A New Hydrogen Sensor Fault Diagnosis Method Based on Transfer Learning With LeNet-5 - Sun, Yongyi; Liu, Shuxia; Zhao, Tingting; Zou, Zhihui; Shen, Bin; Yu, Ying; Zhang, Shuang; Zhang, Hongquan (Frontiers in Neurorobotics, 2021)
The fault safety monitoring of hydrogen sensors is very important for their practical application. The precondition of traditional machine learning (ML) methods for sensor fault diagnosis is that enough fault data with the same distribution and feature space under the same working environment must exist. However, widely used fault diagnosis methods are not suitable for real working environments because they are easily interfered by environmental conditions such as temperature, humidity, shock, and vibration. Under the influence of such complex conditions, the acquisition of sensor fault data is limited. In order to improve fault diagnosis accuracy under complex environmental conditions, a novel method of transfer learning (TL) with LeNet-5 is proposed in this paper. Firstly, LeNet-5 is applied to learn the features of the data-rich datasets of gas sensor fault in a normal environment and to adjust the parameters accordingly. The parameters of the LeNet-5 are transferred from the task in the normal environment to a task in a complex environment by using the TL method. Then, the migrated LeNet-5 is used for the fault diagnosis of gas sensors with a small amount of fault data in a complex environment. Finally, a prototype hydrogen sensor array is designed and implemented for experimental verification. The experimental results show that the method adopted presents an excellent solution for the fault diagnosis of a hydrogen sensor using a small quantity of fault data obtained under complex environmental conditions.