Applications of Fault Diagnosis and Failure Prognosis of Dynamic Systems

Dear Colleagues,

Fault diagnosis and failure prognosis concern several fields of application, such as transport, energy, aeronautics and production units. There are various causes for incipient faults in these applications, such as wear and tear, cracks in mechanical structures, and corrosion. If the onset of an incipient fault is not detected and maintenance actions are not taken into account in time, it may lead to a catastrophic failure. Once an incipient fault has been detected, a question is whether and for how long a faulty system can continue to operate with an accommodated control and an admissible reduced functionality if there is a limited number of redundant hardwares. Safety, reliability and availability require one to constantly monitor the health state of a dynamic system, to detect the onset of an incipient fault, to project the start of a potential failure into the future and to estimate the remaining operating time until a failure occurs. Predictive failure prognosis should take into account all kind of uncertainties, such as uncertainties in the monitored data, uncertainties in the choice of a degradation model and its parameters, or uncertainties with regard to appropriate failure alarm thresholds.

The predictive maintenance of dynamic systems and pro-active supervision strategies have gained significant importance in the industrial sector and have been increasingly addressed in academia in recent years. New and efficient techniques for the detection of fault events, their monitoring for health assessment and predictions are to be developed for safe and reliable operations. 

This Special Issue seeks original scientific contributions on novel methods/algorithms for the health monitoring (fault diagnosis and failure prognosis) of dynamic systems that may be of a theoretical and/or applied nature. Suitable topics for this Special Issue may include, but are not limited to: fault detection and system supervision.

• Health monitoring of multi-energy systems.
• Prognostics and prediction of remaining useful life (RUL).
• Model-based or hybrid, i.e., integrated model-based and data-based diagnostics and prognostics.
• Data-driven, model-based, or hybrid methods for industrial maintenance.
• Machine learning and data mining methods for system/component prognostics.
• Integrated diagnostics and prognostics architectures.
• Algorithms for health assessment and prognosis based on multiple sensor data.

Dr. Mohand Djeziri
Dr. Marc Bendahan
Guest Editors

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