Feature Extraction and Ensemble Decision Tree Classifier in Plant Failure Detection

This paper describes a set of algorithms used to tackle the plant prognostic problem provided in the IEEE 2015 PHM Data Challenge. The task is to detect failure events by analyzing a dataset including sensor measurements and control reference signals of multiple plants without prior knowledge. There are two main difficulties lies in the data challenge. One is to identify which of the faults will occur. And the other is when the fault is going to happen. In this study, the authors tried to transform the task issue into a classification problem by three key steps including: 1) data cleansing and event time alignment; 2) feature extraction; 3) application of the ensemble decision tree classifiers. Results show that the proposed data-driven methods can effectively detect several types of the failure events, which may be promising in the real world plant prognostic applications.

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