Predictive maintenance on injection molds by generalized fault trees and anomaly detection

Abstract

Predictive maintenance (PdM) plays a key role in the Industry since it allows optimization of the schedule for proactive interventions and to take the maximum advantage of the useful lifetime of industrial assets. The reliability-centered maintenance (RCM) is based on equipment's reliability and allows the use of different maintenance strategies to optimize maintenance costs. With a recently proposed data-driven methodology entitled generalized fault trees (GFT), it is possible to assess the reliability of industrial equipment in real-time, based on their actual condition. In this paper, we exploit the GFT methodology in a completely different industrial scenario. A new training algorithm that intends to minimize operational costs, together with an anomaly detection technique (isolation forest) is presented to perform the predictive maintenance of injection molds at OLI, an enterprise specialized in producing plastic parts by the injection process. The results show that the proposed methodology may allow savings of 27.05% compared with preventive maintenance (PM) in optimized constant periods, and 63.43% compared to corrective maintenance (CM).