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Fault diagnosis using information systems and fuzzy reasoning

The aim of the CHEM project was to develop and implement advanced Decision Support Systems (DSS) for process monitoring, data and event analysis, and operation support in industrial processes. The systems are synergistic integration of innovative software tools, which improve the safety, product quality and operation reliability as well as reduce the economic losses due to faulty states, mainly in refining, chemical and petrochemical processes.

The CHEM applications consist of integrated sets of software toolboxes that provide robust detection and diagnosis of process problems in real-time. The systems assist operators in assessing process status and responding to abnormal events. The project provides a flexible architecture and a methodology in order to facilitate the development of such applications on many processes.

Toolbox "Fault diagnosis using information systems and fuzzy reasoning" primary task is real time assessment of residuals and isolation of faults in industrial processes. "Fault diagnosis using information systems and fuzzy reasoning" TB (toolbox) is suited for fault isolation in instrumentation, actuators and process components. This toolbox is particularly suited to be applied in the continuous processes such as in: chemical, petrochemical, pharmaceutical, food, power, metallurgical, and thermal industries. Algorithms applied in the toolbox are especially valuable for large-scale systems, when huge sets of residuals and faults have to be considered.

The fault isolation process is based on analysis of the detected symptoms set in respect to the relation between faults and symptoms defined by experts during TB configuration. The fault symptoms are calculated by assessment of residuals generated by other decision support system modules. The fault isolation procedure generates a diagnosis that points out a set of faults weighted by the faults’ certainty factors. Such a diagnosis more precisely specifies the system malfunction, comparing for example to the sequences of alarms generated by SCADA or DCS systems. Fast and precise diagnosis increases the process safety, decreases the pollution hazard and lowers the economical losses.

Fuzzy logic is applied for residual assessment and diagnostic reasoning, which makes possible, among others, to produce the uncertainty degrees of symptoms. Also, knowledge of symptoms’ dynamics may be fed optionally to the toolbox. This increases the fault isolability, protects against incorrect diagnosis, and makes the FDI process more robust.

The toolbox is divided into on-line calculation module and off-line configuration module. TB works under MS Windows 2000/XP. Residuals, inputs to on-line module, can be calculated by "Process modelling using fuzzy logic and neural networks for fault detection" toolbox. Toolboxes "Process modelling using fuzzy logic and neural networks for fault detection" and "Fault diagnosis using information systems and fuzzy reasoning" cooperate with each other in AMandD – Advance Monitoring and Diagnostic System.

The preliminary tests of the "Fault diagnosis using information systems and fuzzy reasoning" were conducted on: IDR Urea Synthesis Section of Urea Manufacturing Process in Nitrogen Factory "Pulawy" SA, Steam Generator Laboratory Stand in LAIL Universite Des Sciences et Technologies de Lille, Laboratory Stand for Diagnostic of Industrial Process at Warsaw University of Technology.

Procedures delivered in the software can help user to monitor and diagnose the state of his system. Its application can potentially bring following benefits:
- Reduction of costs caused by abnormal states of process;

- Increasing process safety;

- Reduction of environmental hazard;

- Increase comfort of working for plant operators by reduction the number of alarms;

- Decrease costs of planned repairs.

Reported by

Institute of Automatic Control and Robotics
A. Boboli 8
02-525 Warszawa
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