Novel Imbalanced Fault Diagnosis Method based on CSMOTE integrated with LSDA and LightGBM for Industrial Process

With the coming of the big data era, the data collected in the process industry shows features of high volume, high-dimensional and non-linear. Meanwhile, these process data present imbalanced feature, leading to a lack of fault information. These problems mentioned above have brought difficulties to fault diagnosis. To solve the above difficulties, a new synthetic minority over-sampling technique (SMOTE) considering the correlation of sample integrated with locality sensitive discriminant analysis (LSDA) and LightGBM fault diagnosis methodology (CSMOTE-LSDA-LightGBM) is proposed in this article. In our proposed methodology, firstly, the SMOTE fully considering correlation (CSMOTE) which uses both Euclidean and Mahalanobis distance to calculate the nearest neighbor relationship is used to resample the imbalanced samples and expand the number of small classification fault samples; secondly, the LSDA is used to dimensionality reduction (DR) to extract the fault-related critical features; finally, the LightGBM classifier is used for fault classification. The Tennessee Eastman (TE) process case is selected for simulation to verify the effectiveness of the proposed CSMOTE-LSDA-LightGBM in fault diagnosis. The simulation results of TE process case show that the proposed method has improved the accuracy of fault diagnosis compared with imbalanced data and traditional DR methods indicating the CSMOTE-LSDA-LightGBM methodology is applicable to fault diagnosis for imbalanced samples.