Professional paper

https://doi.org/10.31803/tg-20210205105921

Predictive Maintenance of Cash Dispenser Using a Cognitive Prioritization Model

Archana Dixit orcid.org/0000-0001-7093-6013 ; IBM India
Amol B. Mahamuni ; IBM India

Abstract

In this technical paper, we address the issue of predicting cash dispenser (addressed as ‘Device’ henceforth) failure by harnessing the power of humungous data from service 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) of individual components. This study uses a cognitive prioritization model which entails the following at its core; a) Machine Learning engineered features with highest influence on machine failure, b) Observation Windows, Transition Windows and Prediction Windows to accommodate various business processes and service planning delivery windows, and c) 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. The model not only predicts failure score for the devices to be serviced, but it also reduces the service miss impact for the prediction windows.

Keywords

Cash Dispenser Failure Prediction; Cognitive Prioritization Model; Feature Engineering; Machine Learning; Predictive Maintenance (PdM)

Hrčak ID:

253082

URI

https://hrcak.srce.hr/253082

Publication date:

3.3.2021.

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