several new features were created by classic drilling theories, such as downhole weight on bit (DWOB), hydraulic impact force, formation heterogeneity index, etc. to improve the efficiency of learning from data. A random forest model was trained by cross validation and hyperparameters optimization methods. Field test results shows that the model could predict the ROP in different hole sections (vertical, deviated and horizontal) and different drilling modes (sliding and rotating drilling) and the average accuracy meets the requirement of well planning. A novel data processing and feature engineering workflow was designed according the characteristics of ROP modelling in different well path types. An integrated data-driven ROP modelling and optimization software was developed, including functions of mechanical specific energy analysis, bit wear analysis and predict, 2D & 3D ROP sensitivity analysis, offset wells benchmark, ROP prediction, drilling parameters constraints analysis, cost per meter prediction, etc. and providing quantitative evidences for drilling parameters optimization, drilling tools selection and well time estimation.
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Eltaleb, Ibrahim, Ali Rezaei, M. Y. Soliman, and Birol Dindoruk. "A Signal Processing Approach for Analysis of Fracture Injection Test in Geothermal Reservoirs: A Case Study on the Utah FORGE Formation." In SPE Hydraulic Fracturing Technology Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/204164-ms.
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Abstract Assessment of in-situ stresses and hydraulic fracturing stimulation are two critical parameters for successful heat extraction from Enhanced Geothermal Systems (EGS). Fracture injection and injection/flow back tests are two conventional techniques for estimating the minimum horizontal stress in subsurface formations. Because of the heat exchange during the test, ultra-low permeability of the host rock, and natural fractures, the conventional methods yield inaccurate results in geothermal reservoirs. In this paper, we present a new methodology based on the signal processing approach for analyzing DFIT in geothermal reservoirs. The applicability of our technique is demonstrated using several test data from the Utah FORGE project. The main advantage of our methodology is that it does not depend on any assumption regarding fracture geometry and rock properties. Also, unlike most similar studies, we consider the effect of heat exchange between fracturing fluid and the hot rock. In our methodology, the recorded pressure and temperature are treated as signals, and a wavelet transform is applied to separate them to high pass (noise) and low pass (approximation) components. Using the noise energy of the two signals, we then identify different events such as fracture closure. Also, an analytical technique is used to correct the pressure by extracting the effect of fluid compressibility and heat exchange between the rock and injected fluid. We show that the G-Function technique underestimates the minimum horizontal stress in tight formations. After applying the corrections for pressure, the underestimation becomes more apparent. However, our approach gives consistent results before and after the pressure correction. Using the developed technique, we analyzed several injection tests from the Utah FORGE project. Both recorded pressure and temperature have been analyzed. Results show that the energy of the pressure signal noise decreases to a minimum level at the fracture closure. The fracture closure is confirmed by applying the same technique on the recorded temperature. The moment of closure using the proposed methodology is compared to the G-function approach, before and after correction of the pressure for temperature. Unlike physics-based techniques, the proposed method does not have any pre-assumption about the fracture's geometry or type of the well. The method solely relies on the pressure and temperature signals that are recorded during the injection and shut-in periods. Combining several analysis techniques to analyze DFIT (including the analysis of monitored temperature for a geothermal reservoir) is unique and maybe the first of its kind.
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Tijsseling, Arris S., Qingzhi Hou, Bjørnar Svingen, and Anton Bergant. "Acoustic Resonance Experiments in a Reservoir-Pipeline-Orifice System." In ASME 2013 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/pvp2013-97534.
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Acoustic resonance in liquid-filled pipe systems is an undesirable phenomenon that cannot always be prevented. It causes noise, vibration, fatigue, instability, and it may lead to damage of hydraulic machinery and pipe supports. If possible, resonance should be anticipated in the design process and be part of the hydraulic-transients analysis. This paper describes acoustic resonance tests carried out at Deltares, Delft, The Netherlands, within the framework of the European Hydralab III programme. The test system is a 49 m long pipeline of 206 mm diameter that is discharging water from a 24 m high reservoir through a 240 mm2 orifice to the open atmosphere. The outflow is partly interrupted by a rotating disc which generates flow disturbances at a fixed frequency in the range 1.5 Hz to 100 Hz. In previous studies [1, 2] a similar system was analysed theoretically. Herein experimental data are presented and interpreted. Steady oscillatory behaviour is inferred from pressures measured at four different positions along the pipeline. Heavy pipe vibration during resonance was observed (visually and audibly) and recorded by a displacement transducer.
