Vibration Based Condition Monitoring of Planetary Gearboxes Operating Under Speed Varying Operating Conditions Based on Cyclo-non-stationary Analysis

Mauricio, Alexandre; Qi, Junyu; Smith, Wade A.; Randall, R.B.; Gryllias, Konstantinos

doi:10.1007/978-3-319-99268-6_19

Cited by 7 publications

(9 citation statements)

References 4 publications

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“…In rotating machines, the bearing defects usually generate modulated signals by the characteristic frequencies of the bearings. Such signals are usually second-order periodicity processes and are able to be separated from other interfering signals to detect and identify their hidden periodic behavior [36][37][38].…”

Section: Cyclic Spectral Coherence (Cscoh)mentioning

confidence: 99%

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A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks

Chen

Mauricio

et al. 2020

Mechanical Systems and Signal Processing

306

112

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Section: Cyclic Spectral Coherence (Cscoh)mentioning

confidence: 99%

“…The CSCoh can be interpreted as the CSC of a whitened signal, which tends to equalize regions with very different energy levels, magnifying weak cyclostationary signals [37].…”

Section: Cyclic Spectral Coherence (Cscoh)mentioning

confidence: 99%

A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks

Chen

Mauricio

et al. 2020

Mechanical Systems and Signal Processing

306

112

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“…Detection and quantification of noise in a gearbox caused due to vibro-impacts between teeth of unloaded gears was also experimentally studied [153]. Studies on cyclostationarity analysis fundamentally use the cyclic statistics to analyze, detect, and estimate signals with hidden periodicities of the occurring fault, modulated by nonstationary speed/load variations observed on wind turbine [154][155][156][157].…”

Section: Cyclostationarity Analysismentioning

confidence: 99%

A Review on Vibration-Based Fault Diagnosis Techniques for Wind Turbine Gearboxes Operating Under Nonstationary Conditions

Sharma

2021

J. Inst. Eng. India Ser. C

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Numerous studies on vibration-based techniques for fault diagnosis of wind turbine gearboxes operating under nonstationary conditions have been reported. In spite, a review on vibration-based condition monitoring and fault diagnosis techniques for gearboxes of wind turbines operating under nonstationary conditions is unavailable. Thus, the objective of this review is to discuss filtering, decomposition, entropy, and cyclostationary analysis techniques, as well as summarizing the remaining issues. This review will discuss various vibration-based diagnostic approaches developed for wind turbine gearboxes under nonstationary conditions, including both simulation and experimental approaches. Studies on dynamic models of gear systems and advanced signal-processing techniques developed for nonstationary conditions are reviewed. Additionally, the importance of multi-sensor and cointegration based approaches is discussed, and intelligent classification methods that have been used to distinguish healthy and faulty gear systems are also reviewed. Finally, the remaining research challenges are outlined.

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“…Cyclic spectral correlation can be the considered as double FT of the signal, which depicts the relationship of the spectral frequency f and the cyclic frequency. In view of the deficiencies of conventional cyclic spectral analysis in data processing under noisy environment, a more effective tool called cyclic spectral coherence (CSCoh) includes extra frequency dimension, with the sensitive advantage of processing cyclostationary signals [94,95,96].…”

Section: Cyclic Spectral Coherencementioning

confidence: 99%

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

2020

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Rotating machinery plays a critical role in many significant fields. However, the unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of great value to explore the precise approaches for fault diagnosis. With the development of the intelligent fault diagnosis methods based on deep learning, convolutional neural network (CNN) has aroused the attention of researchers in machinery fault diagnosis. In the light of the reduction of difficulty in feature learning and the improvement of final diagnosis accuracy, data preprocessing is necessary and crucial in CNN-based fault diagnosis methods. This review focuses on CNN-based fault diagnosis approaches in rotating machinery. Firstly, data preprocessing methods are overviewed. Then, we emphatically analyze and discuss several main techniques applied in CNN-based intelligent diagnosis, principally including the fast Fourier transform, wavelet transform, data augmentation, S-transform, and cyclic spectral analysis. Finally, the potential challenges and research objects are prospected on data preprocessing in intelligent fault diagnosis of rotary machinery. INDEX TERMS Data preprocessing, convolutional neural network, intelligent fault diagnosis, rotating machinery.

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Vibration Based Condition Monitoring of Planetary Gearboxes Operating Under Speed Varying Operating Conditions Based on Cyclo-non-stationary Analysis

Cited by 7 publications

References 4 publications

A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks

A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks

A Review on Vibration-Based Fault Diagnosis Techniques for Wind Turbine Gearboxes Operating Under Nonstationary Conditions

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

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