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A Generic Health Index Approach for Multisensor Degradation Modeling and Sensor Selection

With recent development in sensor technology, multiple sensors have been widely adopted to monitor the degradation of a single unit simultaneously. The challenge of multisensor degradation modeling lies in that the sensor signals are often correlated and may contain only partial or even no information on the degradation status of a unit. To address these issues, this paper proposes a novel data fusion method that constructs a 1-D health index (HI) via automatically selecting and combining multiple sensor signals to better characterize the degradation process. In particular, this paper develops a new latent linear model that constructs the HI and selects informative sensors in a unified manner. Compared to the existing literature, the proposed method enjoys several unique advantages: 1) being able to derive the best linear unbiased estimator of the fusion coefficients; 2) offering high computational efficiency; 3) not requiring to know the exact value of the failure threshold; and 4) exhibiting general applicability in practice by not imposing restrictive assumptions on the degradation process. Simulation studies are presented to illustrate the effectiveness and evaluate the sensitivity of the proposed method. A case study on the degradation of aircraft gas turbine engines is also performed which shows a better prognostic performance of the proposed method compared with existing approaches. Note to Practitioners—This paper is motivated by the practical issue of degradation modeling and prognostics when multiple sensors simultaneously monitor the degradation status of a unit. Specifically, there are two fundamental questions involved, including: 1) how to screen out noninformative sensors and 2) how to properly combine the information from the selected sensor signals to accurately estimate the underlying degradation status of the unit. The novelty of this paper lies in developing an innovative latent model that tackles these two challenging questions in an integrated manner. There are four main steps involved when implementing the proposed method: 1) collecting multiple sensor signals and failure time of historical units; 2) selecting the informative sensors and deriving the optimal weight for each selected sensor; 3) constructing the health indices (HIs) of in-service units; and 4) predicting the remaining useful life of the in-service units using the constructed HIs. The proposed method is very useful when the degradation is under a single failure mode in a single environmental condition. In the future research, we will study the extension of the proposed model when sensor signals have a nonlinear relationship, as well as when the degradation process is under more complex scenarios such as multiple failure modes and multiple operation conditions.