Design and application of enhanced deep convolutional neural networks model for fault diagnosis in practical chemical processes
Data-driven fault diagnosis technologies can help operators find and detect process abnormalities in a timely and effective manner,having emerged as one of the hot topics in the current integration of industry and big data.The deep convolutional neural network(DCNN)approach is the most commonly used data-driven fault diagnosis model,but its activation process suffers from the mismatch of positive and negative values and the problem of parameter redundancy resulted by inefficient information flow.In this study,a novel activation mechanism based on the maximum smoothing unit(MSF)function was proposed to overcome the shortcomings of the previous activation functions,and the attention mechanism combined with the gated recurrent unit(GRU)was introduced to overcome the problem of parameter redundancy by improving the efficiency of information flow in DCNN.The as-established model of enhanced deep convolutional neural networks(EDCNN)exhibited significantly improved performance,which was verified by its applications in two industrial processes,the industrial actuator control system and the industrial acid gas absorption process.The average fault diagnosis rate in both processes exceeded 99.0%.
fault diagnosisenhanced deep convolutional neural networksprocess controlsystems engineeringactivation function
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