An unsupervised spatiotemporal fusion network augmented with random mask and time-relative information modulation for anomaly detection of machines with multiple measuring points

In industrial environments, individual sensor is easily affected by background noise, etc. In order to improve the reliability of anomaly detections, sensors are arranged at multiple measuring points to collect monitoring data of machines. However, under the coupling of vibration responses of multiple components of machines, the complex nonlinear relationship between monitoring data of multiple measuring points makes it difficult to achieve the best feature extraction and fusion effect, which reduces the accuracy of anomaly detection. To solve this problem, an unsupervised spatiotemporal fusion network augmented with random mask and time-relative information modulation is proposed. Firstly, we creatively propose random mask and modulated signal generation method based on mask index to learn the dependence of waveform and time dimension and achieve temporal dimension fusion of signals. Based on end-to-end training, modulated signals are also more conducive to spatial fusion. Then, to fully exploit the correlation between monitoring data of multiple measuring points and obtain the best spatial dimension fusion effect, a multi-head graph neural network based on self-attention weight matrix is carried out. Finally, we use transformer encoder to reconstruct the signal of each measuring point and obtain reconstruction error. Based on exponentially weighted moving average, anomaly detection threshold is obtained. Two anomaly detection experiments are conducted, and accuracy of 99.78%, 99% are achieved.