Prognosticating RULs while exploiting the future characteristics of operating profiles

Jain, Amit Kumar; Lad, Bhupesh Kumar

doi:10.1016/j.ress.2020.107031

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…Moreover, the model excellently estimated the bearing RUL. Jain and Lad [23] developed the innovative design of a generic Tool Condition Monitoring system that accounts for real-life future degradation profile viz. low, medium, and severe profile.…”

Section: Introductionmentioning

confidence: 99%

Prognostics of Induction Motor Shaft Based on Feature Importance and Least Square Support Vector Machine Regression

Susilo¹,

Widodo²,

Prahasto³

et al. 2021

IJAME

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This paper aims to present a prognostic method for induction motor shafts that experience fatigue failure in the keyway area, using motor vibration signals. Preprocessing the data to eliminate noise in raw signals is done by decomposing the signal, using discrete wavelet transforms. Prognostic indicator candidates are obtained through the selection of features based on its importance, which involve the superposition of monotonicity and trendability parameters. The prognostics model is built based on the least squares support vector machine regression approach. Remaining useful life (RUL) estimates of motor shafts were performed by fitting the sum of two exponential functions to the regression results and extrapolating over time until the specified failure threshold hits. The results of the study show that the proposed method can work satisfactorily to estimate the RUL of motor shaft. The best prognostic indicator namely the RMS, can be used to predict the motor shaft RUL since 50% of the time step before the end of the motor shaft life is error bound within 20%.

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Section: Introductionmentioning

confidence: 99%

Prognostics of Induction Motor Shaft Based on Feature Importance and Least Square Support Vector Machine Regression

Susilo¹,

Widodo²,

Prahasto³

et al. 2021

IJAME

View full text Add to dashboard Cite

show abstract

“…6 Tool wear can be divided into three stages according to the degree of wear, including initial wear, normal wear and severe wear. 7 Based on the wear characteristics of different wear stages, it is possible to predict the future wear of the cutting tool 8 or to make tool RUL predictions 9 with the analysis of large amounts of historical monitoring data. Various studies have conducted different forms of model analysis to deal with the processing of big data, including physics-based models, data-driven models and a series of hybrid models.…”

Section: Introductionmentioning

confidence: 99%

Tool remaining useful life prediction considering wear state based on hybrid attention network

Wu,

Li,

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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Accurate prediction of the remaining useful life for the cutting tool is a key part of the predictive maintenance of computer numerical control machines. However, the wide variety of tools makes the process of modeling different tool wear regularities redundant and cumbersome. In addition, it is difficult to deal with the input characteristics of multi-sensor monitoring signals in a targeted manner. To solve the above problems, a hybrid predictive model with squeeze-and-excitation (SE) module is proposed. Combined with adaptive feature extraction based on convolutional neural network and observation based on bidirectional gated recurrent unit, accurate multivariate regression prediction is achieved. The SE module enhances the focus on crucial features. Finally, through the design of the tool wear experiment and the combination of the public dataset, the accuracy and generalization ability of the proposed model are verified under different tool types and different working conditions.

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Physics-Based Prognostics: Review and Future Directions

Jain,

Chen,

Alkali

et al. 2024

Lecture Notes on Multidisciplinary Industrial Engineering

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Prognosticating RULs while exploiting the future characteristics of operating profiles

Cited by 7 publications

References 27 publications

Prognostics of Induction Motor Shaft Based on Feature Importance and Least Square Support Vector Machine Regression

Prognostics of Induction Motor Shaft Based on Feature Importance and Least Square Support Vector Machine Regression

Tool remaining useful life prediction considering wear state based on hybrid attention network

Physics-Based Prognostics: Review and Future Directions

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