scholarly journals A New Bearing Fault Diagnosis Method Based on Refined Composite Multiscale Global Fuzzy Entropy and Self-Organizing Fuzzy Logic Classifier

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhang Ziying ◽  
Zhang Xi
Keyword(s):  
Fuzzy Logic ◽  
Fault Diagnosis ◽  
Online Training ◽  
Training Sample ◽  
Fuzzy Entropy ◽  
Testing Sample ◽  
Bearing Fault Diagnosis ◽  
Training Stage ◽  
Diagnosis Method ◽  
Sample Set

In this paper, a new feature extraction method called refined composite multiscale global fuzzy entropy (RCMGFE) is proposed. Based on the proposed RCMGFE and self-organizing fuzzy logic classifier (SOF), a new method for bearing fault diagnosis is proposed. Firstly, the fault features of the original bearing signal are extracted by using the proposed refined composite multiscale global fuzzy entropy, and the fault feature set of RCMGFE is constructed on this basis. Secondly, the extracted RCMGFE fault feature set is divided into an offline training sample set, an online training sample set, and a testing sample set. The offline training sample set and the online training sample set are, respectively, input into the offline training stage and the online training stage of the SOF for selecting representative samples and constructing fuzzy rules. Then, the testing sample set is input to the testing stage of the SOF for classification. Finally, the data of drive end bearing and fan end bearing provided by Case Western Reserve University are used to verify the validity of the proposed fault diagnosis method. The experimental results show that, compared with other methods, the proposed fault diagnosis method has a higher classification effect.

scholarly journals Study on a Fault Identification Method of the Hydraulic Pump Based on a Combination of Voiceprint Characteristics and Extreme Learning Machine

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 894 ◽  
Author(s):  
Wanlu Jiang ◽  
Zhenbao Li ◽  
Jingjing Li ◽  
Yong Zhu ◽  
Peiyao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Recognition Accuracy ◽  
Test Sample ◽  
Training Sample ◽  
Accuracy Rate ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Axial Piston ◽  
Sample Set

Aiming at addressing the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an axial piston pump fault diagnosis method that is based on the combination of Mel-frequency cepstrum coefficients (MFCC) and the extreme learning machine (ELM) is proposed. Firstly, a sound sensor is used to realize contactless sound signal acquisition of the axial piston pump. The wavelet packet default threshold denoises the original acquired sound signals. Afterwards, windowing and framing are added to the de-noised sound signals. The MFCC voiceprint characteristics of the processed sound signals are extracted. The voiceprint characteristics are divided into a training sample set and test sample set. ELM models with different numbers of neurons in the hidden layers are established for training and testing. The relationship between the number of neurons in the hidden layer and the recognition accuracy rate is obtained. The ELM model with the optimal number of hidden layer neurons is established and trained with the training sample set. The trained ELM model is applied to the test sample set for fault diagnosis. The fault diagnosis results are obtained. The fault diagnosis results of the ELM model are compared with those of the back propagation (BP) neural network and the support vector machine. The results show that the fault diagnosis method that is proposed in this paper has a higher recognition accuracy rate, shorter training and diagnosis times, and better application prospect.

scholarly journals A New Fuzzy Logic Classifier Based on Multiscale Permutation Entropy and Its Application in Bearing Fault Diagnosis

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 27 ◽  
Author(s):  
Wenhua Du ◽  
Xiaoming Guo ◽  
Zhijian Wang ◽  
Junyuan Wang ◽  
Mingrang Yu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Mean Difference ◽  
Harmonic Mean ◽  
Permutation Entropy ◽  
Testing Sample ◽  
Training Stage ◽  
Fault Features ◽  
Testing Stage ◽  
Diagnosis Method ◽  
Representative Samples

The self-organizing fuzzy (SOF) logic classifier is an efficient and non-parametric classifier. Its classification process is divided into an offline training stage, an online training stage, and a testing stage. Representative samples of different categories are obtained through the first two stages, and these representative samples are called prototypes. However, in the testing stage, the classification of testing samples is completely dependent on the prototype with the maximum similarity, without considering the influence of other prototypes on the classification decision of testing samples. Aiming at the testing stage, this paper proposed a new SOF classifier based on the harmonic mean difference (HMDSOF). In the testing stage of HMDSOF, firstly, each prototype was sorted in descending order according to the similarity between each prototype in the same category and the testing sample. Secondly, multiple local mean vectors of the prototypes after sorting were calculated. Finally, the testing sample was classified into the category with the smallest harmonic mean difference. Based on the above new method, in this paper, the multiscale permutation entropy (MPE) was used to extract fault features, linear discriminant analysis (LDA) was used to reduce the dimension of fault features, and the proposed HMDSOF was further used to classify the features. At the end of this paper, the proposed fault diagnosis method was applied to the diagnosis examples of two groups of different rolling bearings. The results verify the superiority and generalization of the proposed fault diagnosis method.

scholarly journals Rolling bearing fault diagnosis method based on VMD and LSSVM

2021 ◽  
Vol 1792 (1) ◽  
pp. 012035
Author(s):  
Xingtong Zhu ◽  
Zhiling Huang ◽  
Jinfeng Chen ◽  
Junhao Lu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

scholarly journals An improved bearing fault diagnosis scheme based on Hierarchical fuzzy entropy and Alexnet network

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Xiaoyu Shi ◽  
Gen Qiu ◽  
Chun Yin ◽  
Xuegang Huang ◽  
Kai Chen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Entropy ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis
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