A relevance vector machine (RVM) based fault diagnosis method was presented for non-linear circuits. In order to simplify RVM classifier, parameters selection based on particle swarm optimization (PSO) and preprocessi...A relevance vector machine (RVM) based fault diagnosis method was presented for non-linear circuits. In order to simplify RVM classifier, parameters selection based on particle swarm optimization (PSO) and preprocessing technique based on the kurtosis and entropy of signals were used. Firstly, sinusoidal inputs with different frequencies were applied to the circuit under test (CUT). Then, the resulting frequency responses were sampled to generate features. The frequency response was sampled to compute its kurtosis and entropy, which can show the information capacity of signal. By analyzing the output signals, the proposed method can detect and identify faulty components in circuits. The results indicate that the fault classes can be classified correctly for at least 99% of the test data in example circuit. And the proposed method can diagnose hard and soft faults.展开更多
Stochastic resonance （SR） phenomenon of the system that is subject to the asymmetric two-state noise is investigated from the broad sense. It is shown that the amplitudes of the output signal exhibit the non-monoton...Stochastic resonance （SR） phenomenon of the system that is subject to the asymmetric two-state noise is investigated from the broad sense. It is shown that the amplitudes of the output signal exhibit the non-monotonic dependence on the input signal frequency w, and the parameters describing the asymmetric two-state noise, such as the transition rate A, the order parameter k describing the asymmetric degree of the two-state noise, and the noise strength D.展开更多
基金Project(Z132012)supported by the Second Five Technology-based in Science and Industry Bureau of ChinaProject(YWF1103Q062)supported by the Fundemental Research Funds for the Central Universities in China
文摘A relevance vector machine (RVM) based fault diagnosis method was presented for non-linear circuits. In order to simplify RVM classifier, parameters selection based on particle swarm optimization (PSO) and preprocessing technique based on the kurtosis and entropy of signals were used. Firstly, sinusoidal inputs with different frequencies were applied to the circuit under test (CUT). Then, the resulting frequency responses were sampled to generate features. The frequency response was sampled to compute its kurtosis and entropy, which can show the information capacity of signal. By analyzing the output signals, the proposed method can detect and identify faulty components in circuits. The results indicate that the fault classes can be classified correctly for at least 99% of the test data in example circuit. And the proposed method can diagnose hard and soft faults.
文摘Stochastic resonance （SR） phenomenon of the system that is subject to the asymmetric two-state noise is investigated from the broad sense. It is shown that the amplitudes of the output signal exhibit the non-monotonic dependence on the input signal frequency w, and the parameters describing the asymmetric two-state noise, such as the transition rate A, the order parameter k describing the asymmetric degree of the two-state noise, and the noise strength D.