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Åman, Rafael, Heikki Handroos, Hannu Kärkkäinen, Jari Jussila, and Pasi Korkealaakso. "Novel ICT-Enabled Collaborative Design Processes and Tools for Developing Non-Road Mobile Machinery." In ASME/BATH 2015 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/fpmc2015-9571.
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The improvement of the energy efficiency is an important topic for non-road mobile machinery developers and manufacturers. These machines normally use fluid power transmission in drivelines and working actuators. New energy efficient technologies, e.g. a hybrid power transmission with an energy recovery feature, have been introduced. Currently most of the manufacturers are still using conventional technologies in their product development process. Human operators have an effect on the overall efficiency of the machines. Taking into account the human effects is difficult and expensive using the conventional design processes and tools. The objective of this study is to provide international machine manufacturers instrumental, yet novel, community and simulation-based (ICT-enabled) tools/methods for the strategic and cost effective development of their product practices and design processes. The development of models and methods will allow for rapid real-time virtual prototyping of complex machines and machine fleets that operate within a number of worksites or geographical conditions. The introduction of this state-of-the art (and going beyond) advancement in real-time virtual technology, simulation, internet based design technologies and software, cyber-physical and big data processing systems, will present a holistic approach to improve the entire product life. Targeted user groups are manufacturers of non-road mobile machinery (i.e. excavators, wheel loaders, etc.). These machines and production systems share the following key features: 1) They are complex mechatronic systems with several interconnections between hydraulic drives; mechanics, electronics, software and 2) they include autonomous, semiautonomous and human driven operated systems. Methods developed will enable machine manufacturers’ access to technologies that will lead to a more cost effective consumer ordinated, life cycle optimization process. This paper will introduce the method of developing customized products in a fast, agile and networked way that will lead to significantly reduced life-cycle costs.
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Marketz, Franz, David Brown, Roman Alyabiev, Pavel Khudorozhkov, and Oleg Sychov. "Offshore CRI Well Performance Diagnostics and Fractured Domain Mapping Using Injection Data Analytics and Hydraulic Fracturing Simulation, Verified Through 4D Seismic and Wireline Logging." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205896-ms.
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Abstract The cuttings re-injection (CRI) well in the Astokh area of Piltun-Astokhskoye field offshore Sakhalin Russia is one of the longest operating drilling waste disposal wells in the oil and gas industry worldwide. The Astokh area has been developed as a waterflood and is operated by Sakhalin Energy, a joint venture between Gazprom, Shell, Mitsui, and Mitsubishi. The Astokh CRI well has been utilized for waste injection for over 16 years. About 300,000 m3 of waste has been disposed into the main injection zone of the CRI well. Monitoring and modelling the CRI process to understand the evolution of the disposal domain is paramount for safeguarding further disposal operations. The disposal domain can be described as a complex system of multiple hydraulic and natural fractures due to injection under fracturing conditions. CRI domain evaluation includes analysis of historical injection pressures to identify the reasons of continuous injection pressure increase with increasing cumulative waste volumes disposed, to confirm domain containment, and to predict remaining domain capacity. Transient pressure analysis has revealed that the fracture closure pressure, driven by pore pressure increase and the accumulation of injected solid-phase waste, is the key parameter affecting injection pressures. Injection intensity, periods of shut-in, large overflushes, and solids-free liquids injections with corresponding solids and stresses redistribution are the other factors that affecting the pressure trends. CRI domain mapping was carried out with history-matched time-lapse 3D hydraulic fracture models. Injection pressure history matching results reveal the fracture geometry evolution during well life. The distribution of the injected liquid phase in the sand layers was modeled with a 3D dynamic reservoir sector model, matched with injection pressures and with formation pressure data in two offset wells, located at a distance of 1 and 2 kilometers, respectively. A matched model was then used to assure fracture containment for future waste disposal and to estimate remaining domain capacity. High-precision temperature and spectral noise logs were acquired in seawater injection and shut-in modes. The log-derived fracture height confirmed the domain size predicted by the matched model. 4D seismic data processing revealed that dimensions of Geomechanically Altered Rock Volume (GARV) were also in the same range as predicted by the model p. The integration of CRI domain evaluation with matched 3D hydraulic fracture models, well logs and 4D seismic demonstrated that injection pressure data collected during every injection cycle may be sufficient to characterize disposal domain evolution and to estimate domain capacity.
