A causal knowledge discovery framework as an enabler for equipment prescriptive maintenance

Robust and safe industrial operations emphasize asset criticality, reliability and availability through effective maintenance which minimizes failures and costly overhauls. Existing maintenance strategies often fall short of interpreting the underlying mechanisms driving equipment degradation patterns. Prescriptive maintenance advances beyond failure prediction, generating tailored, granular and actionable strategies as knowledge-driven decision supports. Causality discovery is crucial in prescriptive maintenance through investigating root degradation causes and failure propagation, enabling evidence-grounded proactive interventions. However, systematic methodologies for uncovering and quantifying causality driving sensor data dependencies, temporal degradation progression and early warning indicator relationships remain underdeveloped. This paper presents a new causal discovery framework to facilitate prescriptive maintenance. It applies: (1) Transfer Entropy to identify features exhibiting temporal causal influence on degradation, (2) Dynamic Time Warping-based clustering to form “symptom groups” representing shared degradation behaviors, and (3) a deep learning architecture revealing intra-cluster causal relationships and temporal delays. Case studies demonstrate framework utility of transforming multimodal truck sensor data into explainable causal knowledge. Results indicate failure-prone interconnections on individual trucks and fleet-wide diagnostics where key triggers and their evolving roles across different degradation stages are presented. Optimized with expert calibration, this work delivers a prescriptive decision-support foundation to enhance equipment safety and promote operation sustainability.