基于<strong>SHAP-LightGBM</strong>的电动集装箱正面吊运起重机能耗分析和异常识别

doi:10.3785/j.issn.1006-754X.2024.03.307

工程设计学报

2024, Vol. 31

Issue (1): 81-90 DOI: 10.3785/j.issn.1006-754X.2024.03.307

可靠性与保质设计

基于SHAP-LightGBM的电动集装箱正面吊运起重机能耗分析和异常识别

郄永军¹(

),任杰¹,孙帅¹,周东才²,张凡²

^1.三一重工股份有限公司，北京 102206
^2.三一海洋重工有限公司，广东珠海 519050

Energy consumption analysis and anomaly identification of electric container reach stacker based on SHAP-LightGBM

Yongjun QIE¹(

),Jie REN¹,Shuai SUN¹,Dongcai ZHOU²,Fan ZHANG²

^1.Sany Heavy Industry Co. , Ltd. , Beijing 102206, China
^2.Sany Marine Heavy Industry Co. , Ltd. , Zhuhai 519050, China

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摘要：

集装箱正面吊运起重机（以下简称正面吊）在港口的实际作业中发挥着重要作用。随着社会对能源和环境问题的日益关注，正面吊的电动化趋势愈加显著，市场上电动正面吊的数量逐年增加。电耗性能直接影响电动正面吊的续航能力、作业效率和作业成本，是电动正面吊的重要性能之一。驾驶行为、作业工况、设备故障等因素均会对电动正面吊的能耗产生影响。为此，通过收集电动正面吊客户侧的实际运行数据，基于LightGBM（light gradient boosting machine，轻量级梯度提升机）模型，在微观和宏观两个层面分别对电动正面吊的行驶和作业过程进行能耗建模，并运用SHAP（Shapley additive explanations，沙普利加和解释）理论量化分析不同作业工况、作业行为对电动正面吊能耗的影响，同时识别设备故障所引起的能耗异常。结果表明，基于SHAP-LightGBM的能耗模型能够准确预测和分析电动正面吊的行驶和作业能耗，可为电动正面吊的设计、能耗策略优化提供有效的信息输入，同时可建立电动正面吊实际运行过程的理论能耗基准，有效指导驾驶行为和识别故障造成的能耗异常等。

关键词： 集装箱正面吊运起重机; 能耗模型; 异常识别; LightGBM模型; 能耗优化

Abstract:

The container reach stacker (hereinafter referred to as reach stacker) plays a crucial role in practical port operations. With the increasing attention of society to energy and environmental issues, the electrification trend of reach stackers is becoming more and more significant, and the number of electric reach stackers on the market has been steadily rising year by year. The electric energy consumption performance directly affects endurance capacity, working efficiency and working cost of electric reach stackers, which is one of the important performance of electric reach stackers. Various factors such as driving behavior, operation conditions and equipment malfunctions will have diverse effects on the energy consumption of electric reach stackers. Therefor, by collecting the actual operating data of the customer side of electric reach stackers and based on the LightGBM (light gradient boosting machine) model, the energy consumption modeling for the driving and operational processes of electric reach stackers was conducted at the micro and macro levels, respectively. The SHAP (Shapley additive explanations) theory was used to quantitatively analyze the impact of different operation conditions and behaviors on the energy consumption of reach stackers, while simultaneously identifying energy consumption anomalies caused by equipment malfunctions. The results show that the energy consumption model based on SHAP-LightGBM can accurately predict and analyze the driving and operational energy consumption of reach stackers, which provides valuable input for the design and optimization of energy consumption strategy for electric reach stackers. Additionally, the energy consumption model establishes a theoretical energy consumption benchmark for the actual operational processes of electric reach stackers, effectively guiding driving behavior and identifying energy consumption anomalies caused by malfunctions.

Key words: electric container reach stacker energy consumption model anomaly identification LightGBM model energy consumption optimization

收稿日期: 2023-10-20 出版日期: 2024-03-04

CLC:

TH 21

作者简介: 郄永军（1981—），男，山西原平人，研究员，博士生，从事大数据与算法应用、数字仿真与数字孪生的研究，E-mail: Xiyj19@mails.tsinghua.edu.cn，https://orcid.org/0009-0001-0774-8666

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引用本文:

郄永军,任杰,孙帅,周东才,张凡. 基于SHAP-LightGBM的电动集装箱正面吊运起重机能耗分析和异常识别[J]. 工程设计学报, 2024, 31(1): 81-90.

