An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery

2019 ◽  
Vol 26 (3-4) ◽  
pp. 146-160
Author(s):  
Xianzhi Wang ◽  
Shubin Si ◽  
Yongbo Li ◽  
Xiaoqiang Du
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Random Forest ◽  
Extraction Method ◽  
Rotating Machinery ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Multichannel Signals ◽  
Fault Feature Extraction ◽  
Fault Information

Fault feature extraction of rotating machinery is crucial and challenging due to its nonlinear and nonstationary characteristics. In order to resolve this difficulty, a quality nonlinear fault feature extraction method is required. Hierarchical permutation entropy has been proven to be a promising nonlinear feature extraction method for fault diagnosis of rotating machinery. Compared with multiscale permutation entropy, hierarchical permutation entropy considers the fault information hidden in both high frequency and low frequency components. However, hierarchical permutation entropy still has some shortcomings, such as poor statistical stability for short time series and inability of analyzing multichannel signals. To address such disadvantages, this paper proposes a new entropy method, called refined composite multivariate hierarchical permutation entropy. Refined composite multivariate hierarchical permutation entropy can extract rich fault information hidden in multichannel signals synchronously. Based on refined composite multivariate hierarchical permutation entropy and random forest, a novel fault diagnosis framework is proposed in this paper. The effectiveness of the proposed method is validated using experimental and simulated signals. The results demonstrate that the proposed method outperforms multivariate multiscale fuzzy entropy, refined composite multivariate multiscale fuzzy entropy, multivariate multiscale sample entropy, multivariate multiscale permutation entropy, multivariate hierarchical permutation entropy, and composite multivariate hierarchical permutation entropy in recognizing the different faults of rotating machinery.

scholarly journals Hierarchical Diversity Entropy for the Early Fault Diagnosis of Rolling Bearing

2021 ◽  
Author(s):  
Xianzhi Wang ◽  
Shubin Si ◽  
Yongbo Li
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Health Management ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Intelligent Fault Diagnosis ◽  
Feature Extraction Method ◽  
Fault Information

Abstract Intelligent fault diagnosis provides great convenience for the prognostic and health management of the rotating machinery. Recently, the entropy-based feature extraction method has aroused researchers’ attentions due to its independence with prior knowledge, unnecessary of preprocessing, and easy to perform. The multiscale diversity entropy has been proven to be a promising feature extraction method for the intelligent fault diagnosis. Compared to the existing entropy methods, the multiscale diversity entropy has advantages of high consistency, strong robustness and high calculation efficiency. However, the multiscale diversity entropy encounters the challenge to extract features for early fault diagnosis due to the weak fault symptoms and strong noise. This can be attributed to the multiscale diversity entropy only concerns the fault information embedded in the low frequency, which ignores the information hidden in the high frequency. To address this defect, the hierarchical diversity entropy (HDE) is proposed, which can synchronously extract fault information hidden in both high and low frequency. Based on HDE and random forest, a novel intelligent fault diagnosis frame has been proposed. The effectiveness of the proposed method has been evaluated through simulated and experimental bearing signals. The results show that the proposed HDE has the best feature extraction ability compare to multiscale sample entropy, multiscale permutation entropy, multiscale fuzzy entropy, and multiscale diversity entropy.

An image-based feature extraction method for fault diagnosis of variable-speed rotating machinery

2022 ◽  
Vol 167 ◽  
pp. 108524
Author(s):  
Jungho Park ◽  
Yunhan Kim ◽  
Kyumin Na ◽  
Byeng D. Youn ◽  
Yuejian Chen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Rotating Machinery ◽  
Variable Speed ◽  
Feature Extraction Method

scholarly journals KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiao Hu ◽  
Zhihuai Xiao ◽  
Dong Liu ◽  
Yongjun Tang ◽  
O. P. Malik ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Spectrum ◽  
Extraction Method ◽  
Principal Component ◽  
Rotating Machinery ◽  
Kernel Principal Component Analysis ◽  
Global Feature ◽  
Vibration Signals ◽  
Feature Extraction Method

