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The Application of Machine Learning to Fault Diagnosis of Analog Circuits

Author: XieZuoZuo
Tutor: DingXiangQian
School: Ocean University of China
Course: Signal and Information Processing
Keywords: Machine Learning Pattern Recognition Analog circuit fault diagnosis Model assessment
CLC: TN710
Type: Master's thesis
Year: 2009
Downloads: 105
Quote: 0
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The analog circuit due to tolerance , non-linear and difficult to model the characteristics of fault diagnosis is extremely difficult . Therefore, the fault diagnosis of analog circuits is a challenging research topic . Ultra- deep submicron semiconductor technology progress , and promote the development of ultra-large-scale analog circuits and analog-to-digital hybrid circuits , analog circuit fault diagnosis new challenges , the traditional theory and method of fault diagnosis has been difficult to deal with . As a branch of computational intelligence technologies - machine learning , and provides an effective way for analog circuit fault diagnosis , has drawn wide attention and attention . Characteristics of analog circuits , the use of machine learning algorithms for analog circuit fault diagnosis . Diagnostic methods to explore from engineering practice , a diagnostic system based on pattern recognition theory , and use a series of model evaluation criteria and objective assessment of the diagnostic performance of the machine learning model . How to start from the selected test circuit fault set selection , Monte Carlo simulation method to solve the circuit tolerance , the PCA - based feature extraction and applications of machine learning algorithms are discussed in detail on each link . And then based on the diagnosis of a systematic process , making the machine learning algorithms can be successfully applied to the automatic fault diagnosis of analog circuits industrialization . This program can adapt to and tolerance of analog circuits , noise , nonlinear and difficult to model . Followed by the use of a series of model evaluation criteria established diagnostic model representative of machine learning algorithms to carry out an objective and fair assessment . Currently, the learning algorithm comprehensive comparison study is relatively small . Different areas of learning algorithms have different evaluation criteria . Finishing the evaluation criteria suited to the field of fault diagnosis , decision tree , neural network and support vector machine algorithm application in the field of fault diagnosis to make the assessment . Two international standard circuit fault simulation results , the performance of three machine learning algorithms modeling review.

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Basic electronic circuits > Electronic circuit
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