Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis

TLDR

Standard partial least squares has been widely used for KPI monitoring in large‑scale process industry for two decades, yet it still struggles with fault diagnosis related to underlying KPIs. This study proposes an improved PLS (IPLS) method to address these diagnostic challenges. IPLS decomposes measurable process variables into KPI‑related and unrelated components, then constructs test statistics that provide actionable fault diagnosis information, as demonstrated on a numerical example and the Tennessee Eastman benchmark. The method achieves satisfactory diagnosis of KPI‑related faults and a high fault detection rate.

Abstract

Standard partial least squares (PLS) serves as a powerful tool for key performance indicator (KPI) monitoring in large-scale process industry for last two decades. However, the standard approach and its recent modifications still encounter some problems for fault diagnosis related to KPI of the underlying process. To cope with these difficulties, an improved PLS (IPLS) approach is presented in this paper. IPLS is able to decompose the measurable process variables into the KPI-related and unrelated parts, respectively. Based on it, the corresponding test statistics are designed to offer meaningful fault diagnosis information and thus, the corresponding maintenance actions can be further taken to ensure the desired performance of the systems. In order to demonstrate the effectiveness of the proposed approach, a numerical example and Tennessee Eastman (TE) benchmark process are respectively utilized. It can be seen that the proposed approach shows satisfactory results not only for diagnosing KPI-related faults but also for its high fault detection rate.