Deep Forest-Based Fault Diagnosis Method for Chemical Process

Jiaman Ding,Jinguo You,Lianyin Jia,Qingbo Luo

doi:10.1155/2020/5281512

Abstract

With the rapid expanding of big data in all domains, data-driven and deep learning-based fault diagnosis methods in chemical industry have become a major research topic in recent years. In addition to a deep neural network, deep forest also provides a new idea for deep representation learning and overcomes the shortcomings of a deep neural network such as strong parameter dependence and large training cost. However, the ability of each base classifier is not taken into account in the standard cascade forest, which may lead to its indistinct discrimination. In this paper, a multigrained scanning-based weighted cascade forest (WCForest) is proposed and has been applied to fault diagnosis in chemical processes. In view of the high-dimensional nonlinear data in the process of chemical industry, WCForest first designs a set of relatively suitable windows for the multigrained scan strategy to learn its data representation. Next, considering the fitting quality of each forest classifier, a weighting strategy is proposed to calculate the weight of each forest in the cascade structure without additional calculation cost, so as to improve the overall performance of the model. In order to prove the effectiveness of WCForest, its application has been carried out in the benchmark Tennessee Eastman (TE) process. Experiments demonstrate that WCForest achieves better results than other related approaches across various evaluation metrics.

Highlights

Performance improvement and surveillance facilitation have become increasingly important in industrial processes
The modern industries are developing in the direction of large scale and complexity, and with the widespread use of monitoring technology, large volumes of industrial process data have been collected from broadly deployed sensors and other control equipment. erefore, to maximize use of these massive data to further improve both accuracy and speed of fault diagnosis is significant for a complicated process monitoring system
An improved deep forest model, WCForest, is proposed for fault diagnosis of chemical processes to improve accuracy, reduce false alarm rate, and process highdimensional and nonlinear data. e main performance is that, without increasing the computational complexity, k-fold cross-validation is used to calculate the weight of each forest in the cascade structure in order to boost the good performance of forests and weaken the bad ones, so as to improve the overall performance of the cascade random forest

Summary

Introduction

Performance improvement and surveillance facilitation have become increasingly important in industrial processes. Some shortcomings may limit the application of DNN in fault diagnosis: (1) DNN is mainly used to process the spectrogram of image and speech recognition in computer vision, and in order to extract both spatial and temporal features, the input data in fault diagnosis are usually processed to a two-dimensional data matrix composed of a period of time [29,30,31] It may result in a low real-time performance. E (m − n + 1)-derived instances of each raw instance are input into random forest and completely random forest to generate their class distribution vectors and to concatenate them into transformed feature vector of 2M ∗ (m − n + 1)-dimensional.

WCForest-Based Fault Diagnosis Method

Experiment Result

14 Purge F1

Findings

Conclusions