Fault Diagnosis Using Improved Discrimination Locality Preserving Projections Integrated with Sparse Autoencoder

In order to ensure safe operations of industrial processes, it is especially important to find fault types accurately and quickly based on historical data and then deal with faults in time. Unfortunately, due to the tricky characteristics of industrial process data such as massive number, high dimensionality, and nonlinearity, it turns out that timely and accurate diagnosis of faults becomes of great difficulty in industrial processes. To address this problem, novel effective fault diagnosis using an improved global and local dimensionality reduction (DR) method named discrimination locality preserving projections integrated with sparse autoencoder (SAEDLPP) is proposed in this article. In SAEDLPP, the global DR information of data is first obtained by sparse autoencoder (SAE); next, the DR data obtained through SAE are passed through discrimination locality preserving projections (DLPP) to obtain local DR information, preserving not only the global information but the local information of the extracted features. Finally, fault diagnosis is achieved by separating the extracted features by SAEDLPP using an AdaBoost classifier to recognize fault types. Simulations are conducted on the Tennessee Eastman process (TEP) and the results indicate that the provided SAEDLPP-based fault diagnosis methodology can achieve much higher accuracy in fault diagnosis than other associated methods.