Statistical process monitoring of a multiphase flow facility. (September 2015)

• Record Type: Journal Article
• Authors: Ruiz-Cárcel, C.; Cao, Y.; Mba, D.; Lao, L.; Samuel, R.T.
• Is Part Of: Control engineering practice. Volume 42(2015)
• Page Start: 74
• Page End: 88
• Publication Date: 2015-09
• Subjects: Fault detection -- Diagnosis -- Multivariate -- Canonical -- Experimental -- Process; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2015.04.012
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Industrial needs are evolving fast towards more flexible manufacture schemes. As a consequence, it is often required to adapt the plant production to the demand, which can be volatile depending on the application. This is why it is important to develop tools that can monitor the condition of the process working under varying operational conditions. Canonical Variate Analysis (CVA) is a multivariate data driven methodology which has been demonstrated to be superior to other methods, particularly under dynamically changing operational conditions. These comparative studies normally use computer simulated data in benchmark case studies such as the Tennessee Eastman Process Plant (Ricker, N.L. Tennessee Eastman Challenge Archive, Available at 〈http://depts.washington.edu/control/LARRY/TE/download.html 〉 Accessed 21.03.2014). The aim of this work is to provide a benchmark case to demonstrate the ability of different monitoring techniques to detect and diagnose artificially seeded faults in an industrial scale multiphase flow experimental rig. The changing operational conditions, the size and complexity of the test rig make this case study an ideal candidate for a benchmark case that provides a test bed for the evaluation of novel multivariate process monitoring techniques performance using real experimental data. In this paper, the capabilities of CVA to detect and diagnose faults in a real system working under changing operating conditions are assessed and compared with other methodologies. The results obtained demonstrate that CVA can be effectively applied for the detection and diagnosis of faults in real complex systems, and reinforce the idea that the performance of CVA is superior to other algorithms.