Chaos-Based Fault Diagnosis: Methodologies and Practices

Fault Diagnosis (FD) is vital for industrial systems’ safety and reliability. While traditional approaches — Model-Based (MB), Signal-Based (SB), and Data-Driven (DD) methods — have made significant progress, they face inherent limitations with increasingly complex systems. MB methods struggle with precise mathematical modeling of nonlinear systems, SB techniques falter when fault signatures are obscured by noise, and DD methods face challenges in data quality, model generalization, and real-time constraints. Chaos theory, which addresses these limitations utilizing its special features of deterministic nonlinearity, sensitiveness to initial conditions, and pseudo-randomness, has emerged as a transformative paradigm in FD. Chaotic systems enhance FD methods by enabling complex system modeling, sensitivity to initial conditions improves SB methods by enhancing signal-to-noise ratios, and pseudo-random trajectories boost global optimization capabilities in DD techniques. Each approach is evaluated through theoretical analysis, implementation frameworks, and industrial applications, supported by representative case studies across various equipment domains.