Online probabilistic operational safety assessment of multi-mode engineering systems using Bayesian methods

被引:21
作者
Lin, Yufei [1 ]
Chen, Maoyin [1 ]
Zhou, Donghua [1 ]
机构
[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
关键词
Operational safety assessment; Multi-mode engineering systems; Bayesian method; Sample dependency; GAUSSIAN MIXTURE MODEL; IDENTIFICATION; NETWORK;
D O I
10.1016/j.ress.2013.05.018
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In the past decades, engineering systems become more and more complex, and generally work at different operational modes. Since incipient fault can lead to dangerous accidents, it is crucial to develop strategies for online operational safety assessment. However, the existing online assessment methods for multi-mode engineering systems commonly assume that samples are independent, which do not hold for practical cases. This paper proposes a probabilistic framework of online operational safety assessment of multi-mode engineering systems with sample dependency. To begin with, a Gaussian mixture model (GMM) is used to characterize multiple operating modes. Then, based on the definition of safety index (SI), the SI for one single mode is calculated. At last, the Bayesian method is presented to calculate the posterior probabilities belonging to each operating mode with sample dependency. The proposed assessment strategy is applied in two examples: one is the aircraft gas turbine, another is an industrial dryer. Both examples illustrate the efficiency of the proposed method. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:150 / 157
页数:8
相关论文
共 29 条
[1]  
[Anonymous], 2007215026 NASAARL T
[2]  
[Anonymous], 2003, Multi-state system reliability: Assessment, optimization and applications
[3]  
[Anonymous], RELIABILITY ENG SYST
[4]  
[Anonymous], THESIS KTH SCH ELECT
[5]  
[Anonymous], 1970, OPTIMAL STAT DECISIO
[6]  
[Anonymous], RELIABILITY ENG SYST
[7]  
[Anonymous], P INT C MECH AUT
[8]   Recursive operability analysis of a pilot plant gasifier [J].
Arena, U. ;
Romeo, E. ;
Mastellone, M. L. .
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, 2008, 21 (01) :50-65
[9]  
Bishop CM., 1995, NEURAL NETWORKS PATT
[10]   Using Bayesian network for fault location on distribution feeder [J].
Chien, CF ;
Chen, SL ;
Lin, YS .
IEEE TRANSACTIONS ON POWER DELIVERY, 2002, 17 (03) :785-793