scholarly journals Adaptive Multiscale Noise Control Enhanced Stochastic Resonance Method Based on Modified EEMD with Its Application in Bearing Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Jimeng Li ◽  
Jinfeng Zhang
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Noise Control ◽  
Engineering Application ◽  
Resonance Method ◽  
Structural Defects ◽  
Mechanical Equipment ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

The structure of mechanical equipment becomes increasingly complex, and tough environments under which it works often make bearings and gears subject to failure. However, effective extraction of useful feature information submerged in strong noise that is indicative of structural defects has remained a major challenge. Therefore, an adaptive multiscale noise control enhanced stochastic resonance (SR) method based on modified ensemble empirical mode decomposition (EEMD) for mechanical fault diagnosis is proposed in the paper. According to the oscillation characteristics of signal itself, the algorithm of modified EEMD can adaptively decompose the fault signals into different scales and it reduces the decomposition levels to improve calculation efficiency of the proposed method. Through filter processing with the constructed filters, the orthogonality of adjacent intrinsic mode functions (IMFs) can be improved, which is conducive to enhancing the extraction of weak features from strong noise. The constructed signal obtained by using IMFs is inputted into the SR system, and the noise control parameter of different scales is optimized and selected with the help of the genetic algorithm, thus achieving the enhancement extraction of weak features. Finally, simulation experiments and engineering application of bearing fault diagnosis demonstrate the effectiveness and feasibility of the proposed method.

Novel synthetic index-based adaptive stochastic resonance method and its application in bearing fault diagnosis

2017 ◽  
Vol 391 ◽  
pp. 194-210 ◽  
Author(s):  
Peng Zhou ◽  
Siliang Lu ◽  
Fang Liu ◽  
Yongbin Liu ◽  
Guihua Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Resonance Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Synthetic Index

An Adaptive Periodical Stochastic Resonance Method Based on the Grey Wolf Optimizer Algorithm and Its Application in Rolling Bearing Fault Diagnosis

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Bingbing Hu ◽  
Chang Guo ◽  
Jimei Wu ◽  
Jiahui Tang ◽  
Jialing Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Resonance Method ◽  
Rolling Bearing ◽  
Grey Wolf Optimizer ◽  
Engineering Practice ◽  
Model Parameters ◽  
Grey Wolf ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

As a weak signal processing method that utilizes noise enhanced fault signals, stochastic resonance (SR) is widely used in mechanical fault diagnosis. However, the classic bistable SR has a problem with output saturation, which affects its ability to enhance fault characteristics. Moreover, it is difficult to implement SR when the fault frequency is not clear, which limits its application in engineering practice. To solve these problems, this paper proposed an adaptive periodical stochastic resonance (APSR) method based on the grey wolf optimizer (GWO) algorithm for rolling bearing fault diagnosis. The periodical stochastic resonance (PSR) model can independently adjust the system parameters and effectively avoid output saturation. The GWO algorithm is introduced to optimize the PSR model parameters to achieve adaptive detection of the input signal, and the output signal-to-noise ratio (SNR) is used as the objective function of the GWO algorithm. Simulated signals verify the validity of the proposed method. Furthermore, this method is applied to bearing fault diagnosis; experimental analysis demonstrates that the proposed method not only obtains a larger output SNR but also requires less time for the optimization process. The diagnosis results show that the proposed method can effectively enhance the weak fault signal and has strong practical values in engineering.

scholarly journals Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Bo Wu ◽  
Zhe Tong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Optimal Number ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

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