A New Online Detection Approach for Rolling Bearing Incipient Fault via Self-Adaptive Deep Feature Matching

Mao, Wentao; Chen, Jiaxian; Liang, Xun; Zhang, Xinming

doi:10.1109/tim.2019.2903699

Cited by 99 publications

(31 citation statements)

References 32 publications

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“…However, these two methods estimate the fault occurrence merely using the data of target bearing, but they don't focus on extracting accurate early fault features, especially from massive data of auxiliary bearings. Mao et al [21] noticed the distribution difference between auxiliary bearings and target bearing, and proposed an online fault detection method. Although this method can effectively use auxiliary bearings data to establish detection model, it improves the detection performance mainly by proposing a strategy named self-adaptive deep feature matching (SDFM), not reducing such distribution difference.…”

Section: Preliminary Workmentioning

confidence: 99%

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Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

et al. 2020

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In recent years, machine learning techniques have been proven to be a promising tool for early fault detection of rolling bearings. In many actual applications, however, bearing whole-life data are not easy to be historically accumulated, while insufficient data may result in training a detection model that is not good enough. If utilizing the available data under different working conditions to facilitate model training, the data distribution of different bearings are usually quite different, which does not meet the precondition of i n d e p e n d e n t a n d i d e n t i c a l d i s t r i b u t i o n ( i . i . d ) and tends to cause performance reduction. In addition, disturbed by the unstable noise under complex conditions, most of the current detection methods are inclined to raise false alarms, so that the reliability of detection results needs to be improved. To solve these problems, a robust detection method for bearings early fault is proposed based on deep transfer learning. The method includes offline stage and online stage. In the offline stage, by introducing a deep auto-encoder network with domain adaptation, the distribution inconsistency of normal state data among different bearings can be weakened, then the common feature representation of the normal state is obtained. With the extracted common features, a new state assessment method based on the robust deep auto-encoder network is proposed to evaluate the boundary between normal state and early fault state in the low-rank feature space. By training a support vector machine classifier, the detection model is established. In the online stage, along with the data batch arriving sequentially, the features of target bearing are extracted using the common representation learnt in the offline stage, and online detection is conducted by feeding them into the SVM model. Experimental results on IEEE PHM Challenge 2012 bearing dataset and XJTU-SY dataset show that the proposed approach outperforms several state-of-the-art detection methods in terms of detection accuracy and false alarm rate.

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Section: Preliminary Workmentioning

confidence: 99%

“…In addition, we also compare two state-of-the-art methods, BEMD+AMMA [34] and SDFM [21]. BEMD+AMMA which utilizes bandwidth EMD is viewed as the state-of-the-art incipient fault diagnosis method based on signal analysis.…”

Section: Comparative Experimentsmentioning

confidence: 99%

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

et al. 2020

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“…Also, incipient faults are often confused to be noise signals or uncertain behaviour of the system. The detection, therefore, becomes more elusive than the traditional fault detection methods [19,24].…”

Section: Introductionmentioning

confidence: 99%

Incipient fault detection of sensors used in wastewater treatment plants based on deep dropout neural network

Mali

Laskar

2020

SN Appl. Sci.

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Risk of sudden collapse of any industrial plant increases if small magnitude incipient faults are not detected at an early stage. The paper proposes an optimized Monte Carlo deep dropout neural network (MC-DDNN) to identify incipient faults of sensors installed in wastewater treatment plants using the historical dataset of the plant. Such faults usually remain invisible or are misinterpreted as noise signals due to their small magnitude but can be overcome by the proposed method. MC-DDNN easily identifies the incipient faults of sensors installed in a simulated wastewater treatment benchmark model as well as sensors installed in a real industrial plant. The tabulated results show the estimated probability of incipient fault in terms of percentage probability as detected by the MC-DDNN. The dissolved oxygen (DO) sensor incipient faults in benchmark simulation model (BSM2) are detected with probability ranging from 4.9% to 23.4% and DO, pH and mixed liquor suspended solids (MLSS) sensors of effluent treatment plant (ETP) are detected with probability ranging from 0.07% to 11.43%. This estimated probability of faults indicates the small magnitude of the faults and hence proves that the method is capable of identifying faults at an early stage to issue warnings for early maintenance of the plant.

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“…The failures of rolling bearings often develop from the normal stage to the incipient stage, then enter the stage of repeated failure, and finally reach the stage of complete breakdown. The serious faults do not occur in an instant, but has a process of gradual deterioration [5][6][7]. Therefore, it is of great significance to study the incipient fault detection of rolling bearings, find the incipient signal characteristics of faults, and eliminate the safety hazards in time when the fault has not developed toward a serious degree [8,9].…”

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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A New Online Detection Approach for Rolling Bearing Incipient Fault via Self-Adaptive Deep Feature Matching

Cited by 99 publications

References 32 publications

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

Incipient fault detection of sensors used in wastewater treatment plants based on deep dropout neural network

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

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