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

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

Chinese Journal of Engineering Design

2024, Vol. 31

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

Reliability and Quality Design

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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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

Received: 20 October 2023 Published: 04 March 2024

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Cite this article:

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. Chinese Journal of Engineering Design, 2024, 31(1): 81-90.

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

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

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

Fig.1 A certain model of electric reach stacker

Fig.2 Schematic of operation process of electric reach stacker grabbing container

Fig.3 Overall framework of energy consumption modeling and analysis method for electric reach stacker

Table 1 Actual operational parameters required for energy consumption modeling of electric reach stacker

Fig.4 Comparison of vehicle velocity and acceleration signals before and after filtering and smoothing processing

Fig.5 Division schematic of suspension box circulation section

Table 2 Comparison of prediction accuracy of four energy consumption models

Fig.6 Comparison between predicted total energy consumption of four energy consumption models and actual total energy consumption

Fig.7 Contribution of various influence factors in macro energy consumption model to total energy consumption

Fig.8 Contribution of various influence factors in micro energy consumption model to instantaneous energy consumption

Fig.9 Analysis of interaction effect between instantaneous vehicle velocity and instantaneous acceleration based on micro driving energy consumption model

Fig.10 Contribution of various influence factors to total driving energy consumption during a suspension box circulation section

Fig.11 Contribution of various influence factors to total operational energy consumption during a suspension box circulation section

Fig.12 Comparison and error between actual total driving energy consumption and theoretical total driving energy consumption


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