An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing

Aiming at the problem that the weak faults of rolling bearing are difficult to recognize accurately, an approach on the basis of swarm decomposition (SWD), morphology envelope dispersion entropy (MEDE), and random forest (RF) is proposed to realize effective detection and intelligent recognition of weak faults in rolling bearings. The proposed approach is based on the idea of signal denoising, feature extraction and pattern classification. Firstly, the raw signal is divided into a group of oscillatory components through SWD algorithm. The first component has the richest fault information and perceived as the principal oscillatory component (POC). Secondly, the MEDE value of the POC is calculated and used to describe the characteristics of signal. Ultimately, the obtained MEDE values of various states are trained and recognized by being input as the feature vectors into the RF classifier to achieve the automatic identification of rolling bearing fault under different operation states. The dataset of Case Western Reserve University is conducted, the proposed approach achieves recognition accuracy rate of 100%. In summary, the proposed approach is efficient and robust, which can be used as a supplement to the rolling bearing fault diagnosis methods.

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

Wireless Communications and Mobile Computing ◽

10.1155/2021/9790053 ◽

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.

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High Resonance Component of Resonance-Based Sparse Decomposition Application in Extraction of Rolling Bearing Fault Information

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.753-755.2290 ◽

2013 ◽

Vol 753-755 ◽

pp. 2290-2296 ◽

Cited By ~ 2

Author(s):

Wen Tao Huang ◽

Yin Feng Liu ◽

Pei Lu Niu ◽

Wei Jie Wang

Keyword(s):

Fault Diagnosis ◽

Vibration Signal ◽

Rolling Bearing ◽

Frequency Resolution ◽

Sparse Decomposition ◽

High Resonance ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Information Frequency ◽

Fault Information

In the early fault diagnosis of rolling bearing, the vibration signal is mixed with a lot of noise, resulting in the difficulties in analysis of early weak fault signal. This article introduces resonance-based signal sparse decomposition (RSSD) into rolling bearing fault diagnosis, and studies the fault information contained in high resonance component and low resonance component. This article compares the effect of the two resonance components to extract rolling bearing fault information in four aspects: the amount of fault information, frequency resolution of subbands, sensitivity to noise and immunity to autocorrelation processing. We find that the high resonance component has greater advantage in extraction of rolling bearing fault information, and it is able to indicate rolling bearing failure accurately.

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Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Symmetry ◽

10.3390/sym14010013 ◽

2021 ◽

Vol 14 (1) ◽

pp. 13

Author(s):

Jianpeng Ma ◽

Chengwei Li ◽

Guangzhu Zhang

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Information Fusion ◽

Rolling Bearing ◽

Fusion Method ◽

Rolling Bearings ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Evolution Analysis ◽

Comparison Results

The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.

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

Algorithms ◽

10.3390/a12090184 ◽

2019 ◽

Vol 12 (9) ◽

pp. 184 ◽

Cited By ~ 5

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%.

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Rolling Bearing Fault Diagnostic Method Based on VMD-AR Model and Random Forest Classifier

Shock and Vibration ◽

10.1155/2016/5132046 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 15

Author(s):

Te Han ◽

Dongxiang Jiang

Keyword(s):

Random Forest ◽

Vibration Signal ◽

Rolling Bearing ◽

Ar Model ◽

Model Parameters ◽

Feature Extraction Method ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Mode Decomposition ◽

Non Gaussian

Targeting the nonstationary and non-Gaussian characteristics of vibration signal from fault rolling bearing, this paper proposes a fault feature extraction method based on variational mode decomposition (VMD) and autoregressive (AR) model parameters. Firstly, VMD is applied to decompose vibration signals and a series of stationary component signals can be obtained. Secondly, AR model is established for each component mode. Thirdly, the parameters and remnant of AR model served as fault characteristic vectors. Finally, a novel random forest (RF) classifier is put forward for pattern recognition in the field of rolling bearing fault diagnosis. The validity and superiority of proposed method are verified by an experimental dataset. Analysis results show that this method based on VMD-AR model can extract fault features accurately and RF classifier has been proved to outperform comparative classifiers.

