A Hybrid Prognostics Technique for Rolling Element Bearings Using Adaptive Predictive Models

Ahmad, Wasim; Khan, Sheraz Ali; Kim, Jong‐Myon

doi:10.1109/tie.2017.2733487

Cited by 160 publications

(76 citation statements)

References 25 publications

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“…Kurtosis was used as a reference in the method of N. Li et al [3]. The SPT determination results of each method (the As shown in Figure 9, the SPT determined by the method of W. Ahmad et al [13] is earlier than that determined by the method in this paper. This is because the method of W. Ahmad et al [13] determines the SPT on the basis of a fixed and single trigger threshold, while the method in this paper chooses the threshold based on the normal stage information of rolling bearings.…”

Section: Spt Point Of Experimental Datamentioning

confidence: 63%

“…A new linear regression model is constructed by sliding a sampling point forward in the window. This method can be expressed as in [13]: [14]. The time span of the experiment was 164 hours.…”

Section: Mathematical Model Of the Proposed Methodsmentioning

confidence: 99%

“…The SPT selection method proposed in this paper was compared with the method of W. Ahmad et al [13]. The RMS gradient was also used as a reference.…”

Section: Spt Point Of Experimental Datamentioning

confidence: 99%

“…This is because the method of W. Ahmad et al [13] determines the SPT on the basis of a fixed and single trigger threshold, while the method in this paper chooses the threshold based on the normal stage information of rolling bearings. Therefore, the method of W. Ahmad et al [13] carries out RUL prediction prematurely. Furthermore, the presented method was compared with the method of N. Li et al [3].…”

Section: Spt Point Of Experimental Datamentioning

confidence: 99%

“…However, the setting of the baseline depends on the maximum time constant proposed by the author, which is related to engineering experience. W. Ahmad et al [13] proposed a data-driven RUL prediction method for rolling bearings. This method makes use of the residual between the root mean square (RMS) of the rolling bearing vibration signal acquired in real time and the RMS of the rolling bearing vibration signal in the normal state to determine the SPT point.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Method for Determining Starting Point of Rolling Bearing Life Prediction Based on Linear Regression

Gao

Wu³

et al. 2019

Electronics

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Aimed at addressing the problem that the subjective selection of start prediction time (SPT) in rolling bearing remaining useful life (RUL) prediction will lead to excessive noise in the prediction signal, a linear-regression-based SPT point determination was proposed. The sliding window linear regression method was used to establish sliding windows in the root mean square (RMS) range to obtain the RMS gradient domain. The threshold for the RMS gradient was set, and the continuous trigger threshold mechanism to determine the SPT point was used. The experimental results show that the linear-regression-based method can adaptively determine the SPT point and improve the accuracy of life prediction.

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Section: Spt Point Of Experimental Datamentioning

confidence: 63%

Section: Mathematical Model Of the Proposed Methodsmentioning

confidence: 99%

“…The SPT selection method proposed in this paper was compared with the method of W. Ahmad et al [13]. The RMS gradient was also used as a reference.…”

Section: Spt Point Of Experimental Datamentioning

confidence: 99%

Section: Spt Point Of Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Method for Determining Starting Point of Rolling Bearing Life Prediction Based on Linear Regression

Gao

Wu³

et al. 2019

Electronics

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Using temporal convolution network for remaining useful lifetime prediction

Chen

Liu

2020

Engineering Reports

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Because Convolutional Neural Network (CNN) can extract spatial feature, while Long Short‐Term Memory (LSTM) can learn temporal features, many methods combining CNN with LSTM are proposed for remaining useful lifetime prediction. In practice, it is better to learn temporal features from long history sequence data because of the slow inherently long‐term degradation process. However, LSTM is less efficient in processing long history sequence. To solve this problem, in this work, a temporal convolution network combining causal filters with dilated convolutions is used to expand the receptive field length of network. The network structure can be fixed through three key parameters, and the size of time window adopted for time sequence processing is the same as the receptive field length. These two characteristics allow the network to easily be applied for engineering purposes. The method is tested and evaluated using two well‐known datasets, namely the “Turbofan Engine Degradation Simulation Dataset C‐MAPSS” and “Milling Dataset.” The performance analysis shows that the proposed method outperforms more classical methods in terms of prediction accuracy.

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