Artificial Intelligence for Diagnostics and Repair of Household Appliances: Enhancing Efficiency and Cost-Effectiveness

Abstract

The article describes the assessment of the impact of artificial intelligence on the efficiency of diagnostics and repair of household appliances. The relevance of the topic lies in the growing complexity of modern equipment, which nowadays includes many different sensors, microprocessors and control systems. The novelty of the study lies in the study and correlation with predictive maintenance of such key performance indicators as the accuracy of fault detection, reduction in repair time and optimization of maintenance costs. The study analyzed data from sensors, error codes and historical usage logs. A comparison of traditional diagnostics and diagnostics using AI was also conducted. The purpose of the study is to demonstrate how to reduce operating costs and improve service quality by applying automation and machine learning methods. The study used a comparative method, statistical data processing and case study analysis. The results confirm the potential of AI in predictive maintenance and its optimization. The conclusions describe the economic benefits of AI integration and its implementation prospects in the field of household appliance repair. The article will be useful for service companies, engineers and researchers studying AI - based solutions in technical diagnostics and maintenance.