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Reliability analysis of systems with discrete event data using association rules
Quality and Reliability Engineering International ( IF 2.2 ) Pub Date : 2021-07-10 , DOI: 10.1002/qre.2942
Kangzhe He 1 , Bin Liu 2 , Min Xie 1, 3 , Phuc Do 4 , Benoit Iung 4 , Way Kuo 1, 3
Affiliation  

With the popularization of big data, an increasing number of discrete event data have been collected and recorded during system operations. These events are usually stored in the form of event logs, which contain rich information of system operations and have potential applications in fault diagnosis and failure prediction. In manufacturing processes, various levels of correlations exist among the events, which can be used to predict the occurrence of failure events. However, two challenges remain to be solved for effective reliability analysis and failure prediction: (1) how to leverage various information from the event log to predict the occurrence of failure events and (2) how to model the effects of multiple correlations on the prediction. To address these issues, this paper proposes a novel reliability model, which integrates Cox proportional hazards (PHs) regression into survival analysis and association rule mining methodology. The model is used to evaluate the probability of failure event, which occurs within a certain period of time conditional on the occurrence history of correlated events. To estimate parameters and predict occurrence of failure events in the model, an effective algorithm is proposed based on piecewise-constant time axis division, Cox PHs model, and maximum likelihood estimation. Unlike the existing literature, our model focuses on the interactions among events. The applicability of the proposed model is illustrated through a case study of a manufacturing company. Sensitivity analysis is conducted to illustrate the effectiveness of the proposed model.

中文翻译:

基于关联规则的离散事件数据系统可靠性分析

随着大数据的普及,越来越多的离散事件数据在系统运行过程中被收集和记录。这些事件通常以事件日志的形式存储,其中包含丰富的系统运行信息,在故障诊断和故障预测方面具有潜在的应用价值。在制造过程中,事件之间存在各种级别的相关性,可以用来预测故障事件的发生。然而,有效的可靠性分析和故障预测仍有两个挑战需要解决:(1) 如何利用事件日志中的各种信息来预测故障事件的发生,以及 (2) 如何对多个相关性对预测的影响进行建模. 为了解决这些问题,本文提出了一种新颖的可靠性模型,它将 Cox 比例风险 (PHs) 回归集成到生存分析和关联规则挖掘方法中。该模型用于评估故障事件发生的概率,故障事件以相关事件的发生历史为条件,在一定时间内发生。为了估计模型中的参数并预测故障事件的发生,提出了一种基于分段常数时间轴划分、Cox PHs模型和最大似然估计的有效算法。与现有文献不同,我们的模型侧重于事件之间的相互作用。通过对一家制造公司的案例研究说明了所提出模型的适用性。进行敏感性分析以说明所提出模型的有效性。
更新日期:2021-07-10
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