Distributed dictionary learning for high-dimensional process monitoring

Abstract

In order to conduct efficient process monitoring of modern industrial system featured with complexity, distributed and high-dimensional, a distributed dictionary learning is proposed for fault detection and fault isolation task. Firstly, it can reduce the computational complexity by decomposing the whole high-dimensional industrial system into several low-dimensional modules, and some prior process knowledge is integrated into the data-driven model to ensure the reliability during the decomposition stage. Secondly, since the small failure is easy to hide in high-dimensional data, it is more conducive to detecting the process data by using the sub-modules. Based on this, a Bayesian inference method is presented to fuse the distributed results for global industrial process monitoring. For the fault samples which have been detected successfully, a count time based method is introduced to determine the fault location on the block level. Then, a sparse contribution plot method is used to locate the failure source of the system on the variable level further. In the end, the performance of the proposed method is verified on a numerical simulation case, the Tennessee Eastman (TE) benchmark and an aluminum electrolysis process.