دورية أكاديمية
The load-haul-dump operation cycle recognition based on multi-sensor feature selection and bidirectional long short-term memory network
| العنوان: | The load-haul-dump operation cycle recognition based on multi-sensor feature selection and bidirectional long short-term memory network |
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| المؤلفون: | Zhimin Qi, Qing Gu, Yu Meng, Guoxing Bai, Dawei Ding |
| المصدر: | Measurement + Control, Vol 56 (2023) |
| بيانات النشر: | SAGE Publishing, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Technology (General) |
| مصطلحات موضوعية: | Control engineering systems. Automatic machinery (General), TJ212-225, Technology (General), T1-995 |
| الوصف: | The operational cycle identification of the load-haul-dump (LHD) can help support the production process optimization in the underground mining industry and thus reduce mining costs. However, most of the existing research works use only the hydraulic bucket signal of LHD as the data source, and the stability and robustness of the identification method are poor. A few advanced research works use the variational Bayesian Gaussian mixture model to introduce other signals, but the accuracy of this recognition method is not perfect at present. In addition, the current identification methods are unable to simultaneously recognize the four working conditions of the LHD which include loading, hauling, dumping, and transiting. To solve these problems, a random forest feature selection (RFFS) and bidirectional long short-term memory (Bi-LSTM) based operation cycle recognition algorithm is proposed. Firstly, RFFS is used to remove redundant features based on the multi-sensor signals of the LHD. Then, Bi-LSTM is applied to fully exploit the temporal correlation between different operation regimes and accurately recognize the operation cycles. The effectiveness and superiority of the algorithm are verified by the experiment on the actual data of the LHD. The proposed algorithm can recognize four working conditions simultaneously, among which the recognition accuracy of loading conditions is the highest, up to 95.42%, and the weighted accuracy of this algorithm can reach 91.75% using the occupied time of each working condition as the weighting factor. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 0020-2940 00202940 |
| Relation: | https://doaj.org/toc/0020-2940 |
| DOI: | 10.1177/00202940231161569 |
| URL الوصول: | https://doaj.org/article/cf958595afab48409675d11eb7faebb7 |
| رقم الأكسشن: | edsdoj.f958595afab48409675d11eb7faebb7 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 00202940 |
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| DOI: | 10.1177/00202940231161569 |