Testing and comparing the application of new techniques to condition based maintenance

本研究的目的是介绍和测试基于状态监测的机器健康监测、诊断和预测的新技术，比较这些技术的性能，并相应地优化维护行动。第一种技术是比例风险模型（PHM）和部分可观察马尔可夫决策模型（POMDM）的结合，这两个模型在文献中经常提到，但它们是分开使用的。第二种技术是基于统计过程控制（SPC）图表，即Hotelling的T2。第三种技术使用支持向量机（SVM）模型，该模型最初是为解决模式识别问题而开发的。第四种技术应用数据逻辑分析（LAD），这是一种基于组合学和优化的数据分析方法。这些技术分别代表了基于建模退化、离群值检测、回归分析和组分类的四种不同的CBM方法。

The objective of the proposed research is to introduce and test new techniques for monitoring, diagnosis and prognosis of machine health based on condition monitoring, to compare the performance of those techniques, and to optimize maintenance actions accordingly. The first technique is a combination of the Proportional Hazards Model (PHM) and a Partially Observable Markov Decision Model (POMDM), two models which are frequently mentioned in the literature, but which are used separately. The second technique is based on a Statistical Process Control (SPC) chart, the Hotelling's T2 . The third technique uses the Support Vector Machines (SVM) model, which was originally developed for solving pattern recognition problems. The fourth technique applies the Logical Analysis of Data (LAD), a combinatorics and optimization-based data analysis method.These techniques represent four different approaches to CBM based on modeling degradation, outlier detection, regression analysis, and group classification, respectively.