Rider-Deep-LSTM Network for Hybrid Distance Score-Based Fault Prediction in Analog Circuits

Abstract

Fault prediction in the analog circuits is a serious problem to be addressed on an immediate basis, as traditionally, the faults in the analog circuits are diagnosed only after their occurrence. Since the outcome of the faults creates highly expensive scenarios in case of the analog circuit industry, there is a need for an effective prediction model that keeps track of the faults prior to their occurrence. Accordingly, this article focuses on the fault prediction model in analog circuits using proposed deep model called, Rider-deep-long short-term memory (LSTM). Here, the significance and precision of the prediction relies on the fault indicator, which is computed based on three distance measures, such as mahalanobis distance, Euclidean distance, and angular distance, and thereby, enables an effective health estimation of the circuit. The estimation is effectively solved using the Rider-deep-LSTM, which is the integration of proposed Rider-Adam algorithm in deep-LSTM, for training the model parameters. The proposed prediction method acquires the Pearson correlation coefficient of 0.9973 and 0.9919 while using the circuits, such as solar power converter and low noise bipolar transistor amplifier.