Detection of Abnormal Sound of Power Plant Equipment Fault based on Self-supervised Learning

Based on the detection of abnormal sound of equipment based on self-supervision, it can help to solve the detection method of abnormal sound, extract artificially constructed algorithms, identify the characteristics of sound, and detect abnormal sound. In this process, the human factor is relatively large. Due to human factors, the generality of the method is limited, which affects the accuracy of abnormal sound detection of power plant equipment to a certain extent. The steps include the acquisition of sound from power plant equipment, data processing, production of sound samples, identification of abnormal sound and normal sound samples, and application of the trained sound in the system through an encoder. This makes it possible to detect whether the sound is abnormal. This research is mainly based on a self-supervised learning for abnormal sound detection of power plant equipment faults. The specific research is as follows.