Sequential Fault Diagnosis Based on LSTM Neural Network

Abstract

Fault diagnosis of chemical process data becomes one of the most important directions in research and practice. Conventional fault diagnosis and classification methods first extract features from the raw process data. Then certain classifiers are adopted to make diagnosis. However, these conventional methods suffer from the expertise of feature extraction and classifier design. They also lack the adaptive processing of the dynamic information in raw data. This paper proposes a fault diagnosis method based on long short-term memory (LSTM) neural network. The novel method can directly classify the raw process data without specific feature extraction and classifier design. It is also able to adaptively learn the dynamic information in raw data. First, raw process data are used to train the LSTM neural network until the cost function of LSTM converges below certain predefined small positive value. In this step, the dynamic information of raw process data is adaptively learned by LSTM. Then testing data are used to obtain the diagnosis results of the trained LSTM neural network. The application of LSTM to fault identification and analysis is evaluated in the Tennessee Eastman benchmark process. Extensive experimental results show LSTM can better separate different faults and provide more promising fault diagnosis performance.

Keywords

• Process monitoring
• fault diagnosis
• recurrent neural network
• long short-term memory (LSTM) neural network