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

Optics and Precision Engineering ◽

10.3788/ope.20202803.0621 ◽

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

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Application of modified culture Kalman filter in bearing fault diagnosis

Open Physics ◽

10.1515/phys-2018-0095 ◽

2018 ◽

Vol 16 (1) ◽

pp. 757-765

Author(s):

Wang Hailun ◽

Alexander Martinez

Keyword(s):

Kalman Filter ◽

Fault Diagnosis ◽

Nonlinear Model ◽

Vibration Signal ◽

Rolling Bearing ◽

Sensor Data ◽

Estimation Accuracy ◽

Rolling Bearings ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Abstract Rolling bearings are an important part of rotary machines. They are used most widely in various mechanical sectors, which are among the most vulnerable components in machines. This paper uses CKF algorithm to compile a signal analysis system, analyses the vibration signal of the rolling bearing, extracts fault features, and realizes fault diagnosis. In order to improve the estimation accuracy of bearing fault diagnosis under nonlinear model, a nonlinear model of bearing fault diagnosis based on quaternion and low-accuracy high-noise sensors is established, and the attitude estimation has performed using the culture Kalman filter (CKF) algorithm. The sensor data comparison shows that the use of the volumetric Kalman filter algorithm can effectively improve the estimation accuracy of bearing fault diagnosis and stability. In this paper, the measured vibration signals of several groups of rolling bearings are analysed, and the signal characteristic frequency has extracted. The results show that using the analysis software designed in this paper, several typical faults of rolling bearings can be correctly identified.

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Weak Signal Detection Method Based on the Coupled Lorenz System and Its Application in Rolling Bearing Fault Diagnosis

Applied Sciences ◽

10.3390/app10124086 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4086

Author(s):

Guozheng Li ◽

Nanlin Tan ◽

Xiang Li

Keyword(s):

Fault Diagnosis ◽

Power Spectrum ◽

Lorenz System ◽

Signal To Noise Ratio ◽

Rolling Bearing ◽

Synchronization Error ◽

New Method ◽

Rolling Bearings ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Rolling bearings are widely used in rotating machinery. Their fault feature signals are often submerged in strong noise and are difficult to identify. This paper presents a new method of bearing fault diagnosis that combines the coupled Lorenz system and power spectrum technology. The process is achieved in the following three steps. First, a synchronization system based on the Lorenz system is constructed using the driving-response method. Second, when the tested signal is connected to the driving end, the synchronization error between the two sub-chaotic systems is obtained. Finally, the power spectrum density of the synchronization error is calculated and compared with the corresponding fault characteristic frequency. The coupled Lorenz system makes full use of the noise immunity and nonlinear amplification of the chaotic system. The detection characteristics and feasibility of the new method are verified by simulation and actual measured vibration data. The result shows that the noise reduction effect of the coupled Lorenz system is obvious. This method can improve the signal-to-noise ratio of the tested signal and provide a new way to perform fault diagnosis of rolling bearings.

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Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Applied Sciences ◽

10.3390/app9081681 ◽

2019 ◽

Vol 9 (8) ◽

pp. 1681 ◽

Cited By ~ 2

Author(s):

Cui ◽

Du ◽

Yang ◽

Xu ◽

Song

Keyword(s):

Rolling Bearing ◽

Rolling Bearings ◽

Outer Race ◽

Spectral Kurtosis ◽

Bearing Fault Diagnosis ◽

Fault Features ◽

Q Factors ◽

The Impact ◽

Fault Information ◽

Fault Characteristic

Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and achallenge is how to accurately separate the inner and outer race fault features from noisy compoundfaults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Qfactorsand improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, thecompound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonancecomponents of the signal (compound fault impact component and small amount of noise) are obtained,but it can only highlight the impact of compound faults, and failed to separate the inner and outerrace compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection ofparameters (the shift order M and the filter length L) based on the iterative calculation method withthe Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filteredand de-noised by the proposed method, the inner and outer race fault signals are obtained respectively.The fault characteristic frequency is consistent with the theoretical calculation value. The results showthat the proposed method can efficiently separate the mixed fault information and avoid the mutualinterference between the components of the compound fault.

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