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Tzeng, George T. "Encoder-Less Synchronized Averaging Using Order Tracking and Interpolation." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-61148.
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Synchronized averaging is a very useful signal processing technique, in particular, for condition monitoring of rotating machinery. It enhances the signal to noise ratio by attenuating noises that are not repeated from one rotation to the next. Its use, however, is limited due to the costly hardware needed to trigger the sampling at exactly the same angular positions rotation after rotation. This paper describes an improved order tracking technique which employs a Kalman filter to track the instantaneous rotating speed of machinery and an interpolation technique to resample data obtained under constant sampling time interval into data sampled at constant angular increments. Experiments were conducted to validate the proposed algorithm. Comparing the synchronized average obtained by the order tracking algorithm with the true average using encoder triggering, no significant difference can be seen until 3x of meshing frequency. Since the technique only requires band-pass filtered vibration and a once-per-revolution index signal, it is much simpler compared to the existing technique which requires complex and cumbersome hardware to track the rotating speed.
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Gao, Zhanwu, Kai Li, Guanghui Gao, Haiyu Liao, Yadong Zhang, Zheyuan Huang, Yanyan Chen, Yuan Liu, Yuanhua Li, and Ning Shi. "A Closer Look at Hydraulic Fractures: Case Studies of Microseismic Results from Horizontal Monitoring Wells." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205707-ms.
Full textAbstract:
Abstract Amongst different options of hydraulic fracture geometry detection or measurement, microseismic monitoring is a commonly used method to reveal the hydraulic fracture geometry in three-dimensional space. Microseismic monitoring typically requires one or several monitoring wells within an effective range from the treatment well, in which the geophones are set to detect the microseismic events occurring during or after the treatment. In the past, most of the monitoring wells have been vertical wells. We present several recent case studies in which both the treatment and monitoring wells were horizontal wells, which produced some unique and interesting observations beyond the initial expectations. One of the prerequisites of a proper microseismic monitoring of hydraulic fracturing treatment is to place the geophone in the proper position because a long distance between the actual fracturing events and the geophone may result in signal deterioration, which influences the processing and increases the uncertainty. This problem is more severe if the treatment well is a horizontal well because the distance from the geophone to the microseismic events varies between stages. One of the methods to solve this issue is to monitor the microseismic events in a horizontal offset well. As horizontal wells are often batched drilled in clusters for tight or unconventional resource nowadays, the availability of the monitoring well is less of a problem, and the constant distance from the monitoring well to the treatment well may help to generate better data quality and more accurate interpretation result. We implemented horizontal well monitoring in two difference cases between 2018 and 2019. For case A, one horizontal monitoring well was used to monitor 54 fracturing stages in three offset wells, and for case B, we monitored 24 fracturing stages in one offset well. In both cases, the geophone arrays were shifted in multiple positions to fit the distance requirements, and both cases generate satisfying interpretation results. The microseismic results from the two cases showed less uncertainty and better precision of microseismic events after processing, as we expected. What is surprising is this type of monitoring showed a unique physical phenomenon a couple of times, which is a casing background noise indicating excessive fracturing extension over a long distance. This phenomenon was captured in both cases, even with small injection rate and fluid volumes, which can be important information for us to better understand the dynamics of fracture propagation in such geomechanical environment and help to set a new guideline and design reference in the same region.
