Integrated failure analysis using machine learning predictive system for smart management of medical equipment maintenance

The study aims to develop the failure analysis predictive models, which prognosticate first failure event (FFE), failure-to-year ratio (FYR), and failure rectification group (FRG). The construction of predictive models involved nineteen categories of 13,350 units of medical equipment. We proposed thirteen novel features in assessing medical equipment failures. The failure analysis predictive models were categorised into several classes for training and testing the developed models. There was seven supervised machine learning classifiers and performance metrics applied in the experiment. The experiment demonstrates that Support Vector Machine is the best classifier for the FFE predictive model, which achieves an accuracy of 96.9% after hyperparameter optimisation. Furthermore, Decision Tree is the best classifier for FYR, with an accuracy of 83.9%. Meanwhile, the comparative analysis for FRG discovered that Artificial Neural Network achieved the highest accuracy among others with 76.7% accuracy after the hyperparameter optimisation process. Findings from this study indicate that this failure analysis predictive model functions as a main instrument for conducting predictive maintenance in the direction of smart maintenance practices. Through the developed predictive systems, timely maintenance of medical equipment can be performed. This will also assist healthcare service providers in initiating the remanufacturing and refurbishment programme, ensuring efficient medical care delivery. The suggested framework of machine learning-assisted failure analysis for medical equipment maintenance management may provide clinical engineers with guidance for managing the strategic maintenance management for medical equipment.