Real-time residual life prediction method of gear based on multi-degradation monitoring

Abstract

The invention relates to a real-time residual life prediction method of a gear based on multi-degradation monitoring, and belongs to the technical field of mechanical reliability. The method comprisesthe implementation steps of: (1), monitoring degradation of a gear in a main test gear box in real time by utilizing an acceleration sensor and a noise sensor; (2), performing characteristic extraction and recession evaluation on the degradation state of the gear; (3), respectively modelling the vibration acceleration and noise of the gear box by adopting kernel estimation and random filtering theory methods, obtaining the residual life probability density function of the gear box, and obtaining a single-degradation residual life edge distribution function; (4), representing the random correlation between the vibration acceleration and the noise of the gear box by utilizing a Copula function, and obtaining a joint distribution function of the residual life of the gear box; and (5), obtaining a residual life joint probability density function thereof according to the residual life joint distribution function of the gear box, and finally, obtaining the residual life prediction value ofthe gear box. The method disclosed by the invention has the advantages that: the degradation state and the real-time residual life of the gear are effectively predicted; and basis is provided for preventative maintenance of the gear.

Description

technical field [0001] The invention belongs to the field of mechanical reliability, and in particular relates to a real-time residual life prediction method for gears with multi-degradation monitoring. Background technique [0002] Gears are the key components in the transmission system widely used in the machinery industry; when the gears have broken teeth, tooth surface fatigue, gluing and other failures, it will often cause catastrophic damage to the entire mechanical equipment. Taking wind turbines as an example, gear failures The gear ratio is the highest in the whole wind turbine, accounting for about 60%, and its maintenance cost is also relatively high, accounting for about 40%. In the process of formulating the plan, the remaining life prediction of the gear is the focus and difficulty. With the development of information sensing equipment, the running state of the gear can be monitored in real time, and the degradation state of the system can be predicted more acc...

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Problems solved by technology

[0003] At present, the prediction methods for the remaining life of gears are divided into four categories: prediction methods based on physical models, prediction methods based on statistical experience, prediction methods based on knowledge, and prediction methods based on data-driven; the existing prediction methods have the following problems: First, Existing prediction methods need to make state degradation model structure assumptions, and it is necessary to assume that the samples used as the basis for judgment conform to a specific model structure. There is often a large gap between the assumptions of these model structures and the actual physical model; secondly, the prediction Most of the parameter estimation problems involved in the model cannot guarantee global convergence; finally, because the gear is in a changing environment, its state degradation model will change, and a single prediction model cannot adapt to changes in the environment, requiring multiple prediction models Combine

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