Vibration Detection Based on Multi-Sensor Information Fusion for Industrial Internet of Things

As science and technology continue to progress, the Industrial Internet of Things (IIoT) is playing an increasingly pivotal role. However, the complexity of the industrial scene has resulted in some IIoT algorithms for vibration detection facing issues such as incomplete waveforms caused by fixed-length data fragments, low accuracy of feature extraction and counting, and poor information fusion effects. To address these challenges and ensure timely identification of industrial equipment faults and the safety of industrial production, this paper proposes a multi-sensor feature fusion algorithm. The algorithm ensures the integrity of the waveform through the detection of the head and tail of the waveform, and aligns the time axis of the multi-sensor, and then uses the method of feature fusion to comprehensively determine the number of vibrations according to various elements such as wave crest, wave width, and average energy, so as to realize the multi-sensor information fusion based on the IIoT. The results show that the algorithm in this paper performs pretty well.