Bearing Operating State Evaluation Based on Improved HMM

With the development of industry, the fault diagnosis requirements for rolling bearings are getting higher and higher. This paper aims to develop low-complexity solutions for bearing fault diagnosis. In this paper, we use wavelet decomposition to obtain gesture Monitoring Index Vector (MIVs), after this, an improved Hidden Markov Model (HMM) algorithm was proposed for bearing fault diagnosis, in which we apply the Genetic Algorithm (GA) to avoid the convergence to local optimum, thus improving the recognition performance. The experimental results on 11 groups of test datasets demonstrate that the proposed algorithm (GAHMM) can achieve a higher average recognition rate of 93%, 87%, 87%, 93%, 93%, 97%, 100%, 97%, 97%, 100%, 97%.

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Rolling Bearing Fault Diagnosis Research

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.155-156.87 ◽

2012 ◽

Vol 155-156 ◽

pp. 87-91

Author(s):

Zhong Hu Yuan ◽

Yang Su ◽

Xiao Xuan Qi

Keyword(s):

Fault Diagnosis ◽

Time Domain ◽

Recognition Rate ◽

Principal Component ◽

Rolling Bearing ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Time Domain Signal ◽

Feature Information ◽

The Time Domain

According to the characteristics of the rolling bearing fault, we make the research on fault diagnosis. Time domain signal can not perform the fault feature information well. The power spectrum changes the time domain signals into the frequency signals. It sets up the new data model. It uses the principal component analysis on fault diagnosis. It uses T square statistics and Q statistics methods to make fault diagnosis. Simulation experiment results demonstrate that this method provides a high recognition rate.

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Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine

Complexity ◽

10.1155/2019/4031795 ◽

2019 ◽

Vol 2019 ◽

pp. 1-19 ◽

Cited By ~ 19

Author(s):

Zhijian Wang ◽

Likang Zheng ◽

Junyuan Wang ◽

Wenhua Du

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Local Optimum ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Krill Herd Algorithm ◽

Krill Herd ◽

Diagnosis Method ◽

Kernel Extreme Learning Machine ◽

Learning Machine

In this paper, a novel bearing intelligent fault diagnosis method based on a novel krill herd algorithm (NKH) and kernel extreme learning machine (KELM) is proposed. Firstly, multiscale dispersion entropy (MDE) is used to extract fault features of bearings to obtain a set of fault feature vectors composed of dispersion entropy. Then, it is imported into the kernel extreme learning machine for fault diagnosis. But considering the kernel function parameters σ and the error penalty factor C will affect the classification accuracy of the kernel extreme learning machine, this paper uses the novel krill herd algorithm (NKH) for their optimization. The opposite populations are added to the NKH in the initialization of population to improve its speed and prevent local optimum, and during the period of looking for the optimal solution, the impulse operator is introduced to ensure it has enough impulse to rush out of the local optimal once into the local optimum. Finally, in order to verify the effectiveness of the proposed method, it was applied to the bearing fault experiment of Case Western Reserve University and XJTU-SY bearing data set. The results show that the proposed method not only has good fault diagnosis performance and generalization but also has fast convergence speed and does not easily fall into the local optimum. Therefore, this paper provides a method for fault diagnosis under different loads. Meanwhile, the new method (NKH-KELM) is compared and analyzed with other mainstream intelligent bearing fault diagnosis methods to verify the effectiveness and accuracy of the proposed method.

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Bearing Fault Diagnosis Based on Probability Boxes Theory and SVM Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.79.93 ◽

2011 ◽

Vol 79 ◽

pp. 93-98

Author(s):

Yi Du ◽

Yi Lin Chi ◽

Wu Xing

Keyword(s):

Fault Diagnosis ◽

Recognition Rate ◽

Extraction Process ◽

Information Loss ◽

Support Vector ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Sample Frequency ◽

Images Recognition ◽

Test Result

To solve the information loss on the feature extraction process in the traditional fault diagnosis, this paper proposes a new method which based on probability boxes and Dempster Shafer Structure (DSS). The DSS was extracted from the raw data and then transformed into a probability box. The bearing fault diagnosis was done by the probability boxes images recognition. To solve the excessive computing cost caused by large sample frequency and the overlaps among p-boxes, the Support Vector Machine (SVM) was involved. The SVM features database was established by some cumulative uncertainty measures methods of p-boxes. The test result shows that this method is fast, not sensitive to noise and has high recognition rate with high accuracy.

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Research on Bearing Fault Diagnosis Method Based on Filter Features of MOMLMEDA and LSTM

Entropy ◽

10.3390/e21101025 ◽

2019 ◽

Vol 21 (10) ◽

pp. 1025

Author(s):

Yong Li ◽

Gang Cheng ◽

Xihui Chen ◽

Yusong Pang

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Short Term Memory ◽

Recognition Rate ◽

Selection Criterion ◽

Minimum Entropy ◽

Efficient Operation ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

The Status

As the supporting unit of rotating machinery, bearing can ensure efficient operation of the equipment. Therefore, it is very important to monitor the status of bearings accurately. A bearing fault diagnosis mothed based on Multipoint Optimal Minimum Local Mean Entropy Deconvolution Adjusted (MOMLMEDA) and Long Short-Term Memory (LSTM) is proposed. MOMLMEDA is an improved algorithm based on Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA). By setting the local kurtosis mean as a new selection criterion, it can effectively avoid the interference of false kurtosis caused by noise and improve the accuracy of optimal kurtosis position. The optimal filter designed by optimal kurtosis position has periodic and amplitude characteristics, which are used as the fault feature in this paper. However, this feature has temporal characteristics and cannot be used as input of general neural network directly. LSTM is selected as the classification network in this paper. It can effectively avoid the influence of the temporal problem existing in feature vectors. Accurate diagnosis of bearing faults is realized by training classification neural network with samples. The overall recognition rate is up to 93.50%.

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