Fault detection and diagnosis (FDD) modules in a modern control system are effective in detecting and identifying abnormal process behaviours in a timely manner, ensuring the high-performance of large-scale engineering systems. The detection and isolation of faults is essentially built on the characterisation of the observed behaviour of a system. However, due to the large number of technical indicators available for measurement, as well as the various constraints of sensor installation, monitoring all the operating parameters of a large-scale engineering system is not feasible. Therefore, locating sensors optimally in a large-scale system, to achieve a comprehensive description of an abnormality, becomes a key issue to successfully apply diagnostic technologies to real world situations. In this paper, a fault feature reduction (FFR) based sensor location approach is proposed for optimal sensor placement so as to achieve the desired performance of fault detection and isolation. The behaviour of faults is firstly analysed using a fault tree to obtain a comprehensive understanding of the multi-dimensional relationships between faults and symptoms. A Boolean matrix is then constructed to represent the corresponding relations around faults and potential sensors. All the alternative configurations of sensors, for a desired diagnosis of a system, are obtained by eliminating the redundant fault features. The trade-off without a certain sensor is also attained using the following proposed approach. Three large-scale systems, including, a diesel engine system and two chemical systems, are used to illustrate the proposed approach. Comparisons to existing competitive techniques indicate the enhanced abilities of the proposed approach to meet the varying requirements of a real-world monitoring network. The analysis of sensor placement can be performed at the design phase of a large-scale engineering system, to locate the preset measured hole, or, during the life-cycle, to perfect an incomplete or redundant monitoring system.
|Journal||Journal of the Franklin Institute|
|Publication status||Accepted/In press - 19 May 2020|
Wang , J., Wang, Z., Ma, X., Smith, A., Gu, F., Zhang, C., & Ball, A. (Accepted/In press). Locating Sensors in Large-Scale Engineering Systems for Fault Isolation Based on Fault Feature Reduction. Journal of the Franklin Institute.