Design and application of PFM neural network VLSI
脉频调制神经网络VLSI的设计及应用

This paper presents an improved pulse frequency modulation (PFM) neural network VLSI circuit for fault diagnosis in order to improve the software - based fault diagnosis approach and to give full play to the merits of neural network VLSI circuit. By using the single-level perception network and the field effect transistor circuit, a new digital/analogue based synapse multiplier/adder is designed so that the threshold of the synapse need not be adjusted by learning and hence the circuit becomes less complicated. Based on this circuit, a neural network fault detection system is designed for the noise based fault diagnosis of main bearing. Through signal processing, extraction of fault feature, and neural network computation for the original noise signal containing fault information,the output capacitor voltage value of the VLSI circuit is derived. This value, representing the Euclidean distance between the template fault signal and the signal to be recognized is used to detect the fault. The simulation test shows that the recognition capability of the hardware-based diagnostic system is close to that of a software-based one.