Yongjun QIE,Jie REN,Shuai SUN,Dongcai ZHOU,Fan ZHANG. Energy consumption analysis and anomaly identification of electric container reach stacker based on SHAP-LightGBM[J]. Chinese Journal of Engineering Design, 2024, 31(1): 81-90.

链接本文:

https://www.zjujournals.com/gcsjxb/CN/10.3785/j.issn.1006-754X.2024.03.307 或 https://www.zjujournals.com/gcsjxb/CN/Y2024/V31/I1/81

图1 某型号电动正面吊

图2 电动正面吊抓取集装箱的作业过程示意

图3 电动正面吊能耗建模与分析方法的总体框架

表1 电动正面吊能耗建模所需的实际运行参数

图4 滤波平滑处理前后车速、加速度信号的对比

图5 吊箱循环段划分示意

表2 4个能耗模型的预测精度对比

图6 4个能耗模型的预测总能耗与实际总能耗对比

图7 宏观能耗模型中各影响因子对总能耗的贡献度

图8 微观能耗模型中各影响因子对瞬时能耗的贡献度

图9 基于微观行驶能耗模型的瞬时车速和瞬时加速度的交互作用分析

图10 单个吊箱循环段内各影响因子对行驶总能耗的贡献

图11 单个吊箱循环段内各影响因子对作业总能耗的贡献

图12 实际行驶总能耗与理论行驶总能耗的对比与误差

1	李晓易，谭晓雨，吴睿，等.交通运输领域碳达峰、碳中和路径研究［J］.中国工程科学，2021，23（6）：15-21. doi：10.15302/J-SSCAE-2021.06.008 LI X Y， TAN X Y， WU R， et al. Paths for carbon peak and carbon neutrality in transport sector in China［J］. Engineering Science， 2021， 23（6）： 15-21. doi: 10.15302/J-SSCAE-2021.06.008
2	黄伟，张桂连，周登辉，等.基于能量流分析的纯电动汽车电耗优化研究［J］.汽车工程，2021，43（2）：171-180. doi：10.19562/j.chinasae.qcgc.2021.02.003 HUANG W， ZHANG G L， ZHOU D H， et al. Research on optimization of power consumption of pure electric vehicle based on energy flow analysis［J］. Automotive Engineering， 2021， 43（2）： 171-180. doi: 10.19562/j.chinasae.qcgc.2021.02.003
3	张树培，黄璇，荆哲铖.电动汽车再生制动系统回收特性与能量流分析［J］.重庆交通大学学报（自然科学版），2015，34（1）：157-161. doi：10.3969/j.issn.1674-0696.2015.01.34 ZHANG S P， HUANG X， JING Z C. Recovery characteristics and energy flow of electric vehicle regenerative braking system analysis［J］. Journal of Chongqing Jiaotong University （Natural Science）， 2015， 34（1）： 157-161. doi: 10.3969/j.issn.1674-0696.2015.01.34
4	RAHMAT A， FAHMI M， FANG L H， et al. The performance of the optimisation and regenerative braking systems by using PI controlling technique for electric vehicle （EV）［J］. Journal of Physics： Conference Series， 2022， 2312（1）： 012022.
5	王永鼎，裴开雅.纯电动汽车制动能量回收策略优化研究［J］.机械科学与技术，2022，41（9）：1436-1441. WANG Y D， PEI K Y. Research on optimization of braking energy recovery strategy for pure electric vehicles［J］. Mechanical Science and Technology for Aerospace Engineering， 2022， 41（9）： 1436-1441.
6	王震坡，孙逢春.电动汽车能耗分配及影响因素分析［J］.北京理工大学学报，2004，24（4）：306-310. doi：10.3969/j.issn.1001-0645.2004.04.008 WANG Z P， SUN F C. Analysis of energy consumption distribution and factors of influence in electric vehicles［J］. Transactions of Beijing Institute of Technology， 2004， 24（4）： 306-310. doi: 10.3969/j.issn.1001-0645.2004.04.008
7	张微，徐金波，王旭，等.基于WLTC工况的电动汽车能量流测试与分析［J］.汽车技术，2019（11）：6-9. ZHANG W， XU J B， WANG X， et al. Energy flow test and analysis of electric vehicle based on WLTC mode［J］. Automobile Technology， 2019（11）： 6-9.
8	AL-WREIKAT Y， SERRANO C， SODRE J R. Effects of ambient temperature and trip characteristics on the energy consumption of an electric vehicle［J］. Energy， 2022， 238（Part C）： 122028.
9	YI Z G， BAUER P H. Effects of environmental factors on electric vehicle energy consumption： a sensitivity analysis［J］. IET Electrical Systems in Transportation， 2017， 7（1）： 3-13.
