Fast and accurate detection of faults is essential for the safe and cost-effective operation of chemical plants. Large scale process systems can potentially be affected by a wide variety of faults. An effective approach for detecting faults in such systems is to first divide or decompose the system's sensors into subsystems and to then implement a statistical monitoring method in each of the subsystems. The detection performance of such distributed multivariate statistical process monitoring methods depends strongly on the decomposition of the system's sensors into subsystems. In our previous work, we developed a simulation optimization method, called Performance Driven Agglomerative Clustering, which uses a greedy search strategy, based on clustering algorithms from graph theory, to find a decomposition of the system's sensors into subsystems for which the detection performance of a distributed monitoring method is near optimal. It is possible that the detection performance can be further improved by allowing a sensor to be part of multiple subsystems in the decomposition. User defined requirements may also place constraints on the decomposition of the sensors into subsystems. In this work, we propose the Extended Performance Driven Agglomerative Clustering method which allows to incorporate constraints and sensors to be allocated to multiple subsystems. To demonstrate its effectiveness, the proposed method is applied to the benchmark Tennessee Eastman Process.
Bibliographical noteFunding Information:
Partial financial support from NSF-CBET is gratefully acknowledged.
- Agglomerative clustering
- Distributed fault detection
- Principal Component Analysis
- Simulation optimization
- Statistical process monitoring
- System decomposition