Fault detection for industrial processes based on time-serial maximal deviation analysis

The application of feature extraction algorithms in data-driven fault detection has been widely investigated for industrial processes, with a wide range of multivariate analytical methods available in the literature for this purpose. However, mainstream methods all focus on exploiting the systematic variation in historical normal samples and a fault is usually isolated as a deviation from the characterized normal signatures. For the sole purpose of fault detection in dynamic processes, it may be more effective to timely and adaptively uncover deviations inherent in time-serial variations for online sequential samples of interest, rather than focusing solely on extracting latent representations from normal samples. Therefore, a novel feature extraction algorithm called time-serial maximal deviation analysis (TSMDA) is proposed. TSMDA is designed to derive a projecting scheme with dynamically determined projecting vectors in a timely manner, so that possible time-serial deviations in newly measured sequential samples can be adaptively uncovered to the greatest extent possible. Most importantly, TSMDA cannot be executed without the involvement of online monitored samples and it is expected to guarantee consistently enhanced fault detectability. The effectiveness of the proposed fault detection method is validated through practical experiments on two industrial processes and the influence of key model parameters on fault detectability is also evaluated.