Comparative performance of prognostics for remaining useful life of bearing

Nistane, V. M.

doi:10.1177/0957456520947989

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…Lu et al (2021) introduced generative adversarial network–LSTM predictor for failure prognostics of REB. Considering single-point localized defect at different location in REBs to evaluate the status of bearing was proposed by Nistane (2020). Zhao et al (2021) proposed to predict RUL of bearing using feature extraction for data-driven RUL.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimum prediction model of remaining useful life for rolling element bearing based on integrating optimize health indicator (OHI) and machine learning algorithm

Nistane

2022

WJE

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Purpose Rolling element bearings (REBs) are commonly used in rotating machinery such as pumps, motors, fans and other machineries. The REBs deteriorate over life cycle time. To know the amount of deteriorate at any time, this paper aims to present a prognostics approach based on integrating optimize health indicator (OHI) and machine learning algorithm. Design/methodology/approach Proposed optimum prediction model would be used to evaluate the remaining useful life (RUL) of REBs. Initially, signal raw data are preprocessing through mother wavelet transform; after that, the primary fault features are extracted. Further, these features process to elevate the clarity of features using the random forest algorithm. Based on variable importance of features, the best representation of fault features is selected. Optimize the selected feature by adjusting weight vector using optimization techniques such as genetic algorithm (GA), sequential quadratic optimization (SQO) and multiobjective optimization (MOO). New OHIs are determined and apply to train the network. Finally, optimum predictive models are developed by integrating OHI and artificial neural network (ANN), K-mean clustering (KMC) (i.e. OHI–GA–ANN, OHI–SQO–ANN, OHI–MOO–ANN, OHI–GA–KMC, OHI–SQO–KMC and OHI–MOO–KMC). Findings Optimum prediction models performance are recorded and compared with the actual value. Finally, based on error term values best optimum prediction model is proposed for evaluation of RUL of REBs. Originality/value Proposed OHI–GA–KMC model is compared in terms of error values with previously published work. RUL predicted by OHI–GA–KMC model is smaller, giving the advantage of this method.

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Section: Literature Reviewmentioning

confidence: 99%

Optimum prediction model of remaining useful life for rolling element bearing based on integrating optimize health indicator (OHI) and machine learning algorithm

Nistane

2022

WJE

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Plastic gear remaining useful life prediction using artificial neural network

Kien

Iba

Tsutsui

et al. 2021

Forsch Ingenieurwes

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Research on rolling bearing fault diagnosis method based on simulation and experiment fusion drive

Li,

Wang,

Zhao

et al. 2024

Review of Scientific Instruments

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Aiming at the problem that the rolling bearing fault data are difficult to obtain and that the traditional fault diagnosis method does not consider the signal uncertainty characteristics and the low accuracy of models in the process of rolling bearing fault, a fault diagnosis method based on simulation and experiment fusion drive is proposed. First, the dynamics simulation model of rolling bearings under different fault conditions is established to obtain the bearing fault simulation signals. Second, a sequence generative adversarial network is used to fuse the simulation and experimental data. Bearing vibration signals are often very uncertain, so considering the probability characteristics of fault signals, the probability box model under different fault states is constructed by the direct probability box modeling method, and its characteristic vectors are extracted. Finally, an extreme gradient boosting Tree model for fault diagnosis classification is constructed to compare and evaluate the classification and diagnosis effects of bearing states before and after data fusion. The results show that the proposed method has a good diagnostic effect and is suitable for solving the fault diagnosis problem under the condition of insufficient data.

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Comparative performance of prognostics for remaining useful life of bearing

Cited by 4 publications

References 42 publications

Optimum prediction model of remaining useful life for rolling element bearing based on integrating optimize health indicator (OHI) and machine learning algorithm

Optimum prediction model of remaining useful life for rolling element bearing based on integrating optimize health indicator (OHI) and machine learning algorithm

Plastic gear remaining useful life prediction using artificial neural network

Research on rolling bearing fault diagnosis method based on simulation and experiment fusion drive

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