Principal Component Analysis-Based Ensemble Detector for Incipient Faults in Dynamic Processes
Decheng Liu, Jun Shang, Maoyin Chen
Abstract
The significant advancement in data-driven fault detection has been made, but incipient faults such as faults 3, 9, and 15 in Tennessee Eastern process (TEP) still remain difficult for the current approaches. In this article, a powerful principal component analysis (PCA)-based ensemble detector (PCAED) is developed for detecting incipient faults. To begin with, multiple PCA-based detectors are designed based on bootstrap sampling in the training dataset. It can generate two matrices according to principal component and residual subspaces. Then, two sensitive detection indices are developed using maximal singular values of one-step sliding windows along the rows of the above two matrices. With this kind of detection index, PCAED can effectively detect incipient faults, specially faults 3, 9, and 15 in TEP, which cannot be detected by an individual PCA detector. Simulations of TEP and a practical coal pulverizing system fully verify the effectiveness of PCAED. Faults can be successfully detected at the incipient stage, which is very helpful to avoid possible economic or human loss.