In manufacturing industries, including the wood industry, devices, and equipment are considered the basic elements and the main capital for production. That is why managers are trying to maintain and use these devices and equipment optimally. On the other hand, repurchasing device parts or repairing equipment in case of major damage can cause more damage than planned costs. Therefore, a model that can determine the fault class based on the signs seen in the equipment would prevent major damage to the device and save on repair costs. In this regard, using the registered features for equipment and with the help of machine learning algorithms, a model can be created that can classify devices in the appropriate class based on their observed features. The present study uses nine machine learning algorithms to make this model, trains each model on three sets of selected features, and finally compares them. It is worth mentioning that after evaluating the models, based on the features selected from the embedded techniques, permutation feature importance methods, and genetic algorithm, the best models are considered as categorical boosting with the training and testing accuracy of 0.895 and 0.909, random forest with the training and testing accuracy of 0.905 and 0.893, and extreme gradient boosting with the training and testing accuracy of 0.884 and 0.885.

中文翻译：

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基于集成方法的混合机器学习模型，用于木材工业设备故障预测

在包括木材工业在内的制造业中，装置和设备被认为是生产的基本要素和主要资本。这就是为什么管理人员试图以最佳方式维护和使用这些设备和设备。另一方面，在发生重大损坏时重新购买设备零件或维修设备可能会造成比计划成本更多的损坏。因此，一个可以根据设备中看到的迹象确定故障类别的模型可以防止设备发生重大损坏并节省维修成本。在这方面，使用设备的注册特征并在机器学习算法的帮助下，可以创建一个模型，该模型可以根据观察到的特征将设备分类为适当的类别。本研究使用九种机器学习算法来构建该模型，在三组选定的特征上训练每个模型，最后对它们进行比较。值得一提的是，在评估模型后，基于从嵌入技术、排列特征重要性方法和遗传算法中选择的特征，最佳模型被认为是训练和测试精度分别为 0.895 和 0.909 的分类提升模型、随机森林模型训练和测试精度分别为 0.905 和 0.893，极限梯度提升的训练和测试精度分别为 0.884 和 0.885。