Study of machine fault diagnosis system using neural networks

Develops a machine fault diagnosis system using neural networks and spectral analysis. A neural network is applied to the fault diagnosis of the machine. The neural network has learning and memory capability. By the learning of normal and abnormal states of the object system, a method with neural networks is proposed which can diagnose a fault of the machine. The proposed fault diagnosis system is based on the spectrum of vibrations or sounds obtained from the operating machine. The difference between normal and abnormal data becomes clearer when comparing time series data. It is suitable for the detection of the fault to utilize changes of spectral data. Using this method, it is shown that it can detect unknown fault patterns. Fault diagnosis experiments are performed on both a wood slicing machine and an electromagnetic valve. The possibility of an online fault diagnosis system is examined through the construction of an online data processing system for an electromagnetic valve and it is shown that the fault diagnosis can be performed in real time. Through these results, the effectiveness of the proposed fault diagnosis system is verified