Health indicator construction by quadratic function-based deep convolutional auto-encoder and its application into bearing RUL prediction

Chen, Dingliang; Qin, Yi; Wang, Yi; Zhou, Jianghong

doi:10.1016/j.isatra.2020.12.052

Cited by 116 publications

(36 citation statements)

References 25 publications

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“…In view of the individual variability of bearing degradation trends, the CAE is used to automatically extract degradation characteristics from bearing monitoring data sets and realize the effective extraction of bearing performance degradation features without prior knowledge of bearing RUL. Compared with the traditional autoencoder, CAE, as an unsupervised deep learning method, uses convolutional operation for the encoding and decoding part instead of slicing and stacking the data, which significantly improves the performance of training parameter optimization and feature extraction [ 46 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…The scoring function has been adopted by many researchers and IEEE PHM 2012 Prognostic Challenge [ 46 ]. By considering the different weights of earlier and later prediction results, the scoring function is defined as follows:

where i∈ [ 1 , 11 ] states for the test bearings defined in Table 1 , actRULi and RULi denote the RUL of the bearing estimated by the experimental participants and the actual RUL to be predicted, respectively.…”

Section: Experimental Verificationmentioning

confidence: 99%

“…To verify the performance of the proposed method, several state-of-the-art RUL prediction methods are compared, including Sutrisno's vibration frequency signature anomaly detection and survival time ratio [ 13 ], Hong's combinatorial feature extraction and self-organization mapping [ 17 ], Guo's recurrent neural-network-based health indicator [ 24 ], Singleton's extended Kalman filter-based method [ 46 ], Zhu's multiscale convolutional neural network-based method [ 22 ], Cheng's transferable convolutional neural network-based method [ 29 ], Mao's deep feature representation and transfer learning [ 30 ], and Li's deep adversarial neural networks-based method [ 33 ].…”

Section: Experimental Verificationmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Method for Remaining Useful Life Prediction of Roller Bearings Involving the Discrepancy and Similarity of Degradation Trajectories

Luo

Lin

Liu

et al. 2021

Computational Intelligence and Neuroscience

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Accurate remaining useful life (RUL) prediction of bearings is the key to effective decision-making for predictive maintenance (PdM) of rotating machinery. However, the individual heterogeneity and different working conditions of bearings make the degradation trajectories of bearings different, resulting in the mismatch between the RUL prediction model established by the full-life training bearing and the testing bearings. To address this challenge, this paper proposes a novel RUL prediction method for roller bearings that considers the difference and similarity of degradation trajectories. In this method, a feature extraction method based on continuous wavelet transform (CWT) and convolutional autoencoder (CAE) is proposed to extract the deep features associated with bearing performance degradation before the degradation indicator (DI) is obtained by applying the self-organizing maps (SOM) method. Next, a dynamic time warping (DTW) based method is applied to perform the similarity matching of degradation trajectories of the training and testing bearings. Driven by the historical DIs of the given bearing, the grey forecasting model with full-order time power terms (FOTP-GM) is applied to model the degradation trajectory using a parameter optimization method. Then, the failure threshold of the given testing bearing can be determined using a data-driven method without manual intervention. Finally, the RUL of the given testing bearing can be estimated using the preset failure threshold and the optimized degradation trajectory model of the given testing bearing. The experimental results show that the proposed method retains the individual differences of bearing degradation trend, realizes the independent and reasonable bearing failure threshold setting, and improves the prediction accuracy of RUL.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Experimental Verificationmentioning

confidence: 99%

Section: Experimental Verificationmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Method for Remaining Useful Life Prediction of Roller Bearings Involving the Discrepancy and Similarity of Degradation Trajectories

Luo

Lin

Liu

et al. 2021

Computational Intelligence and Neuroscience

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“…The detection of weak faults is one of the main approach of detecting faults at the early degradation, and is of great significance for the prognosis and maintenance of related devices. [7][8][9][10] Recently weak fault detection is mainly carried out through the analysis of status signals, such as vibration and sound. [11][12][13] Ye and Yu 14 put forward a deep morphological network for feature learning from vibration signal, and applied the morphological layer in the extraction of impulses and filtering of noise.…”

Section: Introductionmentioning

confidence: 99%

A two-step vibration-sound signal fusion method for weak fault feature detection in rolling bearing systems

Yan

Choi

et al. 2021

Advances in Mechanical Engineering

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The vibration and sound signals get widely applications in fault diagnosis of rolling bearing systems, but the detection accuracy is unstable at different measuring positions. This paper puts forward a two-step vibration-sound signal fusion method, in which sound signal fusion and vibration-sound signal fusion are executed respectively. The sound signals are fused through weighting to the vibration signal to reduce the influence by measuring positions, and the phase difference is eliminated by a sliding window on the time axis. Then a second fusion between the vibration signal and sound signal is conducted after normalization and superposition, and the performance of two-step fusion is compared with the existing direct fusion. Results show that the two-step fusion provides a larger signal-to-noise ratio, and the amplitudes of characteristic frequencies are also higher. A cascaded bistable stochastic resonance system is applied in the post-processing of the fusion signal to make the signal features more clear, and it is proved that the fault detection effect has an obvious improvement after the whole process. This method provides a new approach for weak fault feature detection in vibration and sound signals, and is of great significance for the maintenance of rolling bearing systems.

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“…In the long term, it will lead to bearings' performance degradation or even sudden failure, resulting in downtime and even casualties [1,2]. Condition monitoring and life prediction of bearings can avoid the unnecessary shutdown and improve the reliability and safety of QC bearings [3,4].…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Quay Crane Hoist Gearbox Bearing under Dynamic Operating Conditions Based on ARIMA‐CAPF Framework

Sun

Xiong

Dong

2021

Shock and Vibration

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The remaining useful life (RUL) prediction of quay crane (QC) bearings is of great significance to port production safety. An RUL prediction framework of QC bearing under dynamic conditions is proposed. Firstly, the load is discretized, and the corresponding operating conditions are classified. Then, the Autoregressive Integrated Moving Average (ARIMA) model is utilized to predict the load and corresponding operating conditions. Secondly, a Wiener process considering degradation rates and jump coefficients under different operating conditions is developed as the state transfer function. Finally, a condition-activated particle filter (CAPF) is proposed to predict the system state and the bearing’s RUL. The proposed prediction framework is verified by the hoist bearing life cycle data from a port in Shanghai collected by the NetCMAS system. The prediction results by the ARIMA-CAPF framework in comparison with three other prediction strategies identify the effectiveness.

show abstract

Health indicator construction by quadratic function-based deep convolutional auto-encoder and its application into bearing RUL prediction

Cited by 116 publications

References 25 publications

A Novel Method for Remaining Useful Life Prediction of Roller Bearings Involving the Discrepancy and Similarity of Degradation Trajectories

A Novel Method for Remaining Useful Life Prediction of Roller Bearings Involving the Discrepancy and Similarity of Degradation Trajectories

A two-step vibration-sound signal fusion method for weak fault feature detection in rolling bearing systems

Remaining Useful Life Prediction of Quay Crane Hoist Gearbox Bearing under Dynamic Operating Conditions Based on ARIMA‐CAPF Framework

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