The Degradation State Recognition of Rolling Bearing Based on GA and SVM

Wei, Yonghe; Wang, Minghua

doi:10.2991/meic-14.2014.123

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…SVM has good generalization ability, but it needs strict adjustment of kernel parameters and cannot solve multi-level problems effectively. In Reference [34], GA and SVM were combined to recognize the pattern of rolling bearing. In Reference [35], the time-frequency matrix of rolling bearing signal was calculated to extract fault feature and ANFIS was utilized to classify the pattern of rolling bearing fault.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearings Based on Improved Fast Spectral Correlation and Optimized Random Forest

et al. 2018

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Fault diagnosis of rolling bearings is important for ensuring the safe operation of industrial machinery. How to effectively extract the fault features and select a classifier with high precision is the key to realizing the fault recognition of bearings. Accordingly, a new fault diagnosis method of rolling bearings based on improved fast spectral correlation and optimized random forest (i.e., particle swarm optimization-random forest (PSO-RF)) is proposed in this paper. The main contributions of this study are made from two aspects. One is that an improved fast spectral correlation approach was developed to extract the fault features of bearings and form the feature vector more effectively. The other is that an optimized random forest classifier was developed to achieve highly accurate identification by exploiting particle swarm optimization to select the best parameters of random forest (RF). In the presented method, improved fast spectral correlation was first utilized to analyze the raw vibration signal caused by a faulty bearing to obtain the enhanced envelope spectrum. Then, the amplitudes of the four characteristic cyclic frequencies (i.e., the rotating frequency, the characteristic frequency of outer-race fault, the characteristic frequency of inner-race fault, and the characteristic frequency of rolling element fault) exhibited in the enhanced envelope spectrum were selected to form the feature vector. Finally, the PSO-RF method was introduced for identifying and classifying bearing faults. The experimental investigations demonstrate the proposed method can accurately identify bearing faults and outperform other state-of-art techniques considered.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearings Based on Improved Fast Spectral Correlation and Optimized Random Forest

et al. 2018

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“…Shao et al and Pham used the root mean square and the kurtosis factor of the vibration signal to assess the performance degradation and predict a bearing's remaining life [1,2]. Wei et al applied ensemble empirical mode decomposition to decompose bearing vibration signals to obtain the features used for a degradation state estimation [3]. Liao and Lee utilized wavelet packet decomposition and principal component analysis to extract features of bearing vibration signals [4].…”

Section: Introductionmentioning

confidence: 99%

Curve similarity recognition based rolling bearing degradation state estimation and lifetime prediction

Li-pin¹,

Lu²,

Tao³

2016

J. vibroeng.

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The health state of a rolling bearing keeps changing from a normal state to a slight degradation state followed eventually by a severely degraded state. To make reasonable inspection and maintenance plans, it is necessary to estimate the degradation state and predict the lifetime of a running rolling bearing accurately and in a timely fashion. This paper presents a new method for rolling bearing degradation state estimation and lifetime prediction based on curve similarity recognition. Different from existing methods, this method employs a dynamic time warping algorithm to recognize the curve similarity of those extracted features of rolling bearings in different states of health, which can reflect the intrinsic state of the rolling bearing; it discretizes the bearing degradation state reasonably through curve similarity. Next, the curve similarity is used to train the degradation state estimation model and a support vector machine based lifetime prediction model. Finally, this paper conducts a case study for a rolling bearing with impact degradation and one with wear degradation, respectively. The experimental results indicate that the new proposed method is highly efficient in recognizing the bearing's degradation state and predicting its lifetime. University. Ph.D. in System Engineering (2014). His research is focused on fault diagnosis, prognostics and health management for electromechanical systems especially for rotating machinery, battery, and wind turbine. It comprises the analysis, development, and optimization of related algorithms and systems.

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Rolling bearing performance degradation assessment based on deep belief network and improved support vector data description

Pan

Cheng²,

Wei³

et al. 2022

Mechanical Systems and Signal Processing

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The Degradation State Recognition of Rolling Bearing Based on GA and SVM

Cited by 3 publications

References 5 publications

Fault Diagnosis of Rolling Bearings Based on Improved Fast Spectral Correlation and Optimized Random Forest

Fault Diagnosis of Rolling Bearings Based on Improved Fast Spectral Correlation and Optimized Random Forest

Curve similarity recognition based rolling bearing degradation state estimation and lifetime prediction

Rolling bearing performance degradation assessment based on deep belief network and improved support vector data description

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