Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

Tang, Shengnan; Yuan, Shouqi; Zhu, Yong

doi:10.3390/pr8101217

Cited by 7 publications

(7 citation statements)

References 103 publications

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“…This is because the cyclic spectral coherence provides a comparative advantage in analyzing signatures with non-linearity, non-stationarity and non-gaussianity. 9 Figure 9(a) shows the confusion matrix of the proposed method using multiclass SVM as meta learner. Based on the proposed approach, the performance of the trained model was also evaluated using the same performance metrics as those of the individual models.…”

Section: Resultsmentioning

confidence: 99%

“…It has been the focus of most research in which different gear diagnostic solutions have been proffered. Vibration signals are sensitive to rotating machine faults, 7 have ease of online implementation, 9 and give a better correlation with gear dynamics. 10 The traditional approaches of fault diagnosis involve a good number of signal processing techniques such as cyclo-stationary analysis, 11 autogram analysis, 12 wavelet analysis, 13 and improved variable mode decomposition.…”

Section: Introductionmentioning

confidence: 99%

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A composite learning approach for multiple fault diagnosis in gears

Inyang

Petrunin

Jennions

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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A major part of Prognostic and Health Management of rotating machines is dedicated to diagnosis operations. This makes early and accurate diagnosis of single and multiple faults an economically important requirement of many industries. With the well-known challenges of multiple faults, this paper proposes a new Blended Ensemble Convolutional Neural Network – Support Vector Machine (BECNN-SVM) model for multiple and single faults diagnosis of gears. The proposed approach is obtained by preprocessing the acquired signals using complementary signal processing techniques. This form inputs to 2D Convolutional Neural Network base learners which are fused through a blended ensemble model for fault detection in gears. Discriminative properties of the complementary features ensure the high capabilities of the approach to give good results under different load, speed, and fault conditions of the gear system. The experimental results show that the proposed method can accurately detect rotating machine faults. The proposed approach compared with other state-of-the-art methods indicates improved overall effectiveness for gear faults diagnosis.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A composite learning approach for multiple fault diagnosis in gears

Inyang

Petrunin

Jennions

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…The mode is applied to the Internet to form a three-tier client/server application structure. The structure clearly divides the three functional levels of the application system, making them logically independent [9]. Its structure is shown in Figure 2.…”

Section: B/s Structurementioning

confidence: 99%

Remote Monitoring and Intelligent Failure Diagnosis of Rotating Machinery Using the B/S Mode

2021

IJOMAM

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In order to reduce the failure wear of rotating machinery, the subsequent economic loss and casualties, the remote vibration monitoring and fault diagnosis technology of rotating machinery based on B/S (browser/server) mode is studied. Firstly, the characteristics and structure of B/S mode, the remote vibration monitoring and fault diagnosis system of rotating machinery are analyzed and discussed. And then the system structure, flow chart, server and client design and implementation method are displayed. Subsequently, the overall scheme of rotating machinery fault diagnosis system based on B/S structure is designed. Furthermore, based on the idea of modularization, the scheme design of each module of the system is carried out, including network data acquisition and simulation experiment. Finally, the data and related information in the process of fault diagnosis are stored by analyzing the performance of database server, map refresh display and rotor phase. The remote monitoring system based on B/S can objectively reflect the vibration signal of rotating machinery, obtain the industrial field data in time, analyze the operation status of the equipment, find and correct the unreasonable elements in the design, and also show new design requirements and develop new products according to the actual needs of the site. It is helpful for on-site monitoring personnel to carry out routine monitoring and diagnosis of the equipment, saving about half of the human and material resources, and the results of remote monitoring are relatively accurate, which can diagnose the fault in time. Remote monitoring and fault diagnosis provide the first-hand information from the scene for scientific research institutes, and combine scientific research and production practice more closely. As a whole, large-scale rotating machinery is the key equipment in the production unit of enterprises. It is important to provide remote monitoring and fault diagnosis for enterprises. The achievements of remote monitoring and fault diagnosis system greatly improve the production efficiency.

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“…However, hydraulic pumps often face rigorous operating conditions such as high temperature, heavy load, high speed, and high pressure, which accelerate the deterioration of the health condition of the hydraulic pumps [ 3 , 4 ]. Therefore, the investigation on the intelligent fault diagnosis of the piston pump plays a practical and significant role in safe and efficient production, personnel health, and so on [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Zhu

Wang

et al. 2021

Sensors

Self Cite

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Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust.

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Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

Cited by 7 publications

References 103 publications

A composite learning approach for multiple fault diagnosis in gears

A composite learning approach for multiple fault diagnosis in gears

Remote Monitoring and Intelligent Failure Diagnosis of Rotating Machinery Using the B/S Mode

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

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