A Bayesian Spike-and-Slab Sensor Selection Approach for High-Dimensional Prognostics

With recent advances in sensor technology, more and more sensors are being used to simultaneously monitor the degradation of a system. As the number of sensors increases, it becomes increasingly difficult to distinguish informative sensors from uninformative sensors when performing prognostics, especially under the presence of different sensor correlations, signal-to-noise ratios, measurement units, and data characteristics. Existing methods for sensor selection typically rely on penalized-likelihood methods, which are known to provide biased estimates and poor sensor selection results in such high-dimensional settings. To overcome this challenge, we propose a novel data-fusion method that simultaneously selects informative sensors using Bayesian spike-and-slab priors and fuses the informative sensors into a 1-D health index (HI) to better characterize the degradation process for further prognostic analysis. Compared to the existing literature, the proposed Bayesian spike-and-slab sensor selection approach provides several unique advantages: 1) superior sensor selection performance in high-dimensional scenarios; 2) consistent sensor selection results with correlated sensors; 3) guaranteeing weak and strong selection consistency under mild assumptions; and 4) higher RUL prediction accuracy in a wide range of simulation and case studies. Note to Practitioners—This paper is motivated by the practical challenge of selecting informative sensors from high-dimensional multisensor systems (i.e., when there are many sensors relative to the number of training units) when conducting prognostics. Informative sensors not only provide valuable insights on the system’s degradation process, but also can be used to construct degradation indicators for practitioners to monitor and interpret the system status. In order to select informative sensors, this paper proposes a novel approach involving Bayesian spike-and-slab priors. After we select the informative sensors, they are then fused into a 1-D HI to better characterize the degradation process. This approach is particularly useful for selecting informative sensors under high-dimensional scenarios with possibly correlated sensors, when all units degrade under a single failure mode and operating condition.