Predictive Maintenance of Cash Dispenser Using a Cognitive Prioritization Model

In this technical paper, we address the issue of predicting cash dispenser (addressed as ‘Device’ henceforth) failure by harnessing the power of humungous data fromservice history, logs, metrics, transactions, and plausible environmental factors. This study helps increase device availability, enhanced customer experience, manage risk &compliance and revenue growth. It also helps reduce maintenance cost, travel cost, labour cost, downtime, repair duration and increase meantime between failures (MTBF) ofindividual components. This study uses a cognitive prioritization model which entails the following at its core; a) Machine Learning engineered features with highest influence onmachine failure, b) Observation Windows, Transition Windows and Prediction Windows to accommodate various business processes and service planning delivery windows, andc) A forward-looking evaluation of emerging patterns to determine failure prediction score that is prioritized by business impact, for a predefined time window in the future. Themodel not only predicts failure score for the devices to be serviced, but it also reduces the service miss impact for the prediction windows.