Developing and evaluating predictive conveyor belt wear models

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Callum Webb, J. Sikorska, R. N. Khan, M. Hodkiewicz
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引用次数: 11

Abstract

Abstract Conveyor belt wear is an important consideration in the bulk materials handling industry. We define four belt wear rate metrics and develop a model to predict wear rates of new conveyor configurations using an industry dataset that includes ultrasonic thickness measurements, conveyor attributes, and conveyor throughput. All variables are expected to contribute in some way to explaining wear rate and are included in modeling. One specific metric, the maximum throughput-based wear rate, is selected as the prediction target, and cross-validation is used to evaluate the out-of-sample performance of random forest and linear regression algorithms. The random forest approach achieves a lower error of 0.152 mm/megatons (standard deviation [SD] = 0.0648). Permutation importance and partial dependence plots are computed to provide insights into the relationship between conveyor parameters and wear rate. This work demonstrates how belt wear rate can be quantified from imprecise thickness testing methods and provides a transparent modeling framework applicable to other supervised learning problems in risk and reliability.
开发和评估预测输送带磨损模型
摘要输送带磨损是散装物料处理行业的一个重要考虑因素。我们定义了四个皮带磨损率指标,并使用行业数据集开发了一个模型来预测新输送机配置的磨损率,该数据集包括超声波厚度测量、输送机属性和输送机吞吐量。所有变量都有望以某种方式解释磨损率,并包含在建模中。选择一个特定的度量,即基于最大吞吐量的磨损率,作为预测目标,并使用交叉验证来评估随机森林和线性回归算法的样本外性能。随机森林方法实现了0.152 mm/兆吨的较低误差(标准偏差[SD]=0.0648)。计算了排列重要性和部分依赖图,以深入了解输送机参数和磨损率之间的关系。这项工作展示了如何通过不精确的厚度测试方法量化皮带磨损率,并提供了一个透明的建模框架,适用于风险和可靠性方面的其他监督学习问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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