Analog circuit intermittent fault diagnosis method based on multi-granularity cascade forest

Abstract

The invention provides an analog circuit intermittent fault diagnosis method based on a multi-granularity cascade forest. In order to solve the problem that the traditional fault diagnosis method is not suitable for large-scale complex integrated circuits at the present stage, and the tntermittent fault data is small, the characteristic that a multi-granularity cascade forest utilization algorithmis adopted to well express small data is adopted to classify different types of fault data so as to diagnose the intermittent faults of an analog circuit. According to the method, a deep neural network layer-by-layer cascade structure is adopted; the depth of a cascade layer and the number of random forests and complete random forests in each layer can be adjusted according to actual conditions,small-scale intermittent fault data can be trained to achieve a good result, the requirement of deep learning for a large-scale data set is avoided, and meanwhile the detection effect comparable to that of a deep learning algorithm can be achieved.

Description

technical field [0001] The invention relates to the fields of electronic information science and technology and machine learning, in particular to the intermittent fault diagnosis of an analog circuit by using a multi-grain cascade forest. [0002] technical background [0003] With the continuous development of electronic technology and computer technology, the structure of equipment is becoming more and more complex, and the miniaturization of various components has also led to a rapid increase in its scale. The current large-scale use of analog circuits, the system testing and board The diagnosis of level intermittent faults puts forward more urgent needs and higher requirements, in order to detect and classify them before circuit faults occur, and avoid a lot of losses. According to reports, 80% of circuit equipment faults come from the analog part. Although the traditional method has achieved certain results, the response signal is processed by wavelet transform, wavelet...

AI Technical Summary

Problems solved by technology

In recent years, deep learning has attracted widespread attention due to its strong feature extraction capabilities. However, one of the major requirements of deep learning algorithms is the need to use large-scale data to train algorithm models. Using traditional methods to detect intermittent faults is too costly and highly automated. is not high, so it is particularly important to adopt a new method for detecting intermittent faults in analog circuits

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