RideNN: A New Rider Optimization Algorithm-Based Neural Network for Fault Diagnosis in Analog Circuits

• Published in: IEEE transactions on instrumentation and measurement Vol. 68; no. 1; pp. 2 - 26
• Main Authors: Binu, D., Kariyappa, B. S
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analog circuits; Artificial neural networks; Bhattacharyya coefficient; Circuit design; Circuit faults; Circuits; classifier; Electronic circuits; False alarms; Fault diagnosis; Integrated circuit modeling; Low noise; Neural networks; Optimization; Optimization algorithms; orthogonal transformation; Power converters; Racing; Test procedures; Transistor amplifiers
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2018.2836058

Fault diagnosis in electronic circuits is an emerging area of research, where fully automated diagnosis systems are being developed for the investigation of the circuits. Developing test methods for the diagnosis of faults in analog circuits is still a complex task. Consequently, a technique for the fault diagnosis in analog circuits is designed by proposing a new optimization algorithm, named, rider optimization algorithm (ROA). The development of ROA is based on a group of riders, racing toward a target location. Moreover, a classifier, termed RideNN, is developed by including the proposed algorithm as the training algorithm for the neural network (NN). RideNN, along with the orthogonal transformation and Bhattacharyya coefficient, is applied for the fault diagnosis of analog circuits. The proposed technique is experimented using three basic circuits, such as triangular wave generator (TWG), low noise bipolar transistor amplifier (BTA), and differentiator (DIF) and an application circuit, solar power converter (SPC). The performance is evaluated using two evaluation metrics, namely, accuracy (ACC) and false alarm ratio (FAR). The analysis results show that the proposed technique attains an ACC of 99.9% in TWG, 99.9% in BTA, 99% in DIF, and 95% in SPC without noise.