A Fault Prediction and Cause Identification Approach in Complex Industrial Processes Based on Deep Learning

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 6612342
• Main Author: Li, Yao
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Analysis; Big Data; Coders; Cost control; Data processing; Deep learning; Encoders-Decoders; Fault detection; Fault diagnosis; Geospatial data; Industrial production; Long short-term memory; Machine learning; Methods; Neural networks; Predictions; Principal components analysis; Production processes; Sensors; Spatial data; Time series; Weight; Tennessee
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/6612342

Faults occurring in the production line can cause many losses. Predicting the fault events before they occur or identifying the causes can effectively reduce such losses. A modern production line can provide enough data to solve the problem. However, in the face of complex industrial processes, this problem will become very difficult depending on traditional methods. In this paper, we propose a new approach based on a deep learning (DL) algorithm to solve the problem. First, we regard these process data as a spatial sequence according to the production process, which is different from traditional time series data. Second, we improve the long short-term memory (LSTM) neural network in an encoder-decoder model to adapt to the branch structure, corresponding to the spatial sequence. Meanwhile, an attention mechanism (AM) algorithm is used in fault detection and cause identification. Third, instead of traditional biclassification, the output is defined as a sequence of fault types. The approach proposed in this article has two advantages. On the one hand, treating data as a spatial sequence rather than a time sequence can overcome multidimensional problems and improve prediction accuracy. On the other hand, in the trained neural network, the weight vectors generated by the AM algorithm can represent the correlation between faults and the input data. This correlation can help engineers identify the cause of faults. The proposed approach is compared with some well-developed fault diagnosing methods in the Tennessee Eastman process. Experimental results show that the approach has higher prediction accuracy, and the weight vector can accurately label the factors that cause faults.