Fault Detection Enhancement in Rolling Element Bearings via Peak‐Based Multiscale Decomposition and Envelope Demodulation

Wang, Huaqing; Hou, Wei; Tang, Gang; Yuan, Hairong; Zhao, Qingliang; Cao, Xi

doi:10.1155/2014/329458

Cited by 23 publications

(15 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And the object of this paper is to overcome the lack of adaptability of peak-based wavelet decomposition methods. So next, we will make comparisons with the peak-based wavelet decomposition method [19].…”

Section: Analysis and Comparison With Other Methodsmentioning

confidence: 99%

“…Yet, these wavelet-based methods essentially improve the sparsity differences between noise and fault signal through linear variations, and there is still plenty of room for improvement. Wang et al [19] improves the signal sparsity by the nonlinear peakbased wavelet transform, and fault features can be enhanced obviously and detected easily. However, there are many parameters that need to be optimized in the wavelet transform, such as wavelet basis function and thresholding values.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing

Tang

He³

2020

Shock and Vibration

Self Cite

View full text Add to dashboard Cite

Rolling element bearings are widely used in rotating machinery to support shafts, whose failures may affect the health of the whole system. However, strong noise interferences often make the bearing fault features submerged and difficult to be identified. Peak-based wavelet method is such a way to reduce certain noise and enhance the fault features by increasing the sparsity of monitored signals. But peak-based wavelet parameters need to be optimized due to the determined basis function and constant resolution, which will affect the efficiency of vibration signal analysis. To address these problems, a peak-based mode decomposition is proposed for weak bearing fault feature enhancement and detection. Firstly, to enhance the differences between repetitive transients and high-frequency noise, a peak-based piecewise recombination is used to convert the middle frequency parts into low-frequency ones. Then, the recombined signal is processed by empirical mode decomposition, combining with a criterion of cross-correlation coefficients and kurtosis. Subsequently, a backward peak transformation is performed to obtain the enhanced signal. Finally, the fault diagnosis is implemented by the squared envelope spectrum, whose normalized squared magnitude is used as a bearing fault indicator. The analysis results of the simulated signals and the experimental signals show that the proposed method can enhance and identify the weak repetitive transient features. The superiority of the proposed method for faint repetitive transient detection is also verified by comparing with the peak-based wavelet method.

show abstract

Section: Analysis and Comparison With Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing

Tang

He³

2020

Shock and Vibration

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are two common phenomena, a change in amplitude at the typical frequency [34] and a new frequency appearance [35], when rotating machinery breaks down. In this paper, the vibration signal of the hydropower unit bearing is simulated.…”

Section: Simulation Experimentmentioning

confidence: 99%

Coupling Fault Feature Extraction Method Based on Bivariate Empirical Mode Decomposition and Full Spectrum for Rotating Machinery

Jia

Wang

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

To accurately extract the fault characteristics of vibration signals of rotating machinery is of great significance to the unit online monitoring and evaluation. However, because the current feature extraction methods are mainly for single channel, the results of feature extraction are often inaccurate. To this end, a coupling fault feature extraction method based on bivariate empirical mode decomposition (BEMD) and full spectrum is proposed for rotating machinery. Firstly, the two-dimensional orthogonal signal obtained by orthogonal sampling technique is decomposed by bivariate empirical mode decomposition to obtain the intrinsic mode function with phase information. In order to obtain the sensitive modal components, the sensitivity coefficients are constructed on the basis of mutual information. Then, the sensitivity coefficient of each intrinsic mode function is calculated, and the intrinsic mode function with the larger sensitive coefficient is selected as the sensitive component. Finally, the full spectrum of the sensitive component is obtained using the full vector envelope technique, so as to get a comprehensive and accurate characteristic component. The results of simulations experiment and an application example show that this method can extract the fault characteristic component of the rotating machinery comprehensively and accurately. It is of great significance to realize the accurate diagnosis of coupling faults of rotating machinery.

show abstract

“…In [18], efficient means for reducing noise and early fault detection was presented using piecewise recombination and inverse wavelet transform. With this, the detection of the system was proved to efficient and was also easy to implement.…”

Section: Related Workmentioning

confidence: 99%

Vibration Analysis of Industrial Drive for Broken Bearing Detection Using Probabilistic Wavelet Neural Network

Jayakumar

Thangavel²

2015

IJPEDS

View full text Add to dashboard Cite

A reliable monitoring of industrial drives plays a vital role to prevent from the performance degradation of machinery. Today's fault detection system mechanism uses wavelet transform for proper detection of faults, however it required more attention on detecting higher fault rates with lower execution time. Existence of faults on industrial drives leads to higher current flow rate and the broken bearing detected system determined the number of unhealthy bearings but need to develop a faster system with constant frequency domain. Vibration data acquisition was used in our proposed work to detect broken bearing faults in induction machine. To generate an effective fault detection of industrial drives, Biorthogonal Posterior Vibration Signal-Data Probabilistic Wavelet Neural Network (BPPVS-WNN) system was proposed in this paper. This system was focused to reducing the current flow and to identify faults with lesser execution time with harmonic values obtained through fifth derivative. Initially, the construction of Biorthogonal vibration signal-data based wavelet transform in BPPVS-WNN system localizes the time and frequency domain. The Biorthogonal wavelet approximates the broken bearing using double scaling and factor, identifies the transient disturbance due to fault on induction motor through approximate coefficients and detailed coefficient. Posterior Probabilistic Neural Network detects the final level of faults using the detailed coefficient till fifth derivative and the results obtained through it at a faster rate at constant frequency signal on the industrial drive. Experiment through the Simulink tool detects the healthy and unhealthy motor on measuring parametric factors such as fault detection rate based on time, current flow rate and execution time.

show abstract

Fault Detection Enhancement in Rolling Element Bearings via Peak‐Based Multiscale Decomposition and Envelope Demodulation

Cited by 23 publications

References 24 publications

Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing

Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing

Coupling Fault Feature Extraction Method Based on Bivariate Empirical Mode Decomposition and Full Spectrum for Rotating Machinery

Vibration Analysis of Industrial Drive for Broken Bearing Detection Using Probabilistic Wavelet Neural Network

Contact Info

Product

Resources

About