Abstract
A fault diagnosis method based on improved extreme learning machine (IELM) is proposed to solve the weakness (weak generalization ability, low diagnostic rate) of traditional fault diagnosis with feedforward neural network algorithm. This method fuses signal feature vectors, extracts six parameters as the principal component analysis (PCA) variables, and calculates correlation coefficient matrix among the variables. The weight values of control parameters in the extreme learning model are dynamically adjusted according to the test samples’ constantly changing. Consequently, the weight fixed drawback in the original model can be remedied. A fault simulation experiment platform for wind turbine drive system is built, eight kinds of fault modes are diagnosed by the improved extreme learning model, and the result is compared with that of other machine learning methods. The experiment indicates that the method can enhance the accuracy and generalization ability of diagnosis, and increase the computing speed. It is convenient for engineering application.
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Foundation item: the National Natural Science Foundation of China (No. 51535007) and the Innovation Program of Shanghai Municipal Education Commission (No. 15ZS079)
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Wu, B., Xi, L., Fan, S. et al. Fault diagnosis for wind turbine based on improved extreme learning machine. J. Shanghai Jiaotong Univ. (Sci.) 22, 466–473 (2017). https://doi.org/10.1007/s12204-017-1849-x
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DOI: https://doi.org/10.1007/s12204-017-1849-x
Key words
- wind turbine
- improved extreme learning machine (IELM)
- principal component analysis (PCA)
- fault diagnosis