A Novel End-to-End Provenance System for Predictive Maintenance: A Case Study for Industrial Machinery Predictive Maintenance

Abstract

In this study, we address the critical gap in predictive maintenance systems regarding the absence of a robust provenance system and specification. To tackle this issue, we propose a provenance system based on the PROV-O schema, designed to enhance explainability, accountability, and transparency in predictive maintenance processes. Our framework facilitates the collection, processing, recording, and visualization of provenance data, integrating them seamlessly into these systems. We developed a prototype to evaluate the effectiveness of our approach and conducted comprehensive user studies to assess the system's usability. Participants found the extended PROV-O structure valuable, with improved task completion times. Furthermore, performance tests demonstrated that our system manages high workloads efficiently, with minimal overhead. The contributions of this study include the design of a provenance system tailored for predictive maintenance and a specification that ensures scalability and efficiency.