Fan bearing fault self-adaptive identification method

Abstract

The invention discloses a fan bearing fault self-adaptive identification method, which constructs dual monitoring limits on the basis of a deep belief network, and realizes real-time monitoring and fault alarm of the running state of a fan. Then a dynamic time warping method is improved, a DTW-based variable similarity algorithm is adopted for the data judged as the fault, the contribution degree and the threshold value of each variable to the fault are obtained, and the condition that the contribution degree of each variable exceeds the threshold value is analyzed, so that deep analysis of the fault data and fault reason identification are realized. According to the method, the fan bearing fault can be accurately and timely identified, and on the basis of identifying the fault, the fault variable is automatically positioned, and the fault generation reason is identified.

Description

technical field [0001] The invention relates to an identification and automatic identification method of a multivariate time series, in particular to an adaptive identification method for fan bearing faults. Background technique [0002] In recent years, with the continuous advancement of economy and technology, the level of industrialization has been increasing, the scale of factories has been expanding, and the degree of automation has also been significantly improved. The complex mechanical and electrical equipment represented by offshore wind turbines has a precise structure, a high degree of correlation between all levels, and intelligent control strategies. However, any small abnormality may have a negative impact on the equipment and cause major failures. On the one hand, the increasingly developed industrial processes can bring great convenience and enrichment to people's life when they run under predetermined and ideal conditions, but on the other hand, once these l...

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Problems solved by technology

However, in the practical application of state marking of complex electromechanical systems, the above method has the following limitations: 1. Without prior knowledge, the number of classes of the working state of the original data is not known, which affects to a certain extent The accuracy of the clustering algorithm is improved; 2. Although the method based on density clustering can ignore the problem of the initial number of clusters, the parameter tuning process of the algorithm requires certain prior knowledge, and the clustering results vary greatly under different parameters; 3 , For the clustering results, the specific meaning of each category is not known, and the running status cannot be marked intuitively and clearly, but subsequent manual expertise is required for further processing

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