Operations & Maintenance
Performance monitoring and fault diagnosis of industrial processes and machines
Monitoring the performance of an industrial process or of a machine is usually of great interest to the owner, since maximizing performance usually maximizes also quality and income. Different types of performance measures can be constructed. Their basic use is in monitoring, but this can be extended to condition monitoring and fault diagnosis in order to find the causes of poor performance as well as components that need maintenance.
Fault detection and condition monitoring methods are usually divided into three main categories: model-based, knowledge-based and data-driven systems. In model-based methods a mathematical model (often a differential equation system) is built for the monitored system from physical principles and the predictions given by this model are compared with the actual measurements from the system to detect faults. The main limitation with this approach is that many systems are too complex to allow any sufficiently accurate model to be built. Often fault situations are handled by ad-hoc procedures developed by human experts. Knowledge-based methods (e.g. expert systems, fuzzy logic) try to automate the use of this knowledge. The problem with these methods is that it is laborious to gather the knowledge from the experts and to maintain the database as products evolve. The approach used in this work is data-driven. Data-driven methods try to deduce the properties of the system more or less directly from the available measurement data, which are usually readily available in modern industrial plants.
The schematic above represents a complete process monitoring loop. During normal operation a fault detection system monitors the process for possible faults. If a fault is detected, the affected measurement signals are isolated in fault identification. Then the exact location, magnitude, and time of the fault are found out during the fault diagnosis phase. The corrective actions that return the system to its normal operating state are then performed (system recovery).
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