A Deep Forest-Based Fault Diagnosis Scheme for Electronics-Rich Analog Circuit Systems

Zhen Jia, Zhenbao Liu, Yanfen Gan, Chi Man Vong, Michael Pecht

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

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 hyperparameters, a simpler model, faster training speed. In this article, an improved DF 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.

Original languageEnglish
Article number9186310
Pages (from-to)10087-10096
Number of pages10
JournalIEEE Transactions on Industrial Electronics
Volume68
Issue number10
DOIs
StatePublished - Oct 2021

Keywords

  • Analog circuits
  • deep forest (DF)
  • diagnosis
  • failure
  • fault

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