An Imbalance Modified Convolutional Neural Network With Incremental Learning for Chemical Fault Diagnosis

Abstract

Fault diagnosis that identifies the root of the abnormal status is of great importance to eliminate faults in the complex chemical processes. Many data-driven fault diagnosis models ignore different faults that occur with varied frequencies in chemical plants, and they need a complete retraining process with the arrival of new fault modes. In this article, a novel incremental imbalance modified convolutional neural network is proposed to solve the aforementioned issues. The proposed method employs an imbalance modified method to extract the valuable information from the imbalance data, and generate new samples. After that, a local hyperplane-based dynamic Relief is designed to reduce the dimension of the chemical data and simplify the complex learning process. Finally, for the arrival of new fault modes, the proposed method is prompted in an incremental hierarchical way. Unlike the traditional models that are trained on static data, the proposed method inherits the existing knowledge and updates itself to include new coming fault classes. The proposed method is utilized in a simulated process and a real industrial process. Experimental results illustrate that the proposed method is better than the existing methods and has significant robustness and reliability in chemical fault diagnosis.