The Difficulties and Challenges of Energy Efficiency Maintenance for Electromechanical Equipment over the Whole Operating Range

doi:10.3901/JME.2024.23.354

Abstract

Abstract: With the large-scale application of high-efficiency electromechanical equipment and the continuous strengthening of energy efficiency constraints, improving the specialized energy-saving maintenance service capabilities for equipment, and ensuring that each work unit of equipment fully realizes its energy efficiency potential over the whole operating range of various working conditions, are the key issues that need to be urgently solved in the process of comprehensively improving industrial energy efficiency in China. However, numerous internal and external factors that affect the energy efficiency loss of equipment abound, which are deeply coupled in different work units within the whole working space domain of equipment and in multiple working conditions within the whole working time domain. As a result, the process and objectives of energy efficiency maintenance are hard to effectively control, making energy efficiency maintenance over the whole operating range difficult. To this end, by analyzing the causes of low energy efficiency service over the whole operating range of electromechanical equipment, the problem characteristics of energy efficiency maintenance over the whole operating range are clarified, identifying key scientific challenges affecting engineering practice. Furthermore, the representative research progress of three difficult problems is reviewed in detail, and the current research status and the main problems faced are discussed. Three difficult problems include the analysis of energy efficiency loss and identification of regulatory factors over the whole operating range of electromechanical equipment, the construction of the energy efficiency maintenance process model of the whole operating range, and the multi-objective energy efficiency maintenance optimization method of the whole operating range. Finally, the research level and key point of energy efficiency maintenance over the whole operating range are given.

Key words: energy-saving service, energy efficiency maintenance, energy efficiency loss, whole operating range, electromechanical equipment

CLC Number:

TH17

DUAN Rong, JIANG Zhigang, ZHANG Hua, GONG Qingshan, LI Mingyao. The Difficulties and Challenges of Energy Efficiency Maintenance for Electromechanical Equipment over the Whole Operating Range[J]. Journal of Mechanical Engineering, 2024, 60(23): 354-364.

