Research, application, and challenges of causal inference in industrial fault diagnosis: A survey

Industrial fault diagnosis technologies leveraging convolutional neural networks and other advanced neural network architectures are pivotal for ensuring stable equipment operation, enhancing production efficiency, and minimizing maintenance costs. Nevertheless, these methods encounter inherent challenges due to data constraints and the complexity of production environments, particularly in identifying fault root causes and ensuring the interpretability of models. The integration of causal inference into industrial fault diagnosis offers significant promise for elucidating fault propagation pathways, revealing causal interrelations within complex systems, and advancing model interpretability. This survey presents a holistic review of research trajectories, pivotal technologies, and methodological advancements in causal inference for industrial fault diagnosis while systematically delineating the advantages and prospective challenges in this domain. First, this paper examines the limitations of conventional machine-learning approaches in fault diagnosis and traces the evolutionary trajectory of causal inference development in this context. Subsequently, the core theories and foundational technologies underpinning causal inference in industrial fault diagnosis are comprehensively discussed. Following this, the survey categorizes the existing literature according to different causal inferences to solve specific problems in industrial fault diagnosis and delves into detailed case studies, underscoring their utility in addressing distinct challenges. Finally, this survey synthesizes insights from existing literature to encapsulate the merits of causal inference in industrial fault diagnosis and to elucidate the prospective challenges it may encounter.