Strength Prediction Method of Electrical Equipment Casing Driven by Big Data

With the rapid development of industrial automation and informatization, explosion-proof electrical equipment plays a crucial role in high-risk environments such as petrochemicals and mining. The reliability of its performance is directly related to production safety and personnel life safety. Traditional performance evaluation methods often rely on historical operating data and empirical judgments of the equipment, which is not only time-consuming but also difficult to adapt to rapidly changing working conditions. In view of this, this study proposes a performance prediction model for explosion-proof electrical equipment based on big data analysis, aiming to extract valuable information from a large amount of equipment operation data through efficient data mining techniques and machine learning algorithms to predict equipment performance and failure probability. This article uses ensemble learning and deep learning techniques to construct a prediction model, and employs feature selection techniques to optimize the input parameters of the model, thereby improving the accuracy and efficiency of predictions. The weekly updated model performed poorly in the worst-case scenario (accuracy of 0.76), while the quarterly updated model maintained a high accuracy even in the worst-case scenario (0.84). The value of the research lies in providing a new approach and method for the maintenance of explosion-proof electrical equipment. It can not only achieve early warning of equipment failures and reduce the risk of unexpected shutdowns, but also significantly improve the scientific and refined level of equipment management.