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Watson, Matt, Jeremy Sheldon, Sanket Amin, Hyungdae Lee, Carl Byington, and Michael Begin. "A Comprehensive High Frequency Vibration Monitoring System for Incipient Fault Detection and Isolation of Gears, Bearings and Shafts/Couplings in Turbine Engines and Accessories." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27660.
Full textAbstract:
The authors have developed a comprehensive, high frequency (1–100 kHz) vibration monitoring system for incipient fault detection of critical rotating components within engines, drive trains, and generators. The high frequency system collects and analyzes vibration data to estimate the current condition of rotary components; detects and isolates anomalous behavior to a particular bearing, gear, shaft or coupling; and assesses the severity of the fault in the isolated faulty component. The system uses either single/multiple accelerometers, mounted on externally accessible locations, or non-contact vibration monitoring sensors to collect data. While there are published instances of vibration monitoring algorithms for bearing or gear fault detection, there are no comprehensive techniques that provide incipient fault detection and isolation in complex machinery with multiple rotary and drive train components. The author’s techniques provide an algorithm-driven system that fulfills this need. The concept at the core of high frequency vibration monitoring for incipient fault detection is the ability of high frequency regions of the signal to transmit information related to component failures during the fault inception stage. Unlike high frequency regions, the lower frequency regions of vibration data have a high machinery noise floor that often masks the incipient fault signature. The low frequency signal reacts to the fault only when fault levels are high enough for the signal to rise over the machinery noise floor. The developed vibration monitoring system therefore utilizes high frequency vibration data to provide a quantitative assessment of the current health of each component. The system sequentially ascertains sensor validity, extracts multiple statistical, time, and frequency domain features from broadband data, fuses these features, and acts upon this information to isolate faults in a particular gear, bearing, or shaft. The techniques are based on concepts like mechanical transmissibility of structures and sensors, statistical signal processing, demodulation, time synchronous averaging, artificial intelligence, failure modes, and faulty vs. healthy vibration behavior for rotating components. The system exploits common aspects of vibration monitoring algorithms, as applicable to all of the monitored components, to reduce algorithm complexity and computational cost. To isolate anomalous behavior to a particular gear, bearing, shaft, or coupling, the system uses design information and knowledge of the degradation process in these components. This system can function with Commercial Off-The-Shelf (COTS) data acquisition and processing systems or can be adapted to aircraft on-board hardware. The authors have successfully tested this system on a wide variety of test stands and aircraft engine test cells through seeded fault and fault progression tests, as described herein. Verification and Validation (V&V) of the algorithms is also addressed.
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Tan, Chin An, Arvind Gupta, and Shaungqing Li. "Application of Independent Component Analysis for Sound Source Separation." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35834.
Full textAbstract:
In this paper, experiments on the application of the independent component analysis (ICA) technique to separate unknown source signals are reported. ICA is one of the fastest growing fields in signal processing with applications to speech recognition systems, telecommunications, and biomedical signal processing. It is a data-transformation technique that finds independent sources of activity from linear mixtures of unknown independent sources. The statistical method to measure independence is to find a linear representation of the non-Gaussian data so that the components are as independent as possible and the mutual information between them is minimum. Although extensive simulations have been performed to demonstrate the power of the learning algorithm for the problems of instantaneous mixing and un-mixing of sources, its application to the noise diagnosis and separation in an industrial setting has not been considered. Noise separation in machinery has a strong basis in the “cocktail problem” in which it is difficult to separate/isolate the voice of a person in a room filled with competing voices and noises. The experiments conducted consist of separating several artificially generated sources of noise. Our results demonstrate that ICA can be effectively employed for such kinds of applications. The underdetermined problem in which there are fewer sensors than sources in the ICA formulation is also examined by applying a time-invariant linear transformation of the acquired signals to identify a single source.