考虑产品运行大数据的装载机变速箱优化设计

doi:10.3901/JME.2018.22.218

机械工程学报 ›› 2018, Vol. 54 ›› Issue (22): 218-232.doi: 10.3901/JME.2018.22.218

• 交叉与前沿 • 上一篇

考虑产品运行大数据的装载机变速箱优化设计

王少杰¹, 侯亮¹, 方奕凯¹, 林浩菁¹, 郭涛², 焦建新³

1. 厦门大学机电工程系厦门 361102;
2. 厦门厦工机械股份有限公司厦门 361023;
3. 佐治亚理工学院亚特兰大 GA30332 美国

收稿日期:2017-12-05 修回日期:2018-06-20 出版日期:2018-11-20 发布日期:2018-11-20
通讯作者: 侯亮(通信作者),男,1974年出生,博士,教授,博士研究生导师。主要研究方向为产品大批量定制技术、振动噪声控制以及工业大数据等。Email:hliang@xmu.edu.cn
作者简介:王少杰,男,1985年出生,博士,助理教授,硕士研究生导师。主要研究方向为车辆控制策略优化研究与车联网数据挖掘分析、工业大数据分析与人工智能系统研发、嵌入式系统开发与精密仪器设计等。E-mail:wsj@xmu.edu.cn
基金资助:
福建省科技重大专项（2016HZ0001-9）和“十三五”厦门市海洋经济创新发展示范（16CZB033SF14）资助项目。

Optimization Design of Wheel Loader Gearbox Considering Product Operational Big Data

WANG Shaojie¹, HOU Liang¹, FANG Yikai¹, LIN Haoqing¹, GUO Tao², JIAO Jianxin³

1. Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361102;
2. Xiamen XGMA Machinery Co., Ltd, Xiamen 361023;
3. Georgia Institute of Technology, Atlanta, GA30332 USA

Received:2017-12-05 Revised:2018-06-20 Online:2018-11-20 Published:2018-11-20

摘要/Abstract

摘要： 准确获知产品运行大数据对于产品研发与创新具有重要意义。传感网、物联网和CPS等技术的发展使得越来越多的产品运行数据获取成为可能，在此基础上，提出基于运行大数据采集、分析和应用的装载机变速箱优化设计流程。首先选取原生土、铁矿石、细沙和煤渣四种作业对象的典型作业工况，设计采集方案并完成运行大数据采集；然后将获取的挡位信号转化为累积挡位利用率，液压泵压力信号转化为液压系统分流功率，并以此作为轮式装载机动力性和燃油经济性的建模优化因素；最后利用MOPSO和NSAG-Ⅱ两种优化算法权衡动力性、经济性及约束条件的多目标竞争与冲突关系，最终获得保证最优动力性基础上，实现最佳经济性的可行域解集。两种算法获得的最优解集相对于原始设计，在同等仿真条件下，分别使功率损失率降低8.83%、4.80%，工况油耗降低0.19%、0.34%。研究表明，将在役产品运行大数据反馈应用于产品研发前端的设计方法，是实现产品升级和创新设计的一个有效途径。

关键词: 变速箱, 产品开发, 挡位利用率, 燃油经济性, 优化设计, 运行大数据, 装载机

Abstract: The big data collected during the product operations is of great value for product design, equipment maintenance and health assessment. It aims to optimize the wheel loader gearbox design by exploiting the big data acquired when the wheel loader is operating. A data-driven design optimization model is proposed, including data collection, data analysis, and optimization of the wheel loader gearbox. First, a data acquisition system is designed to collect the operational signals under four typical working conditions, i.e. native soil, iron ore, fine sand and coal cinder. Subsequently, the obtained gear operating signals and hydraulic pump pressure signals are processed and converted to cumulative gear utilization and the hydraulic system diversion power, respectively, which are further deployed as the optimization factors for the wheel loader power and fuel economy models. To solve the optimization models, the MOPSO and NSAG-Ⅱ algorithms are implemented to leverage diverse competing and conflicting goals related to multiple sub-targets of the gearbox design. The objective function is geared towards the optimal power performance while achieving fuel economy. Simulation studies indicate improvements of the resulting optimal design. For the respective MOPSO and NSAG-Ⅱ optimal solutions, the power loss rate is reduced by 8.83% and 4.80%, respectively, whilst the fuel consumption is reduced by 0.19% and 0.34%, respectively. The studies demonstrate the feasibility and potential of design optimization with consideration of feedbacks from big operating data. It is envisioned that product operating data-driven design is promising approach to facilitating continuous design improvement and promoting product innovation.

Key words: big operating data, fuel economy, gear utilization ratio, gearbox, optimal design, product development, wheel loader

中图分类号:

U463

王少杰, 侯亮, 方奕凯, 林浩菁, 郭涛, 焦建新. 考虑产品运行大数据的装载机变速箱优化设计[J]. 机械工程学报, 2018, 54(22): 218-232.

WANG Shaojie, HOU Liang, FANG Yikai, LIN Haoqing, GUO Tao, JIAO Jianxin. Optimization Design of Wheel Loader Gearbox Considering Product Operational Big Data[J]. Journal of Mechanical Engineering, 2018, 54(22): 218-232.

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考虑产品运行大数据的装载机变速箱优化设计

Optimization Design of Wheel Loader Gearbox Considering Product Operational Big Data

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