Fan is widely used in various industrial fields and it plays a key role in cooling the machinery. For the machinery to work properly, the fan system should remain in stable and error-free condition. Condition monitoring, as a maintenance tool, is introduced to fan system’s fault diagnosis.

Many available methods are used in condition monitoring, such as vibration monitoring and thermal monitoring. The vibration monitoring method was used in the experiment. A fan system based on Machinery Fault Simulator™ (MFS) was used to simulate fan’s different conditions in the laboratory. An accelerometer was installed on the top of the bearing housing. It was used to detect the vibration signal of the fan when it was working. A data acquisition program designed in LabVIEW was used to record and preprocess the raw vibration signal. The collected data was used to detect the condition of the fan system.

Neural network was used for the fault diagnosis. The raw vibration signal is a one-dimensional time domain series data, while the neural network requires multidimensional features as input data. Therefore, it is important to preprocess the raw vibration signal data. Two different preprocessing methods, time-domain features and Auto Regressive (AR) model features were used to preprocess separately. The neural network model was trained by these two methods respectively. The results showed that the AR model gave better features than that by the time domain features method.

The condition monitoring system consisted of the following parts:​ data acquisition, data storage, data preprocessing and the display of results. Some methods were programmed in Matlab, which were called by Matlab scripts in the LabVIEW software. The hybrid programming method helped to generate an efficient program which provided high accuracy of fault diagnosis.