Analysis of early fault vibration detection and analysis of offshore wind power transmission based on deep neural network

Abstract

Among the main fault locations of wind turbines, downtime accidents caused by bearing faults account for the majority. Therefore, it is of practical significance to detect the fault signal of the fan bearing as early as possible, and to arrange the maintenance plan reasonably, which is of practical significance to reduce the maintenance cost and increase the power generation time. Therefore, this paper proposes an early fault vibration detection and analysis model for wind power transmission based on deep neural networks and sensors. First of all, this paper studies the failure mechanism, vibration mechanism and signal characteristics of common failures of rolling bearings, combined with sensor technology, summarises and designs various bearing failures and failure signal detection models. Secondly, the deep neural network algorithm is used for the interference situation of rolling bearing vibration signal and the process of data re-analysis. The experimental and simulation results verify the adaptability of the early fault vibration detection model of wind power transmission in the early fault processing results, which can provide a reference for the early weak fault signal processing of wind turbine bearings.