Intelligent Maintenance and Life Prediction of Electrical Equipment Based on Machine Learning

With the rapid development of modern industry and the energy sector, the safety and reliability of electrical equipment are crucial to the stable operation of the system. Traditional maintenance methods usually rely on periodic inspections or experience-based judgments, which are difficult to meet the current demand for efficiency and precision in the complex equipment operation environment. Intelligent maintenance and life prediction technology based on machine learning mines the potential patterns in equipment operation data. Electrical equipment often faces the influence of overheating, dust contamination, humidity changes, and other unfavorable factors, resulting in equipment susceptible to temperature change failures. Temperature change failure refers to the dysfunction or performance degradation of electrical equipment caused by abnormal changes in temperature. If these failures are not detected and handled in a timely manner, they can lead to equipment damage, production interruptions, and even personnel safety accidents. Although there are certain techniques and methods for monitoring and maintenance of electrical equipment, such as regular inspection and preventive maintenance, these traditional methods often rely on manual experience, and cannot effectively diagnose and deal with faults in real time, to achieve early diagnosis of electrical equipment failures and accurate prediction of life. Aiming at the current research situation and technical challenges in this field, this paper proposes an intelligent maintenance framework with integrated data acquisition, model construction and algorithm optimization, discusses in detail the key applications of machine learning algorithms in equipment condition monitoring, fault diagnosis and remaining life prediction, and analyzes the effectiveness and feasibility of the technology in combination with typical cases. The machine learning technology can significantly improve the intelligent level of electrical equipment maintenance, which provides important support for the safety and economy of equipment operation, and also provides a guiding direction for future research and application.