Fault-Tolerant Soft Sensors for Dynamic Systems

Unpredicted faults occurring in automation systems deteriorate the performance of soft sensors and may even lead to incorrect results. To address the problem, this study develops three novel data-driven approaches for development of soft sensors. The three proposed soft sensors have fault-tolerant abilities. They are, respectively, called measurement space-aided scheme (MSaS), subspace-aided scheme (SSaS), and improved MSaS (IMSaS). As means to obtain more accurate results of soft sensors in the online phase: 1) MSaS constructs an optimal estimator of faults in the measurement space; 2) SSaS removes the influences caused by unknown sensor faults with the aid of a constructed subspace; and 3) IMSaS is an improved version of MSaS, eliminating the influences of the past prediction error that may accumulate and affect the current prediction result. They are the output-driven fault-tolerant soft sensors because their implementations rely on system measurements only. Furthermore, performance analysis is also conducted to investigate the estimation errors. Both the sufficient and necessary conditions for these designs are provided, and illustrations of the effectiveness and feasibility of the three proposed fault-tolerant soft sensors based on two case studies are given.

Automated summary

This study develops three novel data-driven approaches for the development of fault-tolerant soft sensors in automation systems. The approaches, namely MSaS, SSaS, and IMSaS, aim to address the issue of unpredictable faults and their impact on soft sensor performance. MSaS constructs an optimal estimator of faults, SSaS removes influences from unknown sensor faults using a constructed subspace, and IMSaS is an improved version of MSaS that eliminates the effects of past prediction errors. These fault-tolerant soft sensors rely solely on system measurements and are evaluated through performance analysis and case studies.