References

[1] 中华人民共和国工业和信息化部. 工信部等六部门关于印发《工业能效提升行动计划》的通知(2022-06-29)[EB/OL]. https：//www.gov.cn/zhengce/ zhengceku/2022-06/29/content_5698410.htm. Ministry of Industry and Information Technology of the People's Republic of China. Notice on issuing the action plan for improving industrial energy efficiency by the ministry of industry and information technology and six other departments (2022-06-29)[EB/OL]. https：//www.gov.cn/zhengce/zhengceku/2022-06/29/content_5698410.htm.
[2] 刘培基，刘飞，王旭，等. 绿色制造的理论与技术体系及其新框架[J]. 机械工程学报，2022，57(19)：165-179. LIU Peiji，LIU Fei，WANG Xu，et al. The theory and technology system of green manufacturing and their new frameworks[J]. Journal of Mechanical Engineering，2022，57(19)：165-179.
[3] MA Xiaolu，WANG Zhixiong，DUAN Yanmin，et al. Analysis on promotion effect of high-efficiency motor system[J]. Electric Drive，2022，52(18)：10-15.
[4] CAI Wei，LAI Kehuang，LIU Conghu，et al. Promoting sustainability of manufacturing industry through the lean energy-saving and emission-reduction strategy[J]. Science of the Total Environment，2019，665(1)：23-32.
[5] BERMEO-AYERBE M A，OCAMPO-MARTINEZ C，DIAZ-ROZO J. Adaptive predictive control for peripheral equipment management to enhance energy efficiency in smart manufacturing systems[J]. Journal of Cleaner Production，2021，291：125556.
[6] 中华人民共和国工业和信息化部. “十四五”工业绿色发展规划[EB/OL]. [2021-11-15]. https：//www.gov.cn/ zhengce/zhengceku/2021-12/03/content_5655701.htm. Ministry of Industry and Information Technology of the People's Republic of China. "14th Five-Year Plan" industrial green development plan[EB/OL]. [2021-11-15]. https：//www.gov.cn/zhengce/zhengceku/2021-12/03/ content_5655701.htm.
[7] SIHAG N，SANGWAN K S. A systematic literature review on machine tool energy consumption[J]. Journal of Cleaner Production，2020，275：123125.
[8] 高金吉，张连凯. 中国高能耗机械装备运行现状及节能对策研究[M]. 北京：科学出版社，2013. GAO Jinji，ZHANG Liankai. Research on the operation status and energy-saving countermeasures of high-energy- consuming machinery and equipment in China[M]. Beijing：Science Press，2013.
[9] CAI Wei，LI Li，JIA Shun，et al. Task-oriented energy benchmark of machining systems for energy-efficient production[J]. International Journal of Precision Engineering and Manufacturing-Green Technology，2020，7(1)：205-218.
[10] 石雪松，冯占宸. 智慧运维在风机运行及改造中的应用[J]. 流程工业，2021(8)：44-46. SHI Xuesong，FENG Zhanchen. Application of intelligent operation and maintenance in wind turbine operation and transformation [J]. Process Industry，2021(8)：44-46.
[11] 中华人民共和国国家发展和改革委员会. 国家发展改革委等部门关于发布《重点用能产品设备能效先进水平、节能水平和准入水平(2022年版)》的通知(2022) [EB/OL].[2022-11-18]. https：//www.gov.cn/zhengce/ zhengceku/2022-11/18/content_5727693.htm. National Development and Reform Commission of the People's Republic of China. Notice on the release of "Advanced level，energy-saving level and access level of key energy-consuming products and equipment (2022)" by the National Development and Reform Commission and other departments (2022) [EB/OL]. [2022-11-18]. https：//www.gov.cn/zhengce/zhengceku/2022-11/18/content_5727693.htm.
[12] 中华人民共和国工业和信息化部. “能效之星”装备产品目录(2021)[EB/OL]. [2021-09-24]. https：//www.miit.gov. cn/zwgk/wjgs/art/2021/art_39e36d2ed20d45eda0e4451d82bd7d72.html. Ministry of Industry and Information Technology of the People's Republic of China. "Energy efficiency star" equipment product catalog(2021)[EB/OL]. [2021-09-24]. https：//www.miit.gov.cn/zwgk/wjgs/art/2021/art_ 39e36d2ed20d45eda0e4451d82bd7d72.html.
[13] 何彦，林申龙，王禹林，等. 数控机床多能量源的动态能耗建模与仿真方法[J]. 机械工程学报，2015，51(11)：123-132. HE Yan，LIN Shenlong，WANG Yulin，et al. Method for modeling the dynamic energy characteristics of multi-energy sources in CNC machine tool[J]. Journal of Mechanical Engineering，2015，51(11)：123-132.
[14] DIAZ-ELSAYED N，DORNFELD D，HORVATH A. A comparative analysis of the environmental impacts of machine tool manufacturing facilities[J]. Journal of Cleaner Production，2015，95：223-231.
[15] LIU Jiangyan，WANG Jiangyu，LI Guannan，et al. Evaluation of the energy performance of variable refrigerant flow systems using dynamic energy benchmarks based on data mining techniques[J]. Applied Energy，2017，208：522-539.
