A Review of Fault Diagnosis of Model-based Mechatronic Systems
TL;DR
This comprehensive review systematically categorizes these methodologies into qualitative approaches—including abstraction hierarchies, fault tree models, signed directed graphs, and fuzzy logic formulations—and quantitative mathematical techniques, encompassing analytical redundancy, parity spaces, Kalman filtering frameworks, parametric estimation algorithms, and diagnostic observer topologies.
Abstract
Fault diagnosis is a critical discipline within modern engineering that ensures the operational reliability, structural safety, and maximum availability of complex mechatronic systems. As contemporary automated processes become deeply integrated with mechanical, electrical, and computational sub-units, tracking internal health status transitions from an operational preference to a fundamental necessity. This paper provides an exhaustive, large-scale review of fault diagnosis methodologies, focusing explicitly on the architecture of model-based systems. Model-based fault diagnosis leverages high-fidelity mathematical and topological representations of physical processes to detect, isolate, and identify faults by evaluating analytical discrepancies, or residuals, between actual system responses and anticipated model states. This comprehensive review systematically categorizes these methodologies into qualitative approaches—including abstraction hierarchies, fault tree models, signed directed graphs, and fuzzy logic formulations—and quantitative mathematical techniques, encompassing analytical redundancy, parity spaces, Kalman filtering frameworks, parametric estimation algorithms, and diagnostic observer topologies. Furthermore, complementary architectural considerations, such as hardware redundancy and history-based data analytics, are investigated to build an integrated view of modern industrial developments. A detailed taxonomic synthesis highlights current system implementation hurdles, trade-offs between mathematical precision and computational complexity, and future diagnostic research paradigms for safety-critical systems.