Diagnosis of Incipient Faults in Weak Nonlinear Analog Circuits
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Diagnosis of Incipient Faults in Weak Nonlinear Analog Circuits Yibing Shi · Yong Deng · Wei Zhang
Received: 22 July 2011 / Revised: 28 March 2013 © Springer Science+Business Media New York 2013
Abstract Aiming at the problem to diagnose incipient faults in weak nonlinear analog circuits, an approach is presented in this paper. The approach calculates the fractional Volterra correlation functions beforehand. The next step is to use the fractional Volterra correlation functions and different angle parameters of the fractional wavelet packet transform (FRWPT) to extract the fault signatures. Meanwhile, the computational complexity is analyzed. Then the variables of the fault signatures are constructed, which are used to form the observation sequences of the hidden Markov model (HMM). HMM is used to accomplish the fault diagnosis. The simulations show that the presented method can significantly improve the incipient fault diagnosis capability. Keywords Weak nonlinear circuits · Incipient fault · Fault diagnosis · Volterra series · Fractional correlation 1 Introduction With the rapid development of electronic technology, the problem on how to reduce the overheads of testing mixed-signal circuits has received more attention. It is reY. Shi · Y. Deng · W. Zhang School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China Y. Shi e-mail: [email protected] W. Zhang e-mail: [email protected] Y. Deng () School of Electronics and Information Engineering, Southwest Petroleum University, Chengdu 610500, China e-mail: [email protected]
Circuits Syst Signal Process
ported that 80 % of the test cost in mixed-signal circuits is expended in the analog segment testing [16]. Therefore, the research on the diagnosis of analog circuits has become important. Traditional methods to diagnose the fault of analog circuits are suitable for those faults that have occurred significantly [11, 17, 25]. The fault dictionary method [4] compared the fault values of the unknown conditions of the circuit under test (CUT) with those stored in the dictionary to match one of the predefined faults according to preset criteria, and is one of the most used methods. With the development of a modern signal process, many modern technologies such as neural network, wavelet, fuzzy math, etc. are widely used in the field of fault diagnosis. These approaches are used for failure detection and isolation (FDI). The neural network [2, 3, 18] is a popularly technique to build up a fault dictionary. In [27, 32], fuzzy analysis and sensitivity analysis are used together to diagnose soft faults in linear analog circuits. To obtain the differences between the faulty circuit response and fault-free circuit response, wavelet technology is introduced in [22]. In recent years, the researchers have paid their attention on the preventive maintenance (PM) [30], which is used to predict the fault before it degrade the performance of the CUT by state monitoring. Time-based and condition-based maintenance are two major a
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