These kinds of applications are adequate for estimation, monitoring, fault detection and diagnosis in distributed parameter systems.
This paper proposes a systematic approach to fault diagnosis of FMSs that integrates condition monitoring, fault diagnosis and maintenance planning.
An adaptive clustering procedure specifically designed for process monitoring, fault detection and isolation is presented in this paper.
PHM can be seen, as the process involves system monitoring, fault detection, isolation, and identification (fault diagnostics), failure prognostics, and action taken (e.g., required logistics, maintenance,... etc).
The strategy for monitoring (fault detection) of the system components, as a part of the design for fault tolerance, is also described in this paper.
To support large-scale data processing, we design a multi-factor real-time monitoring fault tolerance (MRMFT) model based on a GPU cluster.
Calibrated energy models can be used for several specific tasks in the operation phase, in particular condition monitoring, fault detection and diagnosis, supervisory control and energy management.
In the context of chemical process plants, these tasks can be categorised as data acquisition, regulatory control, data reconciliation, process monitoring, fault diagnosis, supervisory control scheduling and planning.
Continuous monitoring, fault diagnosis and predictive maintenance, is a crucial task to reduce the degree of damage and stopping time for a rotating machine.
The range of application is quite diverse and covers modelling, simulation, condition monitoring, fault detection and control strategy design and implementation.
