Fault detection and identification method: 3D-CNN combined with continuous wavelet transform

Abstract

This study proposes a novel fault detection and identification method using Continuous Wavelet Transform (CWT) and a three-dimensional Convolutional Neural Network (3D-CNN). In particular, multivariate time series data from chemical plants were divided by a time-shifting window and transformed into scalograms using CWT. These scalograms were fed to 3D-CNN to generate outputs indicating the faults that occurred in the process. We applied the proposed method to a Tennessee Eastman process dataset. The proposed method adequately captures the characteristics in the time–frequency domain and exhibited good fault detection and identification performances on the dataset.