An Adaptive Particle Filtering-based Framework for Real-time Fault Diagnosis and Failure Prognosis of Environmental Control Systems

Maintenance of critical or/complex systems has recently moved from traditional preventive maintenance to Condition Based Maintenance (CBM) exploiting the advances both in hardware (sensors / DAQ cards, etc.) and in software (sophisticated algorithms blending together the state of the art in signal processing and pattern analysis). Along this path, Environmental Control Systems and other critical systems/processes can be improved based on concepts of anomaly detection, fault diagnosis and failure prognosis. The enabling technologies borrow from the fields of modeling, data processing, Bayesian estimation theory and in particular a technique called particle filtering. The efficiency of the diagnostic approach is demonstrated via simulation results.

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