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A nonlinear probabilistic method and contribution analysis for machine condition monitoring

被引：43
作者：
Yu, Jianbo [1 ]
机构：
[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200072, Peoples R China
来源：
MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2013年 / 37卷 / 1-2期
基金：
美国国家科学基金会; 高等学校博士学科点专项科研基金;
关键词：
Condition-based maintenance; Machine health monitoring; Generative topographic mapping; Contribution analysis; Feature selection; FEATURE-SELECTION SCHEME; SELF-ORGANIZING MAP; WAVELET TRANSFORM; BALL-BEARING; PREDICTION; PROGNOSIS; MODEL;
D O I：
10.1016/j.ymssp.2013.01.010
中图分类号：
TH [机械、仪表工业];
学科分类号：
120111 [工业工程];
摘要：
Health degradation assessment from normal to failure condition of machine part or system is a key element in condition-based maintenance (CBM) system. This paper proposes a generative topographic mapping (GTM) and contribution analysis-based method to perform machine health degradation assessment and monitoring. GTM-based negative likelihood probability (NLLP) is developed to offer a comprehensible indication for quantifying machine health states. A Bayesian-inference-based probability (BIP) calculation method is further developed to analyze the failure probability of the monitored machine or component. A variable replacing-based contribution analysis method is developed to discover potential features that are effective for the detection and assessment of machine health degradation in its whole life. The experimental results on a turbine engine simulation system and a bearing testbed illustrate plausibility and effectiveness of the proposed methods. (C) 2013 Elsevier Ltd. All rights reserved.
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页码：293 / 314
页数：22
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