[16] 王仲，顾煜炯，韩旭东，等. 基于数据驱动的全工况下燃气轮机基准值确定[J]. 自动化仪表，2019，40(4)：5-8，13. WANG Zhong，GU Yujiong，HAN Xudong，et al. Reference value determination based on data driven under full operating conditions of gas turbine[J]. Automation Instrumentation，2019，40(4)：5-8，13.
[17] CHEN Dongchao，CAO Lihua，SI Heyong. Benchmark value determination of energy efficiency indexes for coal-fired power units based on data mining methods[J]. Advanced Engineering Informatics，2020，43：101029.
[18] WANG Ningling，FU Peng，XU Han，et al. Heat transfer characteristics and energy-consumption benchmark state with varying operation boundaries for coal-fired power units：An exergy analytics approach[J]. Applied Thermal Engineering，2015，88：433-443.
[19] CAI Wei，LIU Fei，DINOLOV O，et al. Energy benchmarking rules in machining systems[J]. Energy，2018，142：258-263.
[20] CAI Wei，LIU Fei，ZHANG Hua，et al. Development of dynamic energy benchmark for mass production in machining systems for energy management and energy-efficiency improvement[J]. Applied Energy，2017，202：715-725.
[21] JIA Shun，YUAN Qinghe，CAI Wei，et al. Energy modeling method of machine-operator system for sustainable machining[J]. Energy Conversion and Management，2018，172(1)：265-276.
[22] CAI Wei，LIU Conghu，JIA Shun，et al. An energy-based sustainability evaluation method for outsourcing machining resources[J]. Journal of Cleaner Production，2020，245：118849.
[23] MA Feng，ZHANG Hua，GONG Qingshan，et al. A novel energy efficiency grade evaluation approach for machining systems based on inherent energy efficiency[J]. International Journal of Production Research，2021，59(19)：6022-6033.
[24] 庹军波，刘飞，张华，等. 机床固有能量效率的内涵及其评价方法[J]. 机械工程学报，2018，54(7)：167-175. TUO Junbo，LIU Fei，ZHANG Hua，et al. Connotation and assessment method for inherent energy efficiency of machine tools[J]. Journal of Mechanical Engineering，2018，54(7)：167-175.
[25] MENG Di，SHAO Cheng，ZHU Li. Two-level comprehensive energy-efficiency quantitative diagnosis scheme for ethylene-cracking furnace with multi-working-condition of fault and exception operation[J]. Energy，2022，239：121835.
[26] MENG Di，SHAO Cheng，ZHU Li. Dual-radiation-chamber coordinated overall energy efficiency scheduling solution for ethylene cracking process regarding multi-parameter setting and multi-flow allocation[J]. Chinese Journal of Chemical Engineering，2021，34(6)：180-197.
[27] 刘志峰，李磊，黄海鸿，等. 液压机驱动系统分区控制节能方法[J]. 中国机械工程，2016，27(14)：1942. LIU Zhifeng，LI Lei，HUANG Haihong，et al. An energy-saving partition control method drive system for hydraulic presses[J]. China Mechanical Engineering，2016，27(14)：1942.
[28] 黄海鸿，唐运先，金瑞，等. 变速变排量液压驱动系统的动态过程能效优化[J]. 机械工程学报，2021，57(7)：185-193. HUANG Haihong，TANG Yunxian，JIN Rui，et al. Energy efficiency optimized in the dynamic process of a variable-speed variable-displacement pump unit[J]. Journal of Mechanical Engineering，2021，57(7)：185-193.
[29] BERMEO-AYERBE M A，OCAMPO-MARTINEZ C，DIAZ-ROZO J. Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems[J]. Energy，2022，238：121691.
[30] FU Peng，WANG Ningling，WANG Liang，et al. Performance degradation diagnosis of thermal power plants：A method based on advanced exergy analysis[J]. Energy Conversion and Management，2016，130：219-229.
[31] LI Benjie，CAO Huajun，HON B，et al. Exergy-based energy efficiency evaluation model for machine tools considering thermal stability[J]. International Journal of Precision Engineering and Manufacturing-Green Technology，2021，8(2)：423-434.
[32] TUO Junbo，LIU Fei，LIU Peiji. Key performance indicators for assessing inherent energy performance of machine tools in industries[J]. International Journal of Production Research，2019，57(6)：1811-1824.
[33] LIU Peiji，TUO Junbo，LIU Fei，et al. A novel method for energy efficiency evaluation to support efficient machine tool selection[J]. Journal of Cleaner Production，2018，191：57-66.
[34] 赵帅，顾煜炯，李欣，等. 供热机组多元工况下能效状态基准研究[J]. 热力发电，2022，51(4)：88-95. ZHAO Shuai，GU Yujiong，LI Xin，et al. Research on energy efficiency state benchmark of heating units under multiple operating conditions[J]. Thermal Power Generation，2022，51(4)：88-95.
