IMAACA 2018 Call For Papers
Diagnosis and Prognosis of Dynamic Systems
Track Description
There are various causes for incipient faults in industrial plants as well as in mobile autonomous systems such as tear and wear, cracks in mechanical structures, corrosion. If the onset of incipient faults is not detected and maintenance actions are not taken in good time, they may lead to catastrophic failures. 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 hardware. Safety, reliability and availability require 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 the failure would occur. Predictive failure prognosis has to 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.
Predictive maintenance of dynamic systems and pro-active supervision strategies have gained paramount importance in the industrial sector and have become popular within academia in recent years. New and efficient techniques for detection of fault events, their monitoring for health assessment and predictions are being sought for safe and reliable operations.
This invited session is seeking original scientific contributions on novel methods/algorithms for health monitoring (diagnosis and prognosis) of dynamic systems with theoretical and/or applied nature. Suitable topics for this special session may include, but are not limited to:
- Fault detection and system supervision.
- Health monitoring of multi-energetic systems.
- Prognostics and prediction of Remaining Useful Life (RUL).
- Model-based or hybrid Integrated diagnostics and prognostics.
- Data-driven, hybrid, or model based 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 sensors data.
We look forward to receiving your paper(s) and to welcoming you at the IMAACA Conference.
For further information please contact Wolfgang BORUTZKY or Mayank Shekhar JHA
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