Research on optimization of crane fault predictive control system based on data mining

IF 2.4 Q2 ENGINEERING, MECHANICAL

Nonlinear Engineering - Modeling and Application Pub Date : 2023-01-01 DOI:10.1515/nleng-2022-0202

Xu Yanbin, Zhang Jianhua, Xiongwei Wang, Mohammad Shabaz, Mohd Wazih Ahmad, Samrat Ray

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引用次数: 3

Abstract

Abstract To ensure the safe functioning of lifting equipment, a data mining-based optimization study of a crane failure predictive control system is provided. To diagnose lifting machinery faults, the system employs decision tree categorization. Using association rules, a correlation study of hoisting machinery defect and failure was performed. When the minimal confidence and support degree are entered, a total of 244 instances of 18 frequent itemset A9 (safety protection device) may be obtained, indicating that lifting machinery does not perform well in this category. A6 (main parts) and A9 appeared a total of 98 times, with support and confidence of 29.4 and 35.6, respectively, indicating that the main parts can detect that the safety protection device is also having problems. A7 (electrical control system) and A9 appeared a total of 67 times, with support and confidence of 20.1 and 27.3, respectively, indicating that the electrical control system can detect that the safety protection device is also having problems; the correlation between them was also quite large. The system’s feasibility and efficacy shows that it has some application value.

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基于数据挖掘的起重机故障预测控制系统优化研究

摘要为了保证起重设备的安全运行，对起重机故障预测控制系统进行了基于数据挖掘的优化研究。该系统采用决策树分类方法对起重机械进行故障诊断。利用关联规则对起重机械缺陷与故障进行了相关性研究。当输入最小置信度和支持度时，可以得到18个频繁项目集A9(安全保护装置)的总共244个实例，表明起重机械在这一类别中表现不佳。A6(主要部件)和A9共出现98次，支持度和置信度分别为29.4和35.6，说明主要部件可以检测到安全保护装置也存在问题。A7(电气控制系统)和A9共出现67次，支持度和置信度分别为20.1和27.3，说明电气控制系统可以检测到安全保护装置也存在问题;它们之间的相关性也很大。系统的可行性和有效性表明，该系统具有一定的应用价值。

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来源期刊

Nonlinear Engineering - Modeling and Application Multiple-

CiteScore

6.20

自引率

3.60%

发文量

审稿时长

44 weeks

期刊介绍： The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.