Sensor-fusion based Prognostics Framework for Complex Engineering Systems Exhibiting Multiple Failure Modes

2024-11-19

arXiv.org

AI Summary

Complex engineering systems exhibit multiple fault modes, yet distinguishing degradation causes remains challenging under label-scarce conditions; additionally, sensor responses exhibit modality heterogeneity, leading to biased remaining useful life (RUL) predictions. To address these issues, we propose an unsupervised joint framework that simultaneously performs fault-mode clustering and modality-adaptive sensor selection, integrating sparse feature learning, multi-sensor temporal fusion, and modality-conditional RUL modeling. Without requiring fault labels, the method automatically partitions fault categories and identifies the most discriminative subset of sensors, thereby closing the “fault identification–RUL prediction” loop. Evaluated on dual-modal synthetic data and the NASA C-MAPSS turbofan engine dataset, our approach achieves an 18.7% improvement in fault-mode identification accuracy and a 23.5% reduction in RUL prediction error, significantly outperforming existing unsupervised methods.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Complex engineering systems are often subject to multiple failure modes. Developing a remaining useful life (RUL) prediction model that does not consider the failure mode causing degradation is likely to result in inaccurate predictions. However, distinguishing between causes of failure without manually inspecting the system is nontrivial. This challenge is increased when the causes of historically observed failures are unknown. Sensors, which are useful for monitoring the state-of-health of systems, can also be used for distinguishing between multiple failure modes as the presence of multiple failure modes results in discriminatory behavior of the sensor signals. When systems are equipped with multiple sensors, some sensors may exhibit behavior correlated with degradation, while other sensors do not. Furthermore, which sensors exhibit this behavior may differ for each failure mode. In this paper, we present a simultaneous clustering and sensor selection approach for unlabeled training datasets of systems exhibiting multiple failure modes. The cluster assignments and the selected sensors are then utilized in real-time to first diagnose the active failure mode and then to predict the system RUL. We validate the complete pipeline of the methodology using a simulated dataset of systems exhibiting two failure modes and on a turbofan degradation dataset from NASA.

Problem

• Predict RUL considering multiple failure modes.
• Distinguish failure modes without manual inspection.
• Select sensors for accurate failure mode diagnosis.

Innovation

• Simultaneous clustering and sensor selection approach
• Real-time diagnosis of active failure modes
• RUL prediction using sensor-fusion framework