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Abstract |
Due to the rapid development of sensing and computing technologies, multiple sensors have been widely used in a system to simultaneously monitor the health status of an operating unit. Such a data-rich environment creates an unprecedented opportunity to better understand the degradation behavior of the system and make accurate inferences about the remaining lifetime.Since data collected from multiple sensors are often correlated and each sensor data contains only partial information about the degraded unit, data fusion has provided an essential tool for service decision making. This talk will provide an overview of the recent advancement regarding this topic,with a particular focus on the generic data-driven approaches to constructing an effective health index that combines multiple and heterogeneous sensor datato better characterize the health condition of units. The health index can then be used to support smart service decisions, which will lead to: (i) closer monitoring of a unit’s health status; (ii) quicker fault diagnosis; (iii) more accurate forecast of a unit’s remaining lifetime; and (iv) proactive maintenance and control decisions better aligned to future conditions and performance. The proposed methods are tested and validated through the degradation datasets of aircraft gasturbine engines and other complex systems. |
