Research On Technique Of Faults Classification With Support Vector Machines For Analog Electronic Circuits

With the development of microelectronics and semiconductor technologies, the integration of the analog electronic circuit system presents with high density and complicated functions. A higher reliability for such a system is required, but the system under consideration is always with low testability. Signal processing and artificial intelligence can be combined together to implement the testing and diagnosis of the analog circuit system at the level of components or sub-system. Such a task is also an interesting and hot subject in the domain of analog circuit testing and fault diagnosis.This paper conducts the research of faults classification for analog electronic circuit under test (CUT) or system under test (SUT), using the technologies of signal processing, i.e. wavelet transformation and feature selection, and pattern classification, i.e. artificial neural network (ANN) and support vector machines classifier (SVC). In our study, several important parts are listed as follows. In some cases of analog circuit fault diagnosis, it is hard to obtain a large number of fault feature samples. Focusing on this problem, we present a novel method of extended set generation for fault feature samples based on cloud model. The artificial neural networks are used to train the extended sets. The experimental results show that the neural classifiers trained with these extended sets are robust to the random noise.Focusing on the problem of fault classifier design, we conduct the research of fault classifier design based on support vector machines classifier (SVC) and, the diagnosis performances of these classifiers are also investigated. In this section, the contributions and the inventions are as follows. In order to evaluate the computational complexity of the SVC qualitatively, we propose a specification of average test calculations. We improve the conventional one-against-rest SVC, and the KNN classifier is introduced in the stage of fault decision. Such a design can improve the performance of the SVC. In order to reduce the testing time of the conventional one-against-rest SVC and improve the testing efficiency, a hybrid classifier based on the fault dictionary (FD) and space distance discriminant is proposed. We also invent a novel SVC, whose training structure is from the training of the self organization feature mapping ANN (SOFM ANN), and this classifier can adaptively find a good training structure according to the distributions of the training samples. This classifier can reduce the diagnosis errors and testing time, and in this part, we also demonstrate the calculation method of average test calculations for this classifier.A single classifier has its drawbacks in diagnosing the analog electronic circuits and in our study, we present a fusion method at the level of decision classifiers. Two classifiers, the ANN and the SVC, are regarded as the sub-classifiers of the system. The fuzzy inference measure is adopted to realize the final decision. The experiments validate the effectiveness of the proposed method and this method is also robust to the random noise.Both of fault feature extraction and selection are crucial to the faults classification of analog electronic system. At present, the feature extraction technique has received many attentions, but the feature selection technique has not been exploited with further research. Focusing on this problem, this paper proposes a selection technique of scalar wavelet features. This novel method can select the proper features for the subsequent one-against-rest SVC, and at the same time, the kernel function parameter is also determined. This technique solves the problem of kernel parameter selection without exhaustive searching, thus, a lot of calculations are saved. In order to speed up the feature selection process, a fast classifier based on the conventional one-against-rest SVC is proposed and this simple classifier can reduce the selection time significantly while good performance can be achieved.In addition to these contents, we also conduct some real experiments. Two systems, based on the personal computer (PC) and the digital signal controller (DSC), are designed, respectively. For the first experimental system, the data collection system is realized with the data acquisition card (DAC) and the Digital Signal Processor (DSP), respectively. For the second system in independence of PC, the software is designed to implement all tasks, such as high-speed data collection, feature extraction based on advanced signal processing and fault decision based on improved SVC. Several real circuits are demonstrated to vindicate the effectiveness and validness of our proposed methods.