Research on the prediction algorithm of tread wear for locomotive wheels based on GA-ridge regression analysis
Wheel tread wear is an important parameter to evaluate the operational safety of locomotives, yet timely and accurate monitoring is often lacking at wheel operation and maintenance sites. To this end, this paper proposed a prediction algorithm of tread wear for locomotive wheels based on GA-ridge re...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Drive for Locomotives
2023-11-01
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| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2023.06.009 |
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| Summary: | Wheel tread wear is an important parameter to evaluate the operational safety of locomotives, yet timely and accurate monitoring is often lacking at wheel operation and maintenance sites. To this end, this paper proposed a prediction algorithm of tread wear for locomotive wheels based on GA-ridge regression analysis (hereinafter referred to as the "GA-ridge regression” prediction algorithm). This algorithm consisted of two steps: data pre-processing and data-based prediction analysis. In the first step, collected tread wear data was classified according to different measurement methods, and characteristics of different data types were analyzed considering the actual operation and maintenance of wheels. The classified data was then sliced using the profiling cycle as the data partitioning criterion, followed by cleaning and noise reduction of the corresponding dynamic measurement data by relevant criteria and principal component analysis. In the second step, data was integrated into datasets, and a time-sliding window was created for the training set data. The ridge regression algorithm was used to train the training set data for regression analysis, and the model parameters were tuned using a combination of the genetic algorithm and the validation set data to improve the prediction accuracy. The test set data was used for prediction by the traditional prediction algorithm, ridge regression linear prediction algorithm, and GA-ridge regression prediction algorithm respectively to compare and analyze their prediction effects. Additionally, a comparative analysis was conducted using the same evaluation method and sample wheels in an expanded size to further assess the prediction effects. The results indicate relatively lower prediction errors and standard deviations of errors when using the GA-ridge regression prediction algorithm. This research concludes that the GA-ridge regression prediction algorithm provides higher prediction accuracy and better prediction stability. |
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| ISSN: | 1000-128X |