A process fault diagnosis method using multi‐time scale dynamic feature extraction based on convolutional neural network

Abstract

Unlike many other techniques used in process control, which are widely applied in practice and play significant roles, abnormal situation management (ASM) still relies heavily on human experience, not least because the problem of fault detection and diagnosis (FDD) has not been well addressed. In this paper, a process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network (CNN) consisting of similarity measurement, variable ranking, and multi-time scale dynamic feature extraction is proposed. The CNN-based model containing the fixed multiple sampling (FMS) layer can extract dynamic characteristics of process data at different time scales. The benchmark Tennessee Eastman (TE) process is used to verify the performance of the proposed method. K E Y W O R D S convolutional neural network, dynamic feature extraction, fault diagnosis, TE process

A Review on Data-Driven Process Monitoring Methods: Characterization and Mining of Industrial Data

“…The success of the neural network method has brought the development of artificial intelligence into a new era. Convolutional neural networks have been widely applied for fault classification and diagnosis [56,57]. These classification models require a large number of labeled historical fault data, while fault data are usually unavailable in industrial processes and the fault type that occurs in real time production may not be contained in a historical database.…”

“…The simplest way is to employ a single variable fault diagnosis method, for example, each measurement variable will be preset to an operating range in DCS, when a fault occurs, the variables that exceed the defined normal range are considered as the root cause. Gao et al applied Euclidean distance between the normal state and real time state to determine whether the variable is normal or not [57]. Ji et al proposed a fault diagnosis method, by which the variations in information entropy of each variable sequence was applied to identify variables with abnormal deviations [188].…”

Application of Artificial Neural Networks to Chemical and Process Engineering

“…Generally, deep neural networks -ANNs that contain several hidden layers -are used to extract spatial and temporal aspects of the data for this purpose [65]. Their inputs are the sensors responsible for the variable measurement, and their outputs of the kind of faults (e.g., tube plugging, valve blockage, catalyst deactivation, among others) [66]. However, determining the various hyperparameters of deep neural networks demands a considerable amount of time, which is not suitable for fast online process applications.…”

A Quadruplet Deep Metric Learning model for imbalanced time-series fault diagnosis

“…As sensing technology and data storage technology promoting, massive data are recorded in production process. Therefore, in the field of fault diagnosis, many classical and effective models in deep learning were verified to have good performance such as Deep Belief Network (DBN) [10,11,12], Autoencoders (AE) [13,14,15], Convolutional Neural Network (CNN) [16,17,18]. Considering the fault signals are not independent of each other, recurrent neural network (RNN)-based methods were proposed and achieve performance improvement on dynamic time-series fault signals.…”