A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes

作者：Chehade, Abdallah; Song, Changyue; Liu, Kaibo; Saxena, Abhinav; Zhang, Xi^*

来源：Journal of Quality Technology, 2018, 50(2): 150-165.

DOI：10.1080/00224065.2018.1436829

• big data
• condition monitoring
• data fusion
• health index
• multiple sensors

Operating units, in practice, often suffer from multiple modes of failure, and each failure mode has a distinct influence on the service life cycle path of a unit. The rapid development of sensor and communication technologies has enabled multiple sensors to simultaneously monitor and track the health status of a unit in real time. However, one challenging question that remains to be resolved is how to leverage data from multiple sensors for better degradation modeling and prognostic analysis, especially when there are multiple failure modes. Currently, many of the existing approaches in prognostics either (a) fail to capture the dependency between sensors and instead focus on analyzing each sensor independently or (b) fail to incorporate the failure-mode diagnosis for better degradation modeling and prognostics during condition monitoring. To address the limitations in the existing literature, we propose a data-level fusion methodology to construct a composite failure-mode index, named FM-INDEX, via the fusion of multiple sensor data. Our goal is to utilize the FM-INDEX to better characterize the failure mode of an operating unit in real time, thus leading to better degradation modeling and prognostic analysis. A case study that involves the degradation data set of an aircraft gas turbine engine with two potential failure modes is conducted to numerically evaluate the performance of our proposed method compared to other techniques in the related literature.

• 出版日期2018
• 单位北京大学