Optimize the Signal Quality of the Composite Health Index via Data Fusion for Degradation Modeling and Prognostic Analysis

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 methodologies that integrate the data from multiple sensors provide an essential tool for degradation modeling and prognostics. To achieve this goal, a fundamental question needs to be answered first is how to measure the signal quality of a degradation signal. If such a question can be addressed, then the data fusion approach can be simplified as a mission-specific task: to construct a composite health index with the goal of optimizing its signal quality. In this paper, a new signal-to-noise ratio (SNR) metric that is tailored to the needs of degradation signals is proposed. Then, based on the new quality metric, we develop a data-level fusion model to construct a health index via fusion of multiple degradation-based sensor data. Our goal is that the developed health index provides a much better characterization of the health condition of the unit and thus leads to a better prediction of the remaining lifetime. A case study that involves the degradation dataset of aircraft gas turbine engines is conducted to numerically evaluate the performance of the developed health index regarding prognostics and further compare the result with existing literature. Note to Practitioners-This paper aims at addressing the challenges of performing prognostics when multiple sensors are simultaneously monitoring the health status of a degraded unit. Specifically, the paper starts from proposing a new SNR metric that numerically measures the signal quality of a degradation signal. Then, a novel data fusion methodology that combines the information from multiple degradation signals is developed to construct a composite health index with the goal of maximizing the proposed SNR metric. The developed method is greatly beneficial when the degraded unit results from a single failure mode and operates under a single environmental condition. There are several steps involved when implementing the method: 1) collecting the historical degradation signals from multiple sensors; 2) characterizing the degradation patterns of these signals; 3) deriving the optimal weights with the goal of maximizing the proposed SNR metric by using the eigendecomposition approach; and 4) constructing the health index that combines multiple sensor data based on the derived optimal weights.