Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
The Journal of Machine Learning Research
Historical consistent complex valued recurrent neural network
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Integrated Model-Based and Data-Driven Diagnosis of Automotive Antilock Braking Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Design of signal-adapted multidimensional lifting scheme for lossy coding
IEEE Transactions on Image Processing
Stability Analysis and the Stabilization of a Class of Discrete-Time Dynamic Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Brushless DC (BLDC) machines are found increasing use in applications that demand high and rugged performance. In some critical circumstance, such as aerospace, the motor must be highly reliable. In this context, a novel model-based fault diagnosis system is developed for brushless DC motor speed control system. Under the consideration of the complexity of characterizing the dynamic of BLDC motor control system with analytic expression, a LRGF neural network (LRGFNN) with pole assignment technique is carried out for modeling the system. During the diagnosis process, fault signal of the motor is isolated with LRGFNN online. Meanwhile, adaptive lifting scheme and adaptive threshold method are presented for detecting the faults from the isolated fault signal under the existence of mechanical error and electrical error. The effectiveness of the diagnosis system is demonstrated in the simulation of electrical and mechanical fault in the motor. The detection of the incipient fault is also given.