Analysis of Process Data for Remote Health Prediction in Distributed Automation Systems

Abstract

Predictive Maintenance (PdM) is a one of the core topics for Industry 4.0 and entitled as “Predictive Maintenance 4.0.” The main tasks of PdM are to monitor production tool health and then issue an alert when a maintenance is necessary. PdM has become a top priority as it can optimize tool utility. The so-called iFA system platform, realized by integrating several intelligent services including Intelligent Predictive Maintenance (IPM), was proposed to accomplish the goal of Zero-Defect Manufacturing. However, the current algorithm in IPM did not provide a feasible aging feature extraction procedure. Thus, once the aging features cannot be acquired adequately, the monitoring accuracy will become poor. To remedy the above-mentioned problem, the automated Aging Feature Extraction Scheme (AFES) is proposed in this paper to perform analysis of process data for remote health prediction. This automated AFES is packed as an application module and plugged in the cyber physical agent of iFA. The proposed architecture, which integrates iFA, Resource Agent (RA), message broker, and automated Production System, is also designed to effectively monitor tool health status and predict the remaining useful life via the automated AFES. The experimental results indicate that the proposed architecture can not only enhance the performance of the IPM algorithm, but also feed-back the tool health indexes to RA via comprehensive system integration, such that the goal of optimized/maximum OEE can be accomplished. This work was submitted alongside another paper to CASE2022, conceptualizing a data exchange infrastructure and its impact on dependability characteristics of the technical process.