Anomaly Detection in Mechanical Vibration Using Combination of Signal Processing and Autoencoder

抄録

The goal of this research is to detect abnormal behavior in vibration data of factory machinery and take measures against failure. In this study, an accelerometer is mounted on a mechanical device with a conveyor belt. Vibration data are collected to monitor the conveyor belt line. We propose a method of detecting vibration abnormalities by combining signal processing and an autoencoder (AE), one of the neural network models. In the learning phase, the vibration signal is converted to a spectrogram and used for the output of the neural network. Then, a random mask is applied to the horizontal direction of the spectrogram. This technique does not require a search for a valid frequency band. The masked spectrogram is used as the input for the neural network. A model that converts the masked spectrogram back to the original spectrogram is trained. This model is a type of AE, a deep convolutional encoder-decoder architecture. In the detection stage, the masked spectrogram is input to the model and a predicted image is obtained. The predicted images are evaluated with the weighted moving variance of the PSNR. In an experiment, a normal AE and two masked AEs were compared. Both the AE and the proposed method can detect faults, and it was shown that other faults can be detected by a masked AE.