Analog circuit fault diagnosis based on Quantum Clustering based Multi-valued Quantum Fuzzification Decision Tree (QC-MQFDT)

Analog circuit fault diagnosis is challenging due to the parametric deviation and the difficulty in signal quantizing. There still lacks effective approaches to provide reliable fault detection and classification results for a comprehensive diagnosis. In this paper, we propose a fault diagnosis methodology based on a new classification model called Quantum Clustering based Multi-valued Quantum Fuzzification Decision Tree (QC-MQFDT). QC-MQFDT incorporates the adaptive fuzzification method to discretize continuous-valued data. The fuzzification mechanism is devised by incorporating quantum clustering (QC) as well as the quantum membership function (QMF), where the former has the ability to sense the internal dependencies of data, and the latter uses the number of energy levels to approximate the optimal shape for fuzzy membership functions. The QF-C4.5 algorithm is developed as the decision tree learning algorithm, which employs quantum fuzzy entropy (QFE) to evaluate the information in the target variable space. The proposed method is validated using both simulated data and the real time data for the application studies of two benchmark analog circuits. The classification performances are discussed and the diagnostic capability of the model is verified through the application studies.