Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy

Zhang, Weibo; Zhou, Jing

doi:10.3390/e21050519

Cited by 19 publications

(20 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spectral lines, such as the gear-mesh frequency, are known to be sensitive to damage and are then suitable features. More sophisticated signal processing techniques are currently available (e.g., see [5][6][7][8][9][10]) to highlight the signal of interest with respect to the noise (i.e., to increase the signal-to-noise ratio-to de-noise), to compensate for the transmission path from the source to the sensor and to isolate the different sources to enhance their contribution (e.g., Blind Source Separation). These algorithms can be very effective, but in general, are not ready for working independently from human supervision.…”

Section: Featuresmentioning

confidence: 99%

Machine Vibration Monitoring for Diagnostics through Hypothesis Testing

Daga

Garibaldi

2019

Information

View full text Add to dashboard Cite

Nowadays, the subject of machine diagnostics is gathering growing interest in the research field as switching from a programmed to a preventive maintenance regime based on the real health conditions (i.e., condition-based maintenance) can lead to great advantages both in terms of safety and costs. Nondestructive tests monitoring the state of health are fundamental for this purpose. An effective form of condition monitoring is that based on vibration (vibration monitoring), which exploits inexpensive accelerometers to perform machine diagnostics. In this work, statistics and hypothesis testing will be used to build a solid foundation for damage detection by recognition of patterns in a multivariate dataset which collects simple time features extracted from accelerometric measurements. In this regard, data from high-speed aeronautical bearings were analyzed. These were acquired on a test rig built by the Dynamic and Identification Research Group (DIRG) of the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. The proposed strategy was to reduce the multivariate dataset to a single index which the health conditions can be determined. This dimensionality reduction was initially performed using Principal Component Analysis, which proved to be a lossy compression. Improvement was obtained via Fisher's Linear Discriminant Analysis, which finds the direction with maximum distance between the damaged and healthy indices. This method is still ineffective in highlighting phenomena that develop in directions orthogonal to the discriminant. Finally, a lossless compression was achieved using the Mahalanobis distance-based Novelty Indices, which was also able to compensate for possible latent confounding factors. Further, considerations about the confidence, the sensitivity, the curse of dimensionality, and the minimum number of samples were also tackled for ensuring statistical significance. The results obtained here were very good not only in terms of reduced amounts of missed and false alarms, but also considering the speed of the algorithms, their simplicity, and the full independence from human interaction, which make them suitable for real time implementation and integration in condition-based maintenance (CBM) regimes.

show abstract

Section: Featuresmentioning

confidence: 99%

Machine Vibration Monitoring for Diagnostics through Hypothesis Testing

Daga

Garibaldi

2019

Information

View full text Add to dashboard Cite

show abstract

“…The size of them reflects the proportion of the mode to the total mode. Then, in light of the thought on information entropy, the singular value is a kind of time domain division of AE signal [10]. The singular spectrum entropy (SSE) of AE signal is…”

Section: Time Domain Information Entropy Featuresmentioning

confidence: 99%

“…The sampling frequency is set at 1000 KHz, and is therefore gained by 52 groups of AE signals. SSE, PSE, WESE, and WSFSE of all the rotational speeds and measurement points of AE signals are used to fuse, and the fused information entropy points are calculated according to Equation (10).…”

Section: Rolling Bearing Faults Simulation Experimentsmentioning

confidence: 99%

“…The multi-domain entropy points (MDEP) of AE signals are structured in terms of Equation (10). Each of the curves in Figure 10 is the MDEP of a fault at multiple rotational speeds.…”

Section: Extraction Of Multi-domain Entropy Features Of Ae Signalsmentioning

confidence: 99%

“…As some faults have similar signal characteristics, information entropy can only measure their complexity and perform preliminary noise reduction and classification but cannot accurately classify. Recently, information entropy has been widely applied as the feature extraction method of fault diagnosis [8][9][10][11][12][13][14]. The purpose of fault diagnosis is to predict the discrete values at different fault states of a diagnostic object.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Domain Entropy-Random Forest Method for the Fusion Diagnosis of Inter-Shaft Bearing Faults with Acoustic Emission Signals

Jun

Liu

Zhang

et al. 2019

Entropy

View full text Add to dashboard Cite

Inter-shaft bearing as a key component of turbomachinery is a major source of catastrophic accidents. Due to the requirement of high sampling frequency and high sensitivity to impact signals, AE (Acoustic Emission) signals are widely applied to monitor and diagnose inter-shaft bearing faults. With respect to the nonstationary and nonlinear of inter-shaft bearing AE signals, this paper presents a novel fault diagnosis method of inter-shaft bearing called the multi-domain entropy-random forest (MDERF) method by fusing multi-domain entropy and random forest. Firstly, the simulation test of inter-shaft bearing faults is conducted to simulate the typical fault modes of inter-shaft bearing and collect the data of AE signals. Secondly, multi-domain entropy is proposed as a feature extraction approach to extract the four entropies of AE signal. Finally, the samples in the built set are divided into two subsets to train and establish the random forest model of bearing fault diagnosis, respectively. The effectiveness and generalization ability of the developed model are verified based on the other experimental data. The proposed fault diagnosis method is validated to hold good generalization ability and high diagnostic accuracy (~0.9375) without over-fitting phenomenon in the fault diagnosis of bearing shaft.

show abstract

A novel fault diagnosis method of rotating machinery via VMD, CWT and improved CNN

Peng

et al. 2022

Measurement

View full text Add to dashboard Cite

Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy

Cited by 19 publications

References 43 publications

Machine Vibration Monitoring for Diagnostics through Hypothesis Testing

Machine Vibration Monitoring for Diagnostics through Hypothesis Testing

Multi-Domain Entropy-Random Forest Method for the Fusion Diagnosis of Inter-Shaft Bearing Faults with Acoustic Emission Signals

A novel fault diagnosis method of rotating machinery via VMD, CWT and improved CNN

Contact Info

Product

Resources

About