Online monitoring and fault diagnosis for uneven length batch process based on multi‐way orthogonal enhanced neighborhood preserving embedding

Abstract

AbstractIn the practical batch process, the duration of each batch is probably different, and the key event happened may also vary from batch to batch. This paper proposes an effective fault diagnosis algorithm to synchronize the batch runs with uneven duration and keep the key features that reflect the running state of batch process. To address the batch out of sync, the relaxed greedy time warping is used to accurate on‐line synchronization of ongoing batch and avoid evaluating the optimal path every time a new sample is available. For online monitoring and fault diagnosis, an important step is how to extract key features that reflect the running state of batch process, on which basis a weight matrix considers the distance between the neighbor data is embedded to construct the enhanced objective function, and then the mutually orthogonal basis functions which represent the local geometrical structure are computed to extract key features of batch process. After a fault is detected, the contribution plot method is used to diagnose the variables that cause this fault. The effectiveness of the proposed algorithm is verified by the penicillin fermentation process.