10	赵佳伟，胡明辉，荣正璧，等.驾驶风格对纯电动汽车能耗的影响［J］.重庆大学学报，2021，44（12）：103-115. doi：10.11835/j.issn.1000-582X.2020.025 ZHAO J W， HU M H， RONG Z B， et al. Effect of driving style on the energy consumption of an electric vehicle［J］. Journal of Chongqing University， 2021， 44（12）： 103-115. doi: 10.11835/j.issn.1000-582X.2020.025
11	BINGHAM C， WALSH C， CARROLL S. Impact of driving characteristics on electric vehicle energy consumption and range［J］. IET Intelligent Transport Systems， 2012， 6（1）： 29-35.
12	沈涛，李飞，苏天晨，等.传统汽车整车能量流的仿真分析［C］//2017中国汽车工程学会年会论文集.北京：机械工业出版社，2017：1489-1493. SHEN T， LI F， SU T C， et al. Simulation analysis of energy flow of traditional vehicle［C］//2017 Proceedings of the Annual Meeting of the China Society of Automotive Engineering. Beijing： China Machine Press， 2017： 1489-1493.
13	乐智，盛俏，陈龙，等.整车能量流分析在燃油经济性开发中的应用［C］//2015中国汽车工程学会年会论文集.北京：机械工业出版社，2017：2047-2049. YUE Z， SHENG Q， CHEN L， et al. Application of energy flow down analysis in development of fuel economy ［C］//2015 Proceedings of the Annual Meeting of the China Society of Automotive Engineering. Beijing： China Machine Press， 2017： 2047-2049.
14	XU L F， LI J Q， OUYANG M. Energy flow modeling and realtime control design basing on mean values for maximizing driving mileage of a fuel cell bus［J］. International Journal of Hydrogen Energy， 2015， 40： 15052-15066.
15	AHN K， RAKHA H. The effects of route choice decisions on vehicle energy consumption and emissions［J］. Transportation Research Part D： Transport and Environment， 2008， 13（3）： 151-167.
16	KE G L， MENG Q， FINLEY T， et al. LightGBM： a highly efficient gradient boosting decision tree［C］//Proceedings of the 31st International Conference on Neural Information Processing Systems. New York： Curran Associates Inc.， 2017： 3149-3157.
17	LUNDBERG S M， LEE S I. A unified approach to interpreting model predictions［C］//Proceedings of the 31st International Conference on Neural Information Processing Systems. New York： Curran Associates Inc.， 2017： 4768-4777.
18	LUNDBERG S M， ERION G， CHEN H， et al. From local explanations to global understanding with explainable AI for trees［J］. Nature Machine Intelligence， 2020， 2（1）： 56-67.
19	LUNDBERG S M， NAIR B， VAVILALA M S， et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery［J］. Nature Biomedical Engineering， 2018， 2（10）： 749-760.
20	SHAPLEY L S. A value for n-person games［M］// Contributions to the theory of games （AM-28）， Volume Ⅱ. Princeton： Princeton University Press， 1953： 307-318.
21	赵敬玉，徐铖，李晓宇.基于实际行驶数据的电动汽车能耗分析与预测［J］.机械工程学报，2023，59（10）：263-274. ZHAO J Y， XU C， LI X Y. Electric vehicle energy consumption analysis and prediction based on real-world driving data［J］. Journal of Mechanical Engineering， 2023， 59（10）： 263-274.
22	邹萌，姚恩建，潘龙，等.基于微观能耗模型的电动汽车生态驾驶行为研究［EB/OL］.（2016-09-27）［2023-10-20］.. ZOU M， YAO E J， PAN L， et al. Study on eco-driving behavior of electric vehicles based on microscopic consumption models［EB/OL］. （2016-09-27）［2023-10-20］. .

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