An Unsupervised Learning Model for Intelligent Machine-Failure Prediction With Heterogeneous Sensors

This study proposes a system that uses unsupervised learning to autonomously identify sensor data which suggest that a machine may soon fail. The system predicts three failure modes in the servo motor of an injection machine by learning multivariate data from heterogeneous sensors. The unsupervised learning model predicted failures with an average F1 score of 0.9958. A case study in an actual shop verified the system’s practical applicability. This shop is a factory that runs 27 injection machines of various tonnages. Results confirmed the ease of retraining the unsupervised learning model and demonstrated its portability. The use of an unsupervised learning model eliminates the difficulties and dependencies associated with data acquisition for supervised learning models. The case study indicated that the use of the proposed failure-prediction program can reduce maintenance costs by up to $US 140,000/y. It can be applied to various machines across different industries.