00000cab a22000003a 4500 UP-99796217609517390 Buklod 20231007234350.0 m |o d | ta 101103s xx d | ||r ||||| || DENGII eng Wang, W. A neuro-fuzzy approach to gear system monitoring. pp. 710-723 The detection of the onset of damage in gear systems is of great importance to industry. In this paper, a new neuro-fuzzy diagnostic system is developed, whereby the strengths of three robust signal processing techniques are integrated. The adopted techniques are: the continuous wavelet transform (amplitude) and beta kurtosis based on the overall residual signal, and the phase modulation by employing the signal average. Three reference functions are proposed as post-processing techniques to enhance the feature characteristics in a way that increases the accuracy of fault detection. Monitoring indexes are derived to facilitate the automatic diagnoses. A constrained-gradient-reliability algorithm is developed to train the fuzzy membership function parameters and rule weights, while the required fuzzy completeness is retained. The system output is set to different monitoring levels by using an optimization procedure to facilitate the decision-making process. The test results demonstrate that the novel neuro-fuzzy system, because of its adaptability and robustness, significantly improves the diagnostic accuracy. It outperforms other related classifiers, such as those based on fuzzy logic and neuro-fuzzy schemes, which adopt different types of rule weights and employ different training algorithms. Beta kurtosis. Constrained gradient reliability algorithm. Continuous wavelet transform. Fault detection. Fuzzy logic. Gear system monitor. Neuro fuzzy diagnostic system. Phase modulation. Robust signal processing. IEEE Transactions on fuzzy systems 12, 5 (2004). FO UPD DENG-II Article