Using Big Data and Machine Learning to Predict Household Appliance Failures: A New Approach to Preventive Maintenance

Abstract

This study explores the application of big data processing methods and machine learning algorithms for predicting household appliance failures, shifting from traditional reactive maintenance models to proactive preventive repair systems. The study is based on an analysis of historical data, error logs, and sensor readings, enabling the identification of hidden patterns that indicate potential malfunctions.  The novelty of this study lies in the adaptation and comprehensive application of modern data analysis methods to the operational specifics of household appliances, as well as an assessment of the economic efficiency of such solutions. The applied methodology includes stages of data collection and preprocessing, feature engineering, machine learning algorithm implementation, and comparative economic analysis. The results demonstrate the potential of predictive maintenance in reducing downtime, optimizing repair costs, and improving service quality. The findings of this study are valuable for data analysts, household appliance engineers, predictive model developers, and companies engaged in appliance servicing and manufacturing, seeking to enhance preventive maintenance efficiency using advanced machine learning and big data analytics methods. This study aims to shift from reactive maintenance to proactive preventive maintenance, addressing real-world challenges faced by appliance manufacturers and service providers.