Research on Remaining Useful Life Prediction of Rolling Element Bearings Based on Time-Varying Kalman Filter

Cui, Lingli; Wang, Xin; Wang, Huaqing; Ma, Jianfeng

doi:10.1109/tim.2019.2924509

Cited by 163 publications

(53 citation statements)

References 41 publications

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“…Moreover, a large amount of data collected by the collection system for mechanical system fault diagnosis led to the problem of high dimensionality and it is difficult to obtain a high accuracy rate for fault diagnosis. [11][12][13][14] Based on SVM, Suykens and Vandewalle 15 proposed an extension of Standard SVM, LSSVM algorithm. Inequality constraints in SVM are changed into equality constraints in LSSVM algorithm, thus greatly facilitating the solution of Lagrange multiplier.…”

Section: Introductionmentioning

confidence: 99%

A rolling bearing fault diagnosis method based on LSSVM

Gao

Wei

et al. 2020

Advances in Mechanical Engineering

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The fault characteristic signals of rolling bearings are coupled with each other, thus increasing the difficulty in identifying the fault characteristics. In this study, a fault diagnosis method of rolling bearing based on least squares support vector machine is proposed. First, least squares support vector machine model is trained with the samples of known classes. Least squares support vector machine algorithm involves the selection of a kernel function. The complexity of samples in high-dimensional space can be adjusted through changing the parameters of kernel function, thus affecting the search for the optimal function as well as final classification results. Particle swarm optimization and 10-fold cross-validation method are adopted to optimize the parameters in the training model. Then, with the optimized parameters, the classification model is updated. Finally, with the feature vector of the test samples as the input of least squares support vector machine, the pattern recognition of the testing samples is performed to achieve the purpose of fault diagnosis. The actual bearing fault data are analyzed with the diagnosis method. This method allows the accurate classification results and fast diagnosis and can be applied in the diagnosis of compound faults of rolling bearing.

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Section: Introductionmentioning

confidence: 99%

A rolling bearing fault diagnosis method based on LSSVM

Gao

Wei

et al. 2020

Advances in Mechanical Engineering

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“…However, the computational intensity of the complicated model becomes stronger, which will lead to a decrease in the training and detecting speed of the system in a large network structure. The method based on signal processing is also a common detection method for incipient faults [18][19][20]. For example, the signal feature processing based on the wavelet variation method [21] and empirical mode decomposition method [22] has been generalized successfully in many applications.…”

Section: Introductionmentioning

confidence: 99%

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

et al. 2020

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The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability.

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“…However, the motions and internal forces in the gearbox are complicated, which reduces the model accuracy and extends the calculation time. Signal processing is also a common early-stage fault detection method [16]- [17] such as, the empirical mode decomposition method [18] and wavelet transform method [19]. The feature extraction process contains some redundant information, the data dimension is high and the calculation efficiency is low, too.…”

Section: Introductionmentioning

confidence: 99%

Gearbox Incipient Fault Detection Based on Deep Recursive Dynamic Principal Component Analysis

et al. 2020

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As a part of the energy transmission chain, gearboxes are considered as important components in rotating machines, and the gearbox failure results in costly economic losses. Therefore, it is necessary to detect the appearance of incipient gearbox faults by implementing an appropriate detected model. The incipient failure characteristics of the gearbox are weak and hidden in a set of time-varying series signals the vibration signals, which is difficult to effectively extract under the background of strong noise. The PCA method is not effective in detecting weak fault features in time-varying signals, so this paper proposes a method based on Deep Recursive Dynamic Principal Component Analysis (Deep RDPCA) to detect incipient faults in gearboxes. The proposed approach is modeled via both the deep decomposed theorems and time-varying dynamic model based on traditional PCA to extract characteristic of time-varying and weak fault information under the background of strong noise. The proposed method could get a better real-time reflection for changed system by introducing ''Moving Window'' technologies, so that the incipient fault of gearbox could be detected accurately, too. Finally, the effect of Deep RDPCA-based fault diagnosis is compared with the results of PCA, DPCA, RDPCA, Deep PCA, and Deep DPCA methods. It is concluded that the proposed method can effectively capture the time-varying relationship of process variables and accurately extract the weak fault characteristics in the vibration signal, which effectively improves the fault detection performance. INDEX TERMS Gearbox, fault diagnosis, gear failure experiment, feature extraction, Deep RDPCA.

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Research on Remaining Useful Life Prediction of Rolling Element Bearings Based on Time-Varying Kalman Filter

Cited by 163 publications

References 41 publications

A rolling bearing fault diagnosis method based on LSSVM

A rolling bearing fault diagnosis method based on LSSVM

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

Gearbox Incipient Fault Detection Based on Deep Recursive Dynamic Principal Component Analysis

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