Due to aging and environmental factors, system components may either fail or not function as expected, which causes unprecedented changes in the quality of the system. A timely detection of the onset of a fault in a component is crucial to a quality monitoring of a process if costly failures are to be avoided. However, finding the source of the failure is not trivial in systems with a large number of components and complex component relationships. In this paper, an efficient scheme to detect adverse changes in system reliability and find the failed component is proposed in order to have an effective process quality monitoring. The monitoring scheme has been made effective by implementing first the techniques of fixed-parameter Shewhart, MEWMA and Hotelling’s T2 control chart, and then the adaptive versions of Shewhart Chart, MEWMA and T2 control chart for counter checking the precision of quality reports. Once detected, the fault isolation scheme uses a Bayesian decision strategy based on the maximum correlation between the residual and one of a number of hypothesized residual estimates to generate a fault report. By doing so, the critical information about the presence or absence of a fault, and its isolation, is gained in a timely manner, thus making the quality monitoring system an effective tool for a variety of maintenance programs, especially of the preventive type. The proposed scheme is evaluated extensively on simulated examples, and on a physical fluid system exemplified by a benchmarked laboratory scale two-tank system to detect and isolate faults including sensor, actuator, and leakage ones.
|Number of pages||13|
|Journal||International Journal of Advanced Manufacturing Technology|
|Early online date||4 Dec 2010|
|Publication status||Published - Jul 2011|
Rahim, M. A., Khalid, H. M., Akram, M., Khoukhi, A., Cheded, L., & Doraiswami, R. (2011). Quality monitoring of a closed-loop system with parametric uncertainties and external disturbances: a fault detection and isolation approach. International Journal of Advanced Manufacturing Technology, 55(1-4), 293–306. https://doi.org/10.1007/s00170-010-3043-2