Distribution-agnostic Probabilistic Few-shot Learning for Multimodal Recognition and Prediction

Area of Science:

• Prognostics and Health Management (PHM)
• Machine Learning
• Reliability Engineering

Background:

• Industrial systems require accurate failure mode recognition and Remaining Useful Lifetime (RUL) prediction for effective Prognostics and Health Management (PHM).
• Insufficient sensor data is a common challenge in industrial settings, hindering traditional machine learning approaches.
• Existing Few-Shot Learning (FSL) methods often treat failure mode recognition and RUL prediction as separate problems, failing to account for their inherent interdependence.

Purpose of the Study:

• To develop a novel distribution-agnostic probabilistic FSL method that jointly addresses failure mode recognition and RUL prediction.
• To capture the complex dependence between different failure modes and their impact on the RUL of operating units.
• To enhance the accuracy and robustness of PHM in industrial scenarios with limited sensor data.

Main Methods:

• Proposed a neural network with prototypes to integrate few-shot classification and regression for multimodal recognition and prediction.
• Developed multimodal Bayesian model-agnostic meta-learning (MBMAML) to probabilistically model failure modes and RUL under data scarcity.
• Constructed a loss function based on probabilistic modeling to train the model, capturing the interaction between failure modes and RUL.

Main Results:

• The proposed model adaptively learns approximate distributions of failure modes and RUL for new operating units.
• Demonstrated effective performance in a case study involving the degradation of aircraft gas turbine engines.
• The method successfully addresses the challenge of limited sensor data by jointly learning failure modes and RUL.

Conclusions:

• The developed probabilistic FSL method offers a robust solution for joint failure mode recognition and RUL prediction in data-scarce industrial environments.
• MBMAML provides a powerful framework for capturing uncertainty and interdependencies crucial for accurate PHM.
• The approach is adaptable and shows promise for practical applications in various industrial settings.