Gas sensor fault diagnosis based on Convolutional Neural Network

Abstract

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.