1. Diagnosis Method for Analog Circuit Hard fault and Soft Fault
- Author
-
Jingbing Li, Hengyu Wu, and Baoru Han
- Subjects
Engineering ,Artificial neural network ,business.industry ,Hardware_PERFORMANCEANDRELIABILITY ,Fault (power engineering) ,Electronic circuit simulation ,Fault indicator ,Stuck-at fault ,Control theory ,Fault coverage ,Fault model ,business ,MATLAB ,computer ,computer.programming_language - Abstract
Because the traditional BP neural network slow convergence speed, easily falling in local minimum and the learning process will appear oscillation phenomena. This paper introduces a tolerance analog circuit hard fault and soft fault diagnosis method based on adaptive learning rate and the additional momentum algorithm BP neural network. Firstly, tolerance analog circuit is simulated by OrCAD / Pspice circuit simulation software, accurately extracts fault waveform data by matlab program automatically. Secondly, using the adaptive learning rate and momentum BP algorithm to train neural network, and then applies it to analog circuit hard fault and soft fault diagnosis. With shorter training time, high precision and global convergence effectively reduces the misjudgment, missing, it can improve the accuracy of fault diagnosis and fast. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3301
- Published
- 2013