Distribution-Agnostic Probabilistic Few-Shot Learning for Multimodal Recognition and Prediction

In industrial scenarios with insufficient sensor data, intelligent few-shot failure mode recognition and remaining useful lifetime (RUL) prediction are critically essential for effective prognostics and health management. Existing few-shot learning (FSL) methods focus on either the failure mode recognition as a classification problem or the RUL prediction as a regression problem, failing to capture the dependence between failure modes and RUL given that units under different failure modes present distinct degradation characteristics. To address the issue, this paper proposes a distribution-agnostic probabilistic FSL method for multimodal recognition and prediction of operating units. The proposed model establishes a neural network with prototypes to solve a few-shot classification-and regression-integrated problem. To fully capture the uncertainty caused by limited sensor data, we develop multimodal Bayesian model-agnostic meta-learning (MBMAML) for the probabilistic modeling of failure modes and the RUL under multiple failure modes. We construct the loss function based on probabilistic modeling that captures the interaction between failure modes and RUL for model training. Finally, the proposed model adaptively learns the approximate distributions of failure modes and RUL for a new operating unit. We evaluate the proposed model performance through a case study on the degradation of aircraft gas turbine engines. Note to Practitioners-Failure mode recognition and RUL prediction are essential in prognostics health management (PHM) to avoid unexpected failures of units in industrial systems, such as aircraft gas turbine engines. However, insufficient sensor data are quite common issue in industrial scenarios due to expensive sensor deployment, the difficulty of installing sensors to certain special mechanical equipment, and so on. This paper aims to develop a FSL method to jointly recognize the failure mode and predict the RUL of a unit based on insufficient sensor data. The four steps to implement the proposed method in practice are as follows: First, collect sensor signal data, RUL data, and failure mode data of units. Second, construct the model framework via the proposed MBMAML. Third, formulate the loss function based on the probability distributions of failure modes and RUL, and train the model using collected data. Fourth, adaptively recognize the failure mode and predict the RUL of a new operating unit. The proposed method is expected to be applicable to many practical few-shot industrial scenarios due to its data-driven neural network with flexible model structure.