A Review of Predictive Maintenance Strategies for Critical Process Equipment in Photovoltaic Cell Manufacturing Lines

Abstract

Unplanned equipment downtime in photovoltaic cell manufacturing carries direct cost in lost production volume and indirect cost in process-window perturbation that increases yield loss after restart. Predictive-maintenance strategies based on equipment-condition monitoring, vibration analysis, drift trending, and electrical-signature analysis have advanced substantially but remain unevenly adopted across the photovoltaic manufacturing sector. This review consolidates the published evidence on predictive-maintenance program effectiveness, surveys the sensor-instrumentation and analytics technologies most applicable to PECVD reactors, diffusion furnaces, screen printers, and firing belt furnaces, and offers an organizational-readiness framework that connects technology selection to maintenance-organization maturity.