Novel Higher-Order Spectral Cross-Correlation Technologies for Vibration Sensor-Based Diagnosis of Gearboxes

Gelman, Len; Solinski, Krzysztof; Ball, Andrew

doi:10.3390/s20185131

Cited by 16 publications

(18 citation statements)

References 61 publications

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“…This can be attributed to the weakness of this preprocessing method. With the complex spectral components present in the multiple fault signal, the bicoherence does not present the cross correlation between the complex order spectral components of this type of signal [ 15 ].…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…This is because many frequency bands may have been excited simultaneously. In transient vibratory signals, the bicoherence would only give limited diagnostic efficacy [ 15 ]. Even with a change in the excitation frequency range, the cyclic spectral coherence will not change significantly.…”

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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“…The proposed method could be extended for other rotating machinery and structures (e.g., gearboxes, 40 engines, 41 and civil engineering structures 42 ) and make an influence on vibration‐based health monitoring of rotating machinery and structures under nonstationary conditions.…”

Section: Discussionmentioning

confidence: 99%

Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique

Zhao

Gelman

Chu

et al. 2020

Struct Control Health Monit

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“…Gears under test were made of carburised and tempered S156 steel. The pinion operated under a load of 500 Nm at a rotation speed of 50 Hz [59].…”

Section: The Experimental Setup and Test Rig Descriptionmentioning

confidence: 99%

Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems

2021

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Higher order spectra exhibit a powerful detection capability of low-energy fault-related signal components, buried in background random noise. This paper investigates the powerful nonlinear non-stationary instantaneous wavelet bicoherence for local gear fault detection. The new methodology of selecting frequency bands that are relevant for wavelet bicoherence fault detection is proposed and investigated. The capabilities of wavelet bicoherence are proven for early-stage fault detection in a gear pinion, in which natural pitting has developed in multiple pinion teeth in the course of endurance gearbox tests. The results of the WB-based fault detection are compared with a stereo optical fault evaluation. The reliability of WB-based fault detection is quantified based on the complete probability of correct identification. This paper is the first attempt to investigate instantaneous wavelet bicoherence technology for the detection of multiple natural early-stage local gear faults, based on comprehensive statistical evaluation of the industrially relevant detection effectiveness estimate—the complete probability of correct fault detection.

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Novel Higher-Order Spectral Cross-Correlation Technologies for Vibration Sensor-Based Diagnosis of Gearboxes

Cited by 16 publications

References 61 publications

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

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

Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique

Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems

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