Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

Ali, Jaouher Ben; Chebel-Morello, Brigitte; Saïdi, Lotfi; Malinowski, Simon; Fnaiech, Farhat

doi:10.1016/j.ymssp.2014.10.014

Cited by 236 publications

(95 citation statements)

References 29 publications

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“…Compared to the information entropy method and the empirical mode decomposition method, time domain analysis is less affected by the interruption of time-frequency signals, the steps of feature extraction are relatively simple, and different time domain features contain different information in the vibration signal. By comparing and analyzing the time domain feature extraction methods proposed in previous research, the latest or most widely-used feature extraction methods [43][44][45] were selected, as shown in Table 1. Table 1.…”

Section: Feature Extraction Methods Based On Vibration Sensing Datamentioning

confidence: 99%

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Huang

Liu

2019

Sensors

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Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model's ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

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Section: Feature Extraction Methods Based On Vibration Sensing Datamentioning

confidence: 99%

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Huang

Liu

2019

Sensors

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“…Thus, the log-likelihood can be written as: For non-censored and censored failures, the log-likelihood will always have a negative contribution with increasing t, penalizing as time progresses. Following Herp et al [9] we consider a Weibull distribution, for its tractable properties, intuitive parametrization and use in other related studies [19][20][21][22]:…”

Section: Empirical Remaining-useful-lifetime Estimationmentioning

confidence: 99%

“…It adopts the closed-form approach by Si et al [17] by assuming the RUL can be quantified by a closed-form expression and embeds it in a NN, to make confidence calculations of the predictions easier. A Weibull distribution has been chosen as a lifetime distribution, since literature already has shown its use in connection with NNs [19][20][21][22]. In contrast to Ranganath et al [19] deep exponential families model, this work will limit itself to Weibull distribution's only.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Early Stopping through Statistical Health Prognostic Models for Empirical RUL Estimation in Wind Turbine Main Bearing Failure Monitoring

2019

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Details about a fault’s progression, including the remaining-useful-lifetime (RUL), are key features in monitoring, industrial operation and maintenance (O&M) planning. In order to avoid increases in O&M costs through subjective human involvement and over-conservative control strategies, this work presents models to estimate the RUL for wind turbine main bearing failures. The prediction of the RUL is estimated from a likelihood function based on concepts from prognostics and health management, and survival analysis. The RUL is estimated by training the model on run-to-failure wind turbines, extracting a parametrization of a probability density function. In order to ensure analytical moments, a Weibull distribution is assumed. Alongside the RUL model, the fault’s progression is abstracted as discrete states following the bearing stages from damage detection, through overtemperature warnings, to over overtemperature alarms and failure, and are integrated in a separate assessment model. Assuming a naïve O&M plan (wind turbines are run as close to failure as possible without regards for infrastructure or supply chain constrains), 67 non run-to-failure wind turbines are assessed with respect to their early stopping, revealing the potential RUL lost. These are turbines that have been stopped by the operator prior to their failure. On average it was found that wind turbines are stopped 13 days prior to their failure, accumulating 786 days of potentially lost operations across the 67 wind turbines.

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“…Failure rate function is the measurement which indicates the failure probability of a certain unit at a given time [21]. Defined by three parameters, the WFRF is mathematically represented as [32] ( ) = ( − )…”

Section: The Generalized Weibull Failure Rate Functionmentioning

confidence: 99%

“…The generalized WFRF is the updated version of WFRF. The equation of the generalized WFRF is as follows [21,32].…”

Section: The Generalized Weibull Failure Rate Functionmentioning

confidence: 99%

Remaining Useful Life Prediction of Bearing with Vibration Signals Based on a Novel Indicator

Qiu

2017

Shock and Vibration

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Aiming at reducing the production downtime and maintenance cost, prognostics and health management (PHM) of rotating machinery often includes the remaining useful life (RUL) prediction of bearings. In this paper, a method combining the generalized Weibull failure rate function (WFRF) and radial basis function (RBF) neural network is developed to deal with the RUL prediction of bearings. A novel indicator, namely, the power value on the sensitive frequency band (SFB), is proposed to track bearing degradation process. Generalized WFRF is used to fit the degradation indicator series to reduce the effect of noise and avoid areas of fluctuation in the time domain. RBF neural network is employed to predict the RUL of bearings with times and fitted power values at present and previous inspections as input. Meanwhile, the life percentage is selected as output. The performance of the proposed method is validated by an accelerated bearing run-to-failure experiment, and the results demonstrate the advantage of this method in achieving more accurate RUL prediction.

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Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

Cited by 236 publications

References 29 publications

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Assessment of Early Stopping through Statistical Health Prognostic Models for Empirical RUL Estimation in Wind Turbine Main Bearing Failure Monitoring

Remaining Useful Life Prediction of Bearing with Vibration Signals Based on a Novel Indicator

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