Analogue circuit fault diagnosis method based on neural network

Abstract

The invention discloses a method for diagnosing fault of an analog circuit based on a nerve network. The method is characterized in that the method comprises the following steps: (1) an electrical signal of the analog circuit is acquired; the electrical signal is testable node voltage of the analog circuit or a current signal of a branch circuit; (2) the acquired electrical signal is subjected to centralized processing; (3) signal entropy value and signal kurtosis of the electrical signal are calculated; and (4) the signal entropy value and the signal kurtosis are sent to a BP nerve network separator, and a fault diagnosis result is output by the BP nerve network separator. The method can be used for a real-time system and a nonreal-time system and can be also used for diagnosing hard fault and soft fault.

Description

technical field [0001] The invention belongs to the field of neural network and electronic circuit engineering, and relates to a fault diagnosis method for an analog circuit based on a neural network. Background technique [0002] In analog circuits, faults can be divided into two categories: one is called hard fault, which refers to the open circuit and short circuit failure of components; the other is called soft fault, which means that the parameters of components exceed the predetermined tolerance range, generally they None of the equipment completely fails, for example, due to the aging of components, deterioration or changes in the use of environment, etc. caused by changes in component parameters. [0003] Since the testing and diagnosis of analog systems began to be studied in the 1960s, the progress has been relatively slow. The main reason is that the input excitation and output response of analog circuits are continuous quantities, and the parameters of each compo...

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Problems solved by technology

[0003] Since the testing and diagnosis of analog systems began to be studied in the 1960s, the progress has been relatively slow. The main reason is that the input excitation and output response of analog circuits are continuous quantities, and the parameters of each component in the network are usually continuous, that is, most faults The situation is a soft fault, so the fault model in the simulation system is more complicated, and it is difficult to make simple quantification

Since the fault parameters are also continuous, theoretically speaking, an analog component may have infinitely many faults, so testing and diagnosis are far more difficult than digital systems, so it is not yet fully mature in theory and methods, and can be paid There are few practical ones

[0004] In the past few years, a lot of research has been done on the fault diagnosis of analog circuits from the system level, the board level to the chip level, and some methods directly intercept different test point signals as the input of the neural network classifier for fault diagnosis. The network size of this method is usually very large, the network training time is very long, and it is difficult to meet the real-time requirements

Some methods use wavelet transform as the signal feature extraction technology. Since the wavelet coefficient itself has a large amount of calculation, when there is no good algorithm, this method is also not practical for real-time environments.

Some use the fault dictionary method to establish a DC or AC fault dictionary of the circuit, but this method is generally only applicable to hard faults, and is helpless for soft faults

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