Fault Detection And Diagnosis Based On Simulation Of Circuits

The theory of analog circuits fault detection and diagnosis is always an important, significant, and challenging topic. After developing for over forty years, analog circuits fault diagnosis has formed a series of theories and have many methods. However, the complicated theory and poor practicability of these methods make these methods' application foreground far from expectation. The traditional methods of fault diagnosis are performed only if the faults of the circuits are those hard faults, such as open-circuit, short-circuit, etc. The soft faults can't be easily detected. Recent years, Artificial Intelligence (AI) and signal analysis have developed quickly and have been widely applied in fault diagnosis of analog circuits with soft faults.In this paper, a analog circuits fault detection and diagnosis based on simulation of circuits is studied. The major work of this paper is:(1)Introduce a new plan of analog circuits fault detection and diagnosis based on simulation of circuits. First, the software simulation of analog circuits is implemented entirely by computer. All signal waveforms of every test point in circuits are figured out by simulation software. By using the perfect reference signal to research the analog circuits fault detection and diagnosis.(2)In this paper, the PSPICE software is implemented to realize the simulation of the under detecting circuits. In this way, the standard waves of the under detecting points on the circuits can be gotten under the situation that the components on the circuit work in the normal way. The paper also use PSPICE to analysis the most sensitive components to the output of the under detecting point and determine the fault pattern. The paper also gets the sample signals in all default patterns including the normal working pattern by the using of Monte Carlo analysis. The differences between the sample signals and the standard signals are used to carry on Multi-Resolution Analysis and get the feature vectors for pattern recognition. At last, the paper investigates region threshold theory to recognize the normal signals and fault signals. On the construction of BP neural network, the paper develops single BP neural network and multi-BP neural networks to diagnosis the circuits.(3)The paper takes a band-pass filter with source as example, makes experiments on it to test the fault detection and diagnosis systems. The experiment results show that, the fault detection and diagnosis based on simulation of circuits is effective in the fault of single testable point, single soft fault condition.