scholarly journals Fault Diagnosis of Rolling Bearing Using Multiscale Amplitude-Aware Permutation Entropy and Random Forest

Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 184 ◽  
Author(s):  
Chen ◽  
Zhang ◽  
Zhao ◽  
Luo ◽  
Sun
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Rolling Bearing ◽  
Identification Accuracy ◽  
Coarse Grained ◽  
Permutation Entropy ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Bearing Fault Diagnosis ◽  
Fault Severity

A rolling bearing is an important connecting part between rotating machines. It is susceptible to mechanical stress and wear, which affect the running state of bearings. In order to effectively identify the fault types and analyze the fault severity of rolling bearings, a rolling bearing fault diagnosis method based on multiscale amplitude-aware permutation entropy (MAAPE) and random forest is proposed in this paper. The vibration signals of rolling bearings to be analyzed are decomposed into different coarse-grained time series by using the coarse-graining procedure in multiscale entropy, highlighting the fault dynamic characteristics of vibration signals at different scales. The fault features contained in the coarse-grained time series at different time scales are extracted by using amplitude-aware permutation entropy’s sensitive characteristics to signal amplitude and frequency changes to form fault feature vectors. The fault feature vector set is used to establish the random forest multi-classifier, and the fault type identification and fault severity analysis of rolling bearings is realized through random forest. In order to demonstrate the feasibility and effectiveness of the proposed method, experiments were fully conducted in this paper. The experimental results show that multiscale amplitude-aware permutation entropy can effectively extract fault features of rolling bearings from vibration signals, and the extracted feature vectors have high separability. Compared with other rolling bearing fault diagnosis methods, the proposed method not only has higher fault type identification accuracy, but also can analyze the fault severity of rolling bearings to some extent. The identification accuracy of four fault types is up to 96.0% and the fault recognition accuracy under different fault severity reached 92.8%.

Rolling bearing fault diagnosis by improved multiscale amplitude-aware permutation entropy and random forest

2020 ◽  
Vol 28 (3) ◽  
pp. 621-631
Author(s):  
吴海滨 WU Hai-bin ◽  
陈寅生 CHEN Yin-sheng ◽  
张庭豪 ZHANG Ting-hao ◽  
汪 颖 WANG Ying
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Intelligent fault diagnosis of rolling bearings using variational mode decomposition and self-organizing feature map

2020 ◽  
Vol 26 (21-22) ◽  
pp. 1886-1897 ◽  
Author(s):  
Jialing Zhang ◽  
Jimei Wu ◽  
Bingbing Hu ◽  
Jiahui Tang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
The Self ◽  
Permutation Entropy ◽  
Variational Mode Decomposition ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Feature Map ◽  
Mode Decomposition ◽  
Self Organizing

Rotating machinery contains numerous rolling bearings, which are critical for ensuring the normal working position and accurate operation of individual shaft systems. However, damage to rolling bearings can change their damping, stiffness, and elastic force. As a result, fault signals appear nonlinear and nonstationary. Vibration signals thus become difficult to diagnose clearly, especially in the incipient fault stage. To solve this problem, this article proposes an intelligent approach based on variational mode decomposition and the self-organizing feature map for rolling bearing fault diagnosis. First, the intrinsic mode function components of rolling bearing vibration signals are effectively separated by variational mode decomposition. Then, permutation entropy is used to extract feature vectors, which are used as training and testing data for the self-organizing feature map network. Finally, the various fault types of states are clustered on an intuitive visualization map. Clustering results of the experimental signal and the measured signal prove that the proposed method can successfully extract and cluster the rolling bearing faults in engineering applications. The proposed method improves the fault recognition rate to some extent over traditional methods.

scholarly journals A Rolling Bearing Fault Diagnosis Method Based on EMD and Quantile Permutation Entropy

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang-qiang Chen ◽  
Shao-wu Dai ◽  
Hong-de Dai
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Main Fault

The vibration signals resulting from rolling bearings are nonlinear and nonstationary, and an approach for the fault diagnosis of rolling bearings using the quantile permutation entropy and EMD (empirical mode decomposition) is proposed. Firstly, the EMD is used to decompose the rolling bearings vibration signal, and several IMFs (intrinsic mode functions) spanning different scales are obtained. Secondly, aiming at the shortcomings of the permutation entropy algorithm, a new permutation entropy algorithm based on sample quantile is proposed, and the quantile permutation entropy of the first few IMFs, which contain the main fault information, is calculated. The quantile permutation entropies are accordingly seen as the characteristic vector and then input to the particle swarm optimization and support vector machine. Finally, the proposed method is applied to the experimental data. The analysis results show that the proposed approach can effectively achieve fault diagnosis of rolling bearings.

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.

