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Effective fault detection and diagnosis will greatly improve the operational efficiency and reliability of wind turbine equipment, reduce maintenance costs, and ensure the smooth progress of the production process. To achieve efficient equipment fault warning and maintenance, research on equipment operation fault detection and diagnosis methods based on sensor technology and machine learning. Firstly, preprocess the data signals transmitted by sensors using methods such as box plots and wavelet packet denoising. Then, a time series prediction model is constructed using a bidirectional long short-term memory network. Finally, based on prediction residuals and Bayesian probability theory, a signal anomaly recognition strategy was designed to achieve real-time monitoring and fault warning. Performance analysis was conducted on the proposed wind turbine equipment fault monitoring model, and the results showed that the diagnostic accuracy of the model constructed by the research institute was 98.88%, with no missed diagnosis and a misdiagnosis rate below 1.5%. Early warning was given at least 14 hours in advance. The research model can provide timely warning for wind turbine equipment faults and diagnose faults with high accuracy.