Run-to-Failure Relay Dataset for Predictive Maintenance Research With Machine Learning

Abstract

Predictive maintenance (PdM) offers enormous potential for improvement, both economically and ecologically. The extended use of components can improve sustainability, minimize the occurrence of component failures and associated safety risks for people and the environment, and ultimately increase the profitability of plants. PdM is therefore a future key technology that requires sensing, communication of collected data, and analysis of data to implement. In this work, the phoenix contact relay (PCR) dataset is provided and used to demonstrate that there is a need for research into new PdM techniques. The PCR dataset presented includes life data from 546 relays with a total of more than 106 million switching cycles collected over a period of several years at a sampling rate of 10 kHz. Manufacturers and loads were varied to ensure practical relevance. This sets PCR apart from previously published datasets in terms of number of measurements, complexity of measurements, scope of units tested, and practicality, providing the opportunity to design and evaluate new methods for PdM.