Bearing Life Prediction With Informed Hyperprior Distribution: A Bayesian Hierarchical and Machine Learning Approach

Mishra, Madhav; Martinsson, Jesper; Goebel, Kai; Rantatalo, Matti

doi:10.1109/access.2021.3130157

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…These methods search and evaluate the parameter space to find the optimal combination of parameters, optimizing the performance of the model. Common ML algorithms used for this task include linear regression, support vector machines, decision trees, random forests, gradient boosting trees, and neural networks [107][108][109][110]. These algorithms can be selected and adjusted based on the characteristics of the data and the requirements of the task to obtain accurate and reliable RUL prediction models.…”

Section: Supervised ML For Remaining Life Predictionmentioning

confidence: 99%

“…Wang et al [111] combined monitoring bearing data with historical data, established comprehensive similarity analysis, constructed a scaling adjustment function, and dynamically modified the parameter of the state matrix model to achieve adaptive prediction of monitoring bearings. Mishra et al [108] incorporated prior distribution knowledge of the rated life of bearings and used a Bayesian three-level hierarchical model to continuously update and optimize the parameters to improve the model's performance. Wang et al [112] optimized hyperparameters using the Nesterov accelerated gradient method, effectively avoiding the overfitting that can occur when solving analytical solutions.…”

Section: Supervised ML For Remaining Life Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Advancements in bearing remaining useful life prediction methods: a comprehensive review

Song,

Lin,

Jin

et al. 2024

Meas. Sci. Technol.

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This paper presents a comprehensive review of the state-of-the-art techniques for predicting the remaining useful life (RUL) of rolling bearings. Four key aspects of bearing RUL prediction are considered: data acquiring, construction of health indicators (HI), development of RUL prediction algorithms, and evaluation of prediction results. Additionally, publicly available datasets that can be used to validate bearing prediction algorithms are described. The existing RUL prediction algorithms are categorized into three types and have been comprehensively reviewed: physical-based, statistical-based, and data-driven. In particular, the progress made in data-driven prediction methods is summarized, and typical methods such as RNN, TCN, GCN, Transform, and TL-based methods are introduced in detail. Finally, the challenges faced by data-driven methods in RUL prediction for bearings are discussed.

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Section: Supervised ML For Remaining Life Predictionmentioning

confidence: 99%

Section: Supervised ML For Remaining Life Predictionmentioning

confidence: 99%

Advancements in bearing remaining useful life prediction methods: a comprehensive review

Song,

Lin,

Jin

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…In recent years, a variety of Machine Learning (ML) algorithms have been proposed to perform the task of bearing RUL predictions. These algorithms vary in complexity and efficiency and often incorporate supervised ML approaches such as k-Nearest Neighbour (k-NN) and Support Vector Machines (SVM) [3,15,16], regression model approaches [14,39], deep learning models including convolutional neural networks (CNN) and deep belief networks (DBN) [41,42], blind deconvolution methods [43,44], and Bayesian probabilistic prediction models such as Kalman and particle filtering [45,46]. This paper introduces a low complexity prognostic monitoring approach for RUL estimation of mechanical rolling element bearings that builds further on previous work by the authors, which achieved RUL classification accuracy percentages of 74.3% using signal EA combined with novel feature engineering techniques [1].…”

Section: Introductionmentioning

confidence: 99%

Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings

2023

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Improving the reliability and performance of electric and rotating machines is crucial to many industrial applications. This will lead to improved robustness, efficiency, and eco-sustainability, as well as mitigate significant health and safety concerns regarding sudden catastrophic failure modes. Bearing degradation is the most significant cause of machine failure and has been reported to cause up to 75% of low-voltage machine failures. This paper introduces a low complexity machine learning (ML) approach to estimate the remaining useful life (RUL) of rolling element bearings using real vibration signals. This work explores different ML recipes using novel feature engineering coupled with various k-Nearest Neighbour (k-NN), and Support Vector Machines (SVM) kernel and weighting functions in order to optimise this RUL approach. Original non-linear wear state models and feature sets are investigated, the latter are derived from Short-time Fourier Transform (STFT) and Hilbert Marginal Spectrum (HMS). These feature sets incorporate one-third octave band filtering for low complexity multivariate feature subspace compression. Our proposed ML algorithm stage has employed two robust supervised ML approaches: weighted k-NN and SVM. Real vibration data were drawn from the Pronostia platform to test and validate this prognostic monitoring approach. The results clearly demonstrate the effectiveness of this approach, with classification accuracy results of up to 82.8% achieved. This work contributes to the field by introducing a robust and computationally inexpensive method for accurate monitoring of machine health using low-cost vibration-based sensing.

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Research on fault time prediction method for high speed rail BTM unit based on multi method interactive validation

Fu,

Gou,

Sun

et al. 2024

High-speed Railway

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Bearing Life Prediction With Informed Hyperprior Distribution: A Bayesian Hierarchical and Machine Learning Approach

Cited by 6 publications

References 20 publications

Advancements in bearing remaining useful life prediction methods: a comprehensive review

Advancements in bearing remaining useful life prediction methods: a comprehensive review

Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings

Research on fault time prediction method for high speed rail BTM unit based on multi method interactive validation

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