A comprehensive study on causal discovery between degradation paths

2026-04-12

AI Summary

This study addresses the critical challenge of identifying causal dependencies among multi-parameter degradation paths in complex systems, which is essential for accurate modeling and control. The work proposes a novel causal discovery strategy based on degradation increments and systematically evaluates the applicability of five classes of non-temporal methods—constraint-based, score-based, functional causal models, gradient-based, and ordering-based approaches—in degradation analysis. Through validation using Wiener process simulations and real-world engineering data, the results demonstrate that methods leveraging degradation increments significantly outperform those using raw data directly. Among them, the Stable PC and GES algorithms consistently exhibit robustness and high accuracy in both numerical experiments and practical case studies, making them recommended choices for uncovering causal relationships among degradation trajectories.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Existing studies indicate that complex system degradation is characterized by degradation of multiple dependent parameters. Capturing the dependencies is crucial for accurate degradation modeling and effective degradation control. This work aims to uncover these dependencies through causal analysis, focusing on pairwise causal discovery. Firstly, considering the steady-state characteristic of physical dependencies between parameters, a causal discovery strategy using degradation increments is proposed combined with non-temporal causal discovery techniques. Then, five types of non-temporal causal discovery techniques, including constraint-based, score-based, functional causal model-based, gradient-based and the emerging ordering-based technique, are selected as benchmark methods to identify the most suitable approach. Numerical studies based on Wiener process are first conducted to investigate the method effectiveness on both independent and causally dependent degradation paths. Additionally, sensitivity analysis is performed to evaluate how degradation process characteristics affect the accuracy of causal discovery. Then, two engineering applications are given to show the practical applicability of the approach, including a second-order multiple-feedback band pass filter and a turbofan engine. Our findings indicate that the proposed strategy, which uses degradation increments, outperforms methods that rely on raw degradation data. Among all evaluated techniques, stable Peter-Clark and greedy equivalence search exhibit robust and accurate performance across both numerical and engineering cases, which are recommended for causal discovery between degradation paths. The code is available on GitHub: https://github.com/dirge1/causal_deg_data.

Problem

• causal discovery
• degradation paths
• dependency modeling
• system degradation
• causal analysis

Innovation

• causal discovery
• degradation paths
• degradation increments
• non-temporal causal methods
• system reliability