Feature extraction for rolling bearing fault diagnosis by electrostatic monitoring sensors

2014 ◽  
Vol 229 (10) ◽  
pp. 1887-1903 ◽  
Author(s):  
Ying Zhang ◽  
Hongfu Zuo ◽  
Fang Bai
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Hankel Matrix ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Quantitative Indicator ◽  
Bearing Fault Diagnosis

There are mainly two problems with the current feature extraction methods used in the electrostatic monitoring of rolling bearings, which affect their abilities to identify early faults: (1) since noises are mixed in the electrostatic signals, it is difficult to extract weak early fault features; (2) traditional time and frequency domain features have limited ability to provide a quantitative indicator of degradation state. With regard to these two problems, a new feature extraction method for rolling bearing fault diagnosis by electrostatic monitoring sensors is proposed in this paper. First, the spectrum interpolation is adopted to suppress the power-frequency interference in the electrostatic signal. Then the resultant signal is used to construct Hankel matrix, the number of useful components is automatically selected based on the difference spectrum of singular values, after that the signal is reconstructed to remove background noises and random pulses. Finally, the permutation entropy of the denoised signal is calculated and smoothed using the exponential weighted moving average method, which is used to be a quantitative indicator of bearing performance state. The simulation and experimental results show that the proposed method can effectively remove noises and significantly bring forward the time when early faults are detected.

scholarly journals Fault Diagnosis of Rolling Bearing Based on a Novel Adaptive High-Order Local Projection Denoising Method

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Rui Yuan ◽  
Yong Lv ◽  
Gangbing Song
Keyword(s):  
Fault Diagnosis ◽  
Numerical Simulations ◽  
Rolling Bearing ◽  
High Order ◽  
Rolling Bearings ◽  
Theoretical Derivation ◽  
Denoising Method ◽  
Vibration Signals ◽  
Local Projection ◽  
Ring Fault

Rolling bearings are vital components in rotary machinery, and their operating condition affects the entire mechanical systems. As one of the most important denoising methods for nonlinear systems, local projection (LP) denoising method can be used to reduce noise effectively. Afterwards, high-order polynomials are utilized to estimate the centroid of the neighborhood to better preserve complete geometry of attractors; thus, high-order local projection (HLP) can improve noise reduction performance. This paper proposed an adaptive high-order local projection (AHLP) denoising method in the field of fault diagnosis of rolling bearings to deal with different kinds of vibration signals of faulty rolling bearings. Optimal orders can be selected corresponding to vibration signals of outer ring fault (ORF) and inner ring fault (IRF) rolling bearings, because they have different nonlinear geometric structures. The vibration signal model of faulty rolling bearing is adopted in numerical simulations, and the characteristic frequencies of simulated signals can be well extracted by the proposed method. Furthermore, two kinds of experimental data have been processed in application researches, and fault frequencies of ORF and IRF rolling bearings can be both clearly extracted by the proposed method. The theoretical derivation, numerical simulations, and application research can indicate that the proposed novel approach is promising in the field of fault diagnosis of rolling bearing.

scholarly journals A Denoising Autoencoder-Based Bearing Fault Diagnosis System for Time-Domain Vibration Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yi Gu ◽  
Jiawei Cao ◽  
Xin Song ◽  
Jian Yao
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Bp Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Novel Approach ◽  
Fault Diagnosis System ◽  
Original Time

The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of the collected signals with environmental noise in the course of the work of rotating machines, this article proposes a novel approach for detecting the bearing fault, which is based on deep learning. To effectively detect, locate, and identify faults in rolling bearings, a stacked noise reduction autoencoder is utilized for abstracting characteristic from the original vibration of signals, and then, the characteristic is provided as input for backpropagation (BP) network classifier. The results output by this classifier represent different fault categories. Experimental results obtained on rolling bearing datasets show that this method can be used to effectively diagnose bearing faults based on original time-domain signals.

A blind deconvolution algorithm based on backward automatic differentiation and its application to rolling bearing fault diagnosis

2021 ◽  
Author(s):  
Bo Fang ◽  
Hu Jianzhong ◽  
Cheng Yang ◽  
Yudong Cao ◽  
Minping Jia
Keyword(s):  
Fault Diagnosis ◽  
Automatic Differentiation ◽  
Blind Deconvolution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Convex Optimization Problem ◽  
Bearing Fault Diagnosis ◽  
Filter Performance ◽  
Calculation Process

Abstract Blind deconvolution (BD) is an effective algorithm for enhancing the impulsive signature of rolling bearings. As a convex optimization problem, the existing BDs have poor optimization performance and cannot effectively enhance the impulsive signature excited by weak faults. Moreover, the existing BDs require manual derivation of the calculation process, which brings great inconvenience to the researcher's personalized design of the maximization criterion. A new BD algorithm based on backward automatic differentiation (BAD) is proposed, which is named BADBD. The calculation process does not require manual derivation so a general solution of BDs based on different maximization criteria is realized. BADBD constructs multiple cascaded filters to filter the raw vibration signal, which makes up for the deficiency of single filter performance. The filter coefficients are determined by Adam algorithm, which improves the optimization performance of the proposed BADBD. BADBD is compared with classic BDs by synthesized and real vibration signals. The results reveal superior capability of BADBD to enhance the impulsive signature and the fault diagnosis performance is significantly better than the classic BDs.

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