Condition Detection and Fault Diagnosis of Electrical Engineering Equipment based on Deep Learning Algorithm

Traditional fault diagnosis methods often rely on expert experience and simple statistical analysis, but in the field of modern electrical engineering, more efficient and accurate automatic diagnosis means are needed. Based on this background, this study proposes an innovative fault diagnosis method for electrical equipment, which improves the accuracy and efficiency of fault detection by combining Otsu threshold segmentation algorithm with Convolutional Neural Network. Otsu algorithm is used to preprocess the infrared thermal image of electrical equipment. The image is divided into foreground and background regions by calculating the global optimal threshold, which enhances the identifability of fault features. The CNN is then responsible for further feature extraction and fault classification. The image data preprocessed by the Otsu algorithm is input into the customized CNN model, and the weight is continuously adjusted by the back propagation algorithm to optimize the model performance. This framework not only improves the quality and interpretability of image data, but also enhances the training effect and diagnostic accuracy of the CNN model. Experimental results show that the proposed framework can identify fault features more accurately, reduce false positive rate and false negative rate, and improve the overall diagnostic accuracy when dealing with images with high background noise. This innovative framework is expected to be applied in a wider range of electrical engineering fields and provide strong technical support for industrial automation and intelligence.