Feature extraction plays a key role in fault diagnosis of rotating machinery. Many methods reported in the literature are based on masses of labeled data and need much prior knowledge to select the most discriminating features or establish a complex deep-learning model. To solve the dilemma, a novel feature extraction method based on kernel principal component analysis (KPCA) and an autoencoder (AE), namely, SFS-KPCA-AE, is presented in this paper to automatically extract the most discriminative features from the frequency spectrum of vibration signals. First, fast Fourier transform is calculated on the entire vibration signal to get the frequency spectrum. Next, the spectrum is divided into several segments. Then, local-global feature extraction is performed by applying KPCA to these segments. Finally, an AE is employed to obtain the low-dimensional representations of the high-dimensional global feature. The proposed feature extraction method combined with a classifier achieves fault diagnosis for rotating machinery. A rotor dataset and a bearing dataset are utilized to validate the performance of the proposed method. Experimental results demonstrate that the proposed method achieved satisfactory performance in feature extraction when training samples or motor load changed. By comparing with other methods, the superiority of the proposed SFS-KPCA-AE is verified.

Fault Feature Extraction Method of Large Rotating Machinery

2012 ◽  
Vol 226-228 ◽  
pp. 756-759
Author(s):  
Zhang Lei Jiang ◽  
Xiao Li Xu ◽  
Peng Chen
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Working Condition ◽  
Wavelet Packet ◽  
Fault Prediction ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Rotating Machine ◽  
Fault Feature Extraction ◽  
Fault Information

Working condition of large rotating machine is varying. The collected vibration signals contain fault information as well as non-fault information, like load change and noise. The traditional fault feature extraction method which based on energy changing has certain limitations; therefore, new fault feature extraction method based on Fast ICA will be researched. Separating independent signals from blind signals by adopting Independent Component Analysis(ICA), purifying fault information and suppressing interference information; and the lifting wavelet packet is used for acquiring time frequency domain feature band from signals, so as to solve the difficulty that the fault information of the variable working condition rotating machine is always submerged by irregular working condition changing information such that effective fault prediction is hard to carry out.

scholarly journals Rotating Machinery Fault Diagnosis Based on Improved Multiscale Amplitude-Aware Permutation Entropy and Multiclass Relevance Vector Machine

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4542 ◽  
Author(s):  
Chen ◽  
Zhang ◽  
Zhao ◽  
Luo ◽  
Lin
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Relevance Vector Machine ◽  
Rotating Machinery ◽  
Coarse Grained ◽  
Permutation Entropy ◽  
Fault Severity ◽  
Diagnosis Method ◽  
Fault Feature Extraction

The health state of rotating machinery directly affects the overall performance of the mechanical system. The monitoring of the operation condition is very important to reduce the downtime and improve the production efficiency. This paper presents a novel rotating machinery fault diagnosis method based on the improved multiscale amplitude-aware permutation entropy (IMAAPE) and the multiclass relevance vector machine (mRVM) to provide the necessary information for maintenance decisions. Once the fault occurs, the vibration amplitude and frequency of rotating machinery obviously changes and therefore, the vibration signal contains a considerable amount of fault information. In order to effectively extract the fault features from the vibration signals, the intrinsic time-scale decomposition (ITD) was used to highlight the fault characteristics of the vibration signal by extracting the optimum proper rotation (PR) component. Subsequently, the IMAAPE was utilized to realize the fault feature extraction from the PR component. In the IMAAPE algorithm, the coarse-graining procedures in the multi-scale analysis were improved and the stability of fault feature extraction was promoted. The coarse-grained time series of vibration signals at different time scales were firstly obtained, and the sensitivity of the amplitude-aware permutation entropy (AAPE) to signal amplitude and frequency was adopted to realize the fault feature extraction of coarse-grained time series. The multi-classifier based on the mRVM was established by the fault feature set to identify the fault type and analyze the fault severity of rotating machinery. In order to demonstrate the effectiveness and feasibility of the proposed method, the experimental datasets of the rolling bearing and gearbox were used to verify the proposed fault diagnosis method respectively. The experimental results show that the proposed method can be applied to the fault type identification and the fault severity analysis of rotating machinery with high accuracy.

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