Quantitative description of sensor data monotonic trend for system degradation condition monitoring

Condition monitoring is an effective tool for diagnosing and predicting the system fault or failure. One class of method in system condition monitoring is based on the condition data (i.e., data-driven methodology). However, not all the collected condition data can be utilized for the data-driven methodology. Hence, the selection of reasonable condition data is crucial for the application of the data-driven methodology. This is especially useful for the system which has the characteristics of degradation. In such system, the condition data that have the increasing or decreasing trend are desirable. This article provides a combination of entropy and improved permutation entropy to select the condition data based on quantitative description of sensor data monotonic trend. A case study of the aircraft engine is carried out to validate the effectiveness of the quantitative description of sensor data monotonic trend. The detailed experiments prove the advantage of the proposed approach.