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Integrated equipment fault detection and health management (PHM) has become the focus of the researches on the equipment, but because of its high integration, complicated structure, the characteristics of comprehensive, the conventional detection method often face a multi-source heterogeneous information, size, and difficult problems to ensure real-time performance, not only consume large amounts of resources, It also requires strong data analysis and control skills. A fault detection algorithm based on CNN and BI-LSTM (Bidirectional Short and Long Memory Network) and its optimization algorithm is proposed to ensure the unity of accuracy, real-time and validity. Bi-lstm-cnn-fcm model was constructed and verified by tennessee-Eastman chemical process dataset. During the experiment, appropriate activation functions were selected by observing the influence of different activation functions on the model accuracy and effect, and finally tanH activation function was determined to be used in the convolution layer and Relu activation function was used in the full connection layer. After the activation function was determined, the model was continuously optimized, and FCM clustering algorithm was added at the end of the model to improve the accuracy of fault detection and classification. Finally, based on the accuracy and loss value, the superiority of the model was proved by comparing with the single LSTM model, CNN model and LSTM-CNN model. This model improves the accuracy of fault detection to 98.25% and reduces the loss value to 0.0104, which is obviously superior to other models in performance.