Application of Cyclic Spectral Analysis in Diagnosis of Bearing Faults in Complex Machinery

Mahvash, Ali; Lakis, A. A.

doi:10.1080/10402004.2015.1050135

Cited by 19 publications

(8 citation statements)

References 18 publications

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“…Spectral correlation has been developed to investigate cyclostationary characteristics of signals by extracting periodic energy features, and it has been applied successfully to vibration signals for detecting and diagnosing bearing problems. 13–16 As such, spectral correlation is something of a milestone in nonstationary signal analysis, in particular, for bearing vibration signals. Spectral correlation is very effective at extracting energy oscillation information from cyclostationary signals.…”

Section: Introductionmentioning

confidence: 99%

A phase linearisation–based modulation signal bispectrum for analysing cyclostationary bearing signals

et al. 2020

Structural Health Monitoring

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Bearings are used as the most important load-carrying transmission components in various machines, thus subjecting to a number of faults including wear, fatigue pitting, cracks and so on. Fault detection and diagnosis of bearings can effectively prevent the machine from such typical failures and subsequent consequences. The faults in bearings can lead to the vibration signals that exhibit cyclostationary characteristics due to the inevitably random phase noise (or slippage between bearing components). In this article, a phase linearisation–based modulation signal bispectrum is proposed to tune up the cyclostationary bearing signal into a periodic waveform by linearizing the instantaneous phase of the narrow frequency band signals. In this way, the signal becomes more deterministic and modulation signal bispectrum can be effectively applied to suppression noise and obtain accurate and robust diagnosis results. As a result, this fault detector can achieve high performance in characterising the nonstationary bearing vibration signals and hence diagnose the bearing faults even in the case of extremely low signal-to-noise ratio (<−20 dB), which is benchmarked by the method of conventional modulation signal bispectrum in both simulation and experiment studies.

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Section: Introductionmentioning

confidence: 99%

A phase linearisation–based modulation signal bispectrum for analysing cyclostationary bearing signals

et al. 2020

Structural Health Monitoring

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“…It is noted that variable operational conditions are studied in some analysis methods [61][62][63]. In the light of the advantages in the nonstationary signal analysis, spectral correlation has been successfully used for bearing fault diagnosis [64][65][66]. Furthermore, the CSC and CSCoh were employed to diagnose the fault of bearing.…”

Section: Applications Of Cyclostationarity Theory In Fault Diagnosis Of Rotating Machinerymentioning

confidence: 99%

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

2020

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In the light of the significance of the rotating machinery and the possible severe losses resulted from its unexpected defects, it is vital and meaningful to exploit the effective and feasible diagnostic methods of its faults. Among them, the emphasis of the analysis approaches for fault type and severity is on the extraction of useful components in the fault features. On account of the common cyclostationarity of vibration signal under faulty states, fault diagnosis methods based on cyclostationary analysis play an essential role in the rotatory machine. Based on it, the fundamental definition and classification of cyclostationarity are introduced briefly. The mathematical principles of the essential cyclic spectral analysis are outlined. The significant applications of cyclostationary theory are highlighted in the fault diagnosis of the main rotating machinery, involving bearing, gear, and pump. Finally, the widely-used methods on the basis of cyclostationary theory are concluded, and the potential research directions are prospected.

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“…It has been useful in providing the optimum frequency band for demodulation. Considering the bearing fault signal as cyclostationary, cyclic spectral analysis [ 12 ] has been exploited in bearing fault detection. This method has recorded some good results in bearing fault detection, even under a strong masking signal, by utilizing the correlation statistics between spectral components spaced apart by some frequency shift [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach

Inyang

Petrunin

Jennions

2021

Sensors

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Bearings are critical components found in most rotating machinery; their health condition is of immense importance to many industries. The varied conditions and environments in which bearings operate make them prone to single and multiple faults. Widespread interest in the improvements of single fault diagnosis meant limited attention was spent on multiple fault diagnosis. However, multiple fault diagnosis poses extra challenges due to the submergence of the weak fault by the strong fault, presence of non-Gaussian noise, coupling of the frequency components, etc. A number of existing convolutional neural network models operate on a distinct feature that is not enough to assure reliable results in the presence of these challenges. In this paper, extended feature sets in three homogenous deep learning models are used for multiple fault diagnosis. This ensures a measure of diversity is introduced to the health management dataset to obtain complementary solutions from the models. The outputs of the models are fused through blending ensemble learning. Experiments using vibration datasets based on bearing multiple faults show an accuracy of 98.54%, with an improvement of 2.74% in the overall effectiveness over the single models. Compared with other technologies, the results show that this approach provides an improved generalized diagnostic capability.

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Application of Cyclic Spectral Analysis in Diagnosis of Bearing Faults in Complex Machinery

Cited by 19 publications

References 18 publications

A phase linearisation–based modulation signal bispectrum for analysing cyclostationary bearing signals

A phase linearisation–based modulation signal bispectrum for analysing cyclostationary bearing signals

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach

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