Fault detection, identification and diagnosis using CUSUM based PCA

In this paper, a cumulative sum based statistical monitoring scheme is used to monitor a particular set of the Tennessee Eastman Process (TEP) faults that could not be properly detected or diagnosed with other fault detection and diagnosis methodologies previously reported.T2 and Q statistics based on the cumulative sums of all available measurements were successful in observing these three faults. For the purpose of fault isolation, contribution plots were found to be inadequate when similar variable responses are associated with different faults. Fault historical data is then used in combination with the proposed CUSUM based PCA model to unambiguously characterize the different fault signatures. The proposed CUSUM based PCA was successful in detecting, identifying and diagnosing both individual as well as simultaneous occurrences of these faults.

► We propose new multivariate fault detection and diagnosis strategy based on CUSUM based mechanism. ► We were successful in detecting and diagnosing three challenging faults of Tennessee Eastman Process. ► Fault historical data is required for unambiguous fault diagnosis.