Sparse stacked autoencoder network for complex system monitoring with industrial applications

Modern industrial systems are increasingly complex with the development of technology and equipment. Due to the variation of process loads and other factors, many processes often switch their operating modes, which results in the problem of multimode process monitoring. In addition, owing to the harsh industrial conditions, monitoring of multimode processes is difficult and challenging. In this paper, a fault classification and isolation method were proposed based on sparse stacked autoencoder network. In detail, a single autoencoder is trained one by one in an unsupervised way. Then, the hidden layer of each trained autoencoder is cascade connected to form a deep structure. Finally, the stacked autoencoder network is followed by a Softmax layer to realize the fault classification task. Since the deep structure can well learn and fit the nonlinear relationship in the process and perform feature extraction more effectively compare with other traditional methods, it can classify the faults accurately. After an in-depth exploration of the deep neural network, a novel fault isolation scheme is proposed. By calculating the contribution rate of each variable, the one with the largest contribution was identified as the fault location. In order to evaluate the efficiency of the proposed method, some numerical experiments were conducted on the continuous stirred tank heater (CSTH) benchmark process as well as two industrial cases including the wind turbines monitoring and the aluminum electrolysis process monitoring in comparison with several traditional methods. The experimental results show that the accuracy of the proposed method in the fault classification process is better than other methods and the fault isolation scheme can well locate the variable that caused the fault.