Evaluation of Maintenance and Repair Planning for Banking ATMs Using Deep Learning Algorithms
Keywords:
Maintenance planning, ATMs, deep learningAbstract
This study aims to develop an intelligent model for predicting failures and optimizing maintenance scheduling of banking ATMs through deep learning algorithms and operational data analysis. A meta-synthesis approach was used to identify and categorize factors influencing ATM failures based on previous studies. Extracted indicators were classified into major and minor categories, and coding quality was assessed using the Kappa index. The Shannon entropy method was applied to determine the significance of each component. In the experimental phase, a deep neural network model with two hidden layers was implemented, using input features such as transaction logs, temperature, voltage, sensor states, and maintenance history. Model performance was evaluated using accuracy, mean squared error (MSE), and coefficient of determination (R²). Results indicated that variables such as withdrawn cash volume, internal temperature, population density around the installation area, and the type of last maintenance service were the most influential in predicting failures. The Kappa value of 0.82 demonstrated excellent inter-coder reliability. The proposed model accurately predicted potential failures within the defined time window. Shannon entropy analysis showed that the components of performance and transaction, hardware and sensors, and maintenance events carried the highest information weight. The application of deep learning algorithms in predictive maintenance of ATMs effectively reduces emergency repair costs, increases system reliability, and enhances customer satisfaction. The findings highlight that the CNN–LSTM hybrid model can be efficiently integrated into intelligent ATM monitoring systems for proactive maintenance planning.
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