Vikram Sakinala, P. S. Paul, Janardhan Rao Moparthi
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引用次数: 0
摘要
全身振动(WBV)对重型土方机械(HEMM)操作人员的职业健康和安全造成了极大的危害。有必要评估全身振动对重型推土机操作员的危害以及影响全身振动风险暴露的因素。七种机器学习(ML)模型在从七个地下冶金矿山收集的 81 个数据样本上进行了测试。研究考虑了九个因素,这九个因素在影响 HEMM 操作员的 WBV 风险暴露强度方面发挥了重要作用。采用 RReleifF 算法进行降维和特征排序。与其他 ML 技术相比,ANN 模型被认为是最有效的方法。使用 RReleifF 算法将考虑的九个特征缩减为五个特征。所选的五个特征根据其权重依次为笨拙的姿势、机器年龄、运输道路状况、速度和座椅厚度。最后,利用上述五个特征建立了一个预测方程。这项研究将有助于七个地下矿山当局在不使用科学仪器的情况下轻松评估 WBV 风险暴露,并有助于立即采取控制措施,以降低 HEMM 操作员的 WBV 风险暴露。
Assessment of HEMM Operators’ Risk Exposure due to Whole-Body Vibration in Underground Metalliferous Mines Using Machine Learning Techniques
Whole-body vibration (WBV) is a substantial occupational health and safety hazard to heavy earth-moving machinery (HEMM) operators. There is a need to appraise the effect of WBV jeopardize and the factors influencing the WBV risk exposure on the HEMM operators. Seven machine learning (ML) models were tested on 81 data samples collected from seven underground metalliferous mines. The study considered nine factors which have substantial role behind the intensity of the WBV risk exposure of HEMM operators. RReleifF algorithm was used for dimensionality reduction and ranking the features. Compared to other ML techniques, ANN model was determined to be the most effective approach. The nine considered features were reduced to five features using RReleifF algorithm. The ranking of the five features selected was in order of awkward posture, the machine age, haul road condition, speed, and seat thickness based on their weights. Finally, a predictive equation was developed using the aforementioned five features. This study will help the seven underground mines authority to evaluate the WBV risk exposure effortlessly without the usage of scientific instrument and also helps in adopting immediate control measures to mitigate WBV risk exposure of HEMM operators.
期刊介绍:
The aim of this international peer-reviewed journal of the Society for Mining, Metallurgy & Exploration (SME) is to provide a broad-based forum for the exchange of real-world and theoretical knowledge from academia, government and industry that is pertinent to mining, mineral/metallurgical processing, exploration and other fields served by the Society.
The journal publishes high-quality original research publications, in-depth special review articles, reviews of state-of-the-art and innovative technologies and industry methodologies, communications of work of topical and emerging interest, and other works that enhance understanding on both the fundamental and practical levels.