Weak Fault Signal Detection of Rotating Machinery Based on Multistable Stochastic Resonance and VMD‐AMD

Han, Dongying; Su, Xiao; Shi, Pengfei

doi:10.1155/2018/4252438

Cited by 11 publications

(9 citation statements)

References 41 publications

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“…It can be seen that although the Langevin system is helpful to enhance the SNR out of the detected signal, the optimal SNR out of the output signals from Langevin system is generally smaller than those from Duffing system. Again, the Duffing system is demonstrated as a better bistable system in 6 Hz can be identified from the spectrum of output signal shown in Figure 12(d), where SNR out is enhanced to − 14.64 dB. is result indicates the existence of an outer ring fault in the drive end rolling bearing.…”

Section: Case 1: Inner Ring Fault Diagnosismentioning

confidence: 85%

“…Although the aforementioned diagnosis methods can achieve weak-signal detection to some extent through suppressing or canceling the noise embedded in fault vibration signals and highlighting the fault features, their weak-signal detection performances are limited because they inevitably damage the weak fault features submerged in heavy noise background in the denoising process. Compared with these noise cancellation-based fault diagnosis methods, a nonlinear phenomenon called stochastic resonance (SR) leads a type of noise utilization-based fault diagnosis methods, which have intrinsic superiority in weak-signal detection by taking advantage of the noise to enhance the weak fault features through some nonlinear systems [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance

Lai

Wang

Zhang

et al. 2020

Shock and Vibration

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The weak-signal detection technologies based on stochastic resonance (SR) play important roles in the vibration-based health monitoring and fault diagnosis of rolling bearings, especially at their early-fault stage. Aiming at the parameter-fixed vibration signals in practical engineering, it is feasible to diagnose the potential rolling bearing faults through adaptively adjusting the SR system parameters, as well as other generalized parameters such as the amplitude-transformation coefficient and scale-transformation coefficient. However, extant adaptive adjustment methods focus on the system parameters, while the adjustments of other adjustable parameters have not been fully studied, thus limiting the detection performance of the adaptive SR method. In order to further enhance the detection performance of adaptive SR methods and extend their application in rolling bearing fault diagnosis, an adaptive multiparameter-adjusting SR (AMPASR) method for bistable systems based on particle swarm optimization (PSO) algorithm is proposed in this paper. This method can produce optimal SR output through adaptively adjusting multiparameters, thus realizing fault feature extraction and further fault diagnosis. Furthermore, the influence of algorithm parameters on the optimization results is discussed, and the optimization results of the Langevin system and the Duffing system are compared. Finally, we propose a weak-signal detection method based on the AMPASR of the Duffing system and employ three diagnosis examples involving inner ring fault, outer ring fault, and rolling element fault diagnoses to demonstrate its feasibility in rolling bearing fault diagnosis.

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Section: Case 1: Inner Ring Fault Diagnosismentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance

Lai

Wang

Zhang

et al. 2020

Shock and Vibration

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show abstract

“…Then, the weak features were extracted by using the VMD with the optimized parameters. In [88], rescaling subsampling compression is used to preprocess the signal, and analytical mode decomposition was used to enhance and select signal components. Then the processed signal was decomposed into IMF by VMD to analyze weak multi-frequency signals.…”

Section: Authors Methodologiesmentioning

confidence: 99%

“…Ma et al [84] Adaptive scale space spectrum segmentation + VMD + Teager energy operator Li et al [85] Improved autoregressive-Minimum entropy deconvolution + VMD Yang et al [86] Optimized VMD + simulated annealing Guo et al [87] VMD + parameter optimization Han et al [88] Rescaling subsampling compression + analytical mode decomposition + VMD Jiang et al [89] EMD + VMD…”

Section: Authors Methodologiesmentioning

confidence: 99%

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei

et al. 2019

Entropy

174

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Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.

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“…VMD method optimized by Support Vector Machine (SVM) has been used for mechanical fault analysis [20]- [22]. Furthermore, VMD algorithm is also applied in the field of engine fault diagnosis [23], [24].…”

Section: Introductionmentioning

confidence: 99%

A Variety of Engine Faults Detection Based on Optimized Variational Mode Decomposition-Robust Independent Component Analysis and Fuzzy C-Mean Clustering

Cao

Zhang

2019

IEEE Access

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As one kind of current main power sources, internal combustion engines require high-reliability to ensure mechanical systems working well in normal operation. This paper studies vibration signals in order to detect multiple types of faults by a single channel signal. First, for the decomposition level of variational mode decomposition (VMD) which needs to be chosen non-automatically, this paper analyzes the features of various faults and optimizes the iteration initial values of center frequency so as to reduce the adverse effect of inappropriate decomposition level. Moreover, considering that VMD cannot decompose different signal sources in the same frequency, robust independent component analysis (ICA) is an excellent method to overcome this challenge. Then, the fourth-order cumulant of restructured signals from VMD and robust ICA is taken as fault indexes. Finally, because of the high error rate of original fuzzy C-Mean clustering, Euclidean distance between test points and cluster center of fuel injection failure is taken as a fault detection threshold in order to achieve high-recognition rate. In conclusion, through optimizing several algorithms, the purpose of detecting multiple types of faults by single channel signal is achieved in the current research.

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Weak Fault Signal Detection of Rotating Machinery Based on Multistable Stochastic Resonance and VMD‐AMD

Cited by 11 publications

References 41 publications

Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance

Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

A Variety of Engine Faults Detection Based on Optimized Variational Mode Decomposition-Robust Independent Component Analysis and Fuzzy C-Mean Clustering

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