A Fault Diagnosis Of Sub-network In Large-scale Analog Circuits Using Neural Network

In this paper a fault diagnosis of sub-networks in large-scale analog circuits using neural network is proposed. The author proposes an intact diagnosis technique and its implement scheme on the basis of the existing knowledge. The computer simulations validate this approach's swiftness and nicety. We expand circuits from easy to difficult aiming at how to simulate and analysis most circuits. This method is presented by collecting much information; it is also a novelty and an in-depth study. It is a new grope for analog circuits fault diagnostic technique. This method is so promising and widely applicable that it provides a new theoretic proof and detection approach. It may be executed to a high-efficient facility.With the rapid development and application of the LSI technology, in order to maintain various components and devices, we must detect the fault by the aid of computer. So study on a fault diagnostic approach is a significant subject on both theory and application. Electronic systems are getting more and more complicated with the rapid development of microelectronics techniques. An electronic system contains a large number of integrated circuit components and separate elements so that it becomes more and more difficult to detect those complicated systems and devices. The traditional approaches of fault location are performed only if the faults of the circuits are those hard faults,such as open-circuit,short-circuit,etc. Those soft faults aroused by the tolerance of circuit components cannot be easily discovered. At present, few references present a systematic and effective method on the soft faults. That is the capital mission of research work in the dissertation.In recent years, artificial neural network as a newest technique of the information soft processing is applied widely in the field of diagnosis. For the capabilities of self-study, robust, self-adaptive, associate memory and nonlinear mapped traits, people pay much attention to its diagnostic technique. But neural network's accuracy relies heavily on the effective of the features and the reasonable choose for the parameter of the network's framework. Wavelet transform is a fire-new time-frequency planar analytic technique. Its unique merit to high frequency burst signal and low frequency lazy signal is that it can detect the burst faulty signal using its different behavior to different scales. It can acquire the eigenvectors as few as possible on the condition that it can show the fault behavior. If the data is not much, wavelet transform can be very fast, so wavelet transform can be used at real time. If the features extraction of wavelet transform combines with the pattern recognition of neural network to diagnose the fault,it will be a promising method. The author just proposes a method based on this idea. The research work in this paper is divided into two sections: Section1 discusses the theoretical basis and implement approach of fault diagnosis of analog circuits based on neural network. In this section, firstly, the basic knowledge of wavelet transform and neural network is introduced, and then the analyzing means of the circuit fault diagnostic system is proposed, and its feasibility is analyzed. In section2, the large-scale analog circuits fault diagnostic method is described elaborately. This section confirms the detection method at first, and then introduces the theory, makeup and design of the method. At last, circuit simulation by using computer is presented, conclusions are given that it is an effective method. BP algorithm encounters local minimum,slow convergence speed and convergence instability, so we adopt a means based on Levenberg-Marquardt algorithm joint with momentum algorithm to train a neural network and obtain the network weights in this paper, forming robust network architecture. For the large-scale analog circuit, the workload of the traditional method is very large, which has many components and need many combinations. On the point, we tear the network in this paper based on the substitution the...