Life Predictions of Brake Friction Pair Based on Physical Models and Statistical Analysis

Author(s): Wang Yao, Jiusheng Bao*, Yan Yin, Tonggang Liu, Ning Wang

Journal Name: Recent Patents on Mechanical Engineering

Volume 11 , Issue 1 , 2018


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Abstract:

Background: Friction brake is the most important safety device of mechanical systems. In order to avoid accidents caused by braking failure, it is highly important to forecast the remaining useful life of brakes accurately.

Objective: The purpose of this study is to provide an overview about life prediction methods of brake friction pair based on physical models and statistical analysis.

Methods: In this paper, the widely used life prediction methods of brake friction pair based on physical model and statistical analysis are summarized. To be specific, the fatigue life of brake disc/drum and the wear life of brake pad are analyzed in depth based on their physical models. Meanwhile, three life prediction methods based on statistical analysis which are linear regression, grey prediction and neural network, are discussed.

Results: Life predictions based on the physical model often ignore random, mutation and nonlinear factors in building failure model, while life predictions based on statistical analysis don’t need to explore the detailed failure mechanism. Curve fitting, grey prediction and neural network are mainly used in life prediction based on the statistical analysis.

Conclusion: Data mining technology such as neural network plays a more important role as a result of its comprehensive consideration of braking conditions and braking frequency, and studies on real-time monitoring system for life predictions have practical significance for forecasting the catastrophic failure timely.

Keywords: Brake friction pair, fatigue life, life prediction, neural network, physical model, statistical analysis, wear life.

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Article Details

VOLUME: 11
ISSUE: 1
Year: 2018
Published on: 18 April, 2018
Page: [58 - 66]
Pages: 9
DOI: 10.2174/2212797611666180216160021
Price: $25

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