The equipment condition monitoring and fault prediction technology plays an increasingly important role in industrial equipment health management with the development of science and technology on instrument measurement and analysis, Internet of things, cloud computing, data mining, artificial intelligence. This paper studies a condition monitoring and fault prediction technology based on stress wave analysis. The electronic signal of friction, mechanical shock and dynamic load on equipment moving parts are detected and processed by stress wave sensor. The stress wave analysis is fulfilled by using the time domain and frequency domain feature extraction software and sensor data fusion is conducted based on neural network. The equipment states are quantitatively analyzed and the equipment fault is accurately predicted, so as to provide the equipment health diagnosis reports. The test shows that, compared with the traditional vibration analysis, the proposed system can monitor the equipment operation condition better in real time, predict the fault earlier. The production safety can be guaranteed, the equipment maintenance cost can be reduced, and the production efficiency can be improved.