Application of Machine Algorithm in Electrical Equipment Fault Warning and Diagnosis

This study proposes a fusion model based on Convolutional Neural Network (CNN), Long Short Term Memory Network (LSTM), and attention mechanism to address the issues of insufficient efficiency and limited real-time performance of multi-source data fusion in electrical equipment fault diagnosis. The model aims to construct a fault warning and diagnosis system for industrial scenarios. By synchronously collecting multimodal data of vibration signals, temperature field images, and electrical parameters, and combining preprocessing techniques such as wavelet denoising and spatiotemporal alignment to improve data quality, CNN is used to extract spatial features of temperature fields, LSTM is used to model temporal dependencies, and attention machine is used to allocate feature weights for braking states, achieving spatiotemporal joint modeling of equipment operating states. The experimental results show that the model achieves a fault diagnosis accuracy of $\mathbf{9 8. 2 \%}$ on the test set, which is $\mathbf{1 5. 7 \%}$ higher than the traditional support vector machine algorithm. The inference delay is controlled within $50-70 ~\text{ms}$, meeting the real-time requirements of industry $(<100 ~\text{ms})$. Edge computing deployment uses TensorRT quantization and model lightweight technology to compress the total number of parameters to $\mathbf{3. 8 M}$, significantly improving the engineering adaptability of the algorithm. The research has verified the effectiveness of multimodal feature fusion and spatiotemporal joint modeling, providing a solution that combines theoretical depth and engineering value for intelligent operation and maintenance of electrical equipment, and promoting the practical application of machine learning algorithms in smart grid fault diagnosis.