Zhen Jia1, Zhenbao Liu1, Yanfen Gan2, Chi Man Vong3 and Michael G. Pecht 4
1 Northwestern Polytechnical University, 26487 Xi'an China 710072
2 Guangdong University of Foreign Studies South China Business College, 442167 Guangzhou, Guangdong China
3 Dept. of Computer and Information Science, University of Macau, Taipa Macao
4 Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA
Electronics-rich analog systems are difficult to diagnose owing to their complex working mechanisms and the variability of the working environment. In recent years, deep learning has been gradually applied to the field of circuit system fault diagnosis because of its strong ability to mine the intrinsic characteristics of signals. However, the traditional deep learning method requires a lot of effort to achieve satisfactory results due to large number of parameters, complex models, slow training speed, and large datasets. The key factors for the success of traditional deep learning methods are layer-by-layer processing, feature transformation within the model, and sufficient model complexity. Deep forest (DF) is a new feature learning model that inherits the three characteristics of the traditional deep learning model but is that it is not based on neural network. It has fewer hyper-parameters, a simpler model, faster training speed. In this paper, an improved deep forest algorithm based on nonparametric predictive inference (NPI) is proposed, named NPIDF, which can better deal with small sample data. In two typical analog filter circuit fault diagnosis experiments, it is proved that DF and NPIDF achieve good diagnosis effect, and NPIDF performance is better, showing a greater advantage in small sample data.