Open AccessGas sensor fault diagnosis based on Convolutional Neural Network
Author(s) -
Yong Sun,
Yangyang Liu,
Fang Ji,
Gang Li,
Yanjun Ma,
Li Jin,
Lixin Yang,
Hongquan Zhang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/6/062089
Subject(s) - convolutional neural network , fault (geology) , feature extraction , computer science , feature (linguistics) , artificial intelligence , fault detection and isolation , artificial neural network , pattern recognition (psychology) , sensitivity (control systems) , data mining , engineering , actuator , electronic engineering , geology , linguistics , philosophy , seismology
Gas sensor often fails due to the influence of temperature, humidity, lighting, dust and toxic gases, and causes unreliable phenomena. Therefore, the fault diagnosis of gas sensors is considered as a weak link. Feature extraction and classification play an important role in gas sensor fault diagnosis. However, there are many problems in traditional feature extraction methods. For example, 1) requirements for expert experience, 2) sensitivity of changes in mechanical systems, 3) limitations of new feature extraction. Therefore, it is meaningful and attractive to develop a method that can discover and learn fault-sensitive features of gas sensors from the original data and classify them effectively according to the sensitive features. Convolutional neural network(CNN) has been widely applied in image analysis and voice recording, and has achieved great success. However, gas sensor fault diagnosis is rarely applied. This paper focuses on using CNN to learn fault features from data, extract features automatically, and then classify them effectively. Experiments show that the CNN method provides a effective solution for gas sensor fault diagnosis.