[35] DO P，SCARF P，IUNG B. Condition-based maintenance for a two-component system with dependencies[J]. IFAC-PapersOnLine，2015，48(21)：946-951.
[36] NGUYEN K A，DO P，GRALL A. Multi-level predictive maintenance for multi-component systems[J]. Reliability Engineering & System Safety，2015，144：83-94.
[37] HUYNH K T. An adaptive predictive maintenance model for repairable deteriorating systems using inverse gaussian degradation process[J]. Reliability Engineering & System Safety，2021，213：107695.
[38] HUYNH K T，VU H C，Nguyen T D，et al. A predictive maintenance model for k-out-of-n：F continuously deteriorating systems subject to stochastic and economic dependencies[J]. Reliability Engineering & System Safety，2022，226：108671.
[39] YANG Hang，ZHANG Zhe，YIN Xianggen. A novel method of decision‐making for power transformer maintenance based on failure‐probability‐analysis[J]. IEEJ Transactions on Electrical and Electronic Engineering，2018，13(5)：689-695.
[40] ZHAO Hongshan，XU Fanhao，LIANG Botong，et al. A condition-based opportunistic maintenance strategy for multi-component system[J]. Structural Health Monitoring，2019，18(1)：270-283.
[41] XIA Tangbin，XI Lifeng. Manufacturing paradigm-oriented PHM methodologies for cyber-physical systems[J]. Journal of Intelligent Manufacturing，2019，30(4)：1659-1672.
[42] 李想，朱才朝，李垚，等. 基于延迟时间模型的风电机组维修策略[J]. 重庆大学学报，2020，43(10)：20-28. LI Xiang，ZHU Caichao，LI Yao，et al. Maintenance strategy of wind turbine based on the delay time model[J]. Journal of Chongqing University，2020，43(10)：20-28.
[43] 徐婧，顾煜炯，王仲，等. 基于数据挖掘的煤电机组能效特征指标及其基准值的研究[J]. 中国电机工程学报，2017，37(7)：2009-2015. XU Jing，GU Yujiong，WANG Zhong，et al. Research on indexes of energy efficiency and its reference-value for coal-fired power units based on data-mining[J]. Proceedings of the CSEE，2017，37(7)：2009-2015.
[44] HUANG Naicheng，GU Yujiong，XIE Qiyu，et al. Study of intelligent fault diagnosis method for turbo-generator unit based on support vector machine and knowledge[C]//Applied Mechanics and Materials. Trans Tech Publications Ltd，2014，543：1057-1063.
[45] 李祖明，孙仲民，顾乔根，等. 基于继电保护装置态势感知及辅助决策的智能运维系统[J]. 电力系统保护与控制，2020，48(19)：142-150. LI Zuming，SUN Zhongmin，GU Qiaogen，et al. Smart operation and maintenance system based on situational awareness and assistant decision-making of relay protection devices[J]. Power System Protection and Control，2020，48(19)：142-150.
[46] 黄新波，刘成，张烨，等. CBR和RBR融合的牵引变压器运维策略[J]. 电力自动化设备，2020，40(3)：194-200. HUANG Xinbo，LIU Cheng，ZHANG Ye，et al. Operation and maintenance strategy of traction transformer based on CBR and RBR[J]. Electric Power Automation Equipment，2020，40(3)：194-200.
[47] 张丽珠，章超波，陈琦，等. 基于遗传算法和人工神经网络的冷水机组模型参数辨识及误差补偿方法[J]. 制冷学报，2021，42(3)：93-99. ZHANG Lizhu，ZHANG Chaobo，CHEN Qi，et al. Genetic-algorithm-based parameter identification and artificial-neural-network-based error vompensation for vhiller model[J]. Journal of Refrigeration，2021，42(3)：93-99.
[48] 欧一鸣，苏雍贺，靳健，等. 基于知识图谱的分布式光伏运维方案匹配方法[J]. 计算机集成制造系统，2021，27(7)：1860-1870. OU Yiming，SU Yonghe，JIN Jian，et al. Matching method for distributed photovoltaic maintenance scheme based on knowledge graph[J]. Computer Integrated Manufacturing System，2021，27(7)：1860-1870.
[49] 鄢威，张华，江志刚. 基于BPMN的数控机床能耗多源动态特性建模及应用[J]. 机械工程学报，2017，53(11)：195-202. YAN Wei，ZHANG Hua，JIANG Zhigang. A multi-source and dynamic energy modeling method of machining tools based on BPMN and its application[J]. Journal of Mechanical Engineering，2017，53(11)：195-202.
[50] YAN Wei，ZHANG Hua，JIANG Zhigang，et al. A new multi-source and dynamic energy modeling method for machine tools[J]. The International Journal of Advanced Manufacturing Technology，2018，95：4485-4495.
[51] 朱硕，张华，江志刚. 机械加工工艺单元多分辨率实体动态能效获取方法[J]. 机械工程学报，2019，55(1)：160-171. ZHU Shuo，ZHANG Hua，JIANG Zhigang. An acquisition method of dynamic energy efficiency for multi-resolution entity of mechanical processing unit[J]. Journal of Mechanical Engineering，2019，55(1)：160-171.
[52] 闫军威，卢泽东，周璇. 基于NSGA-Ⅱ 的冷源机房设备运行参数多目标优化[J]. 科学技术与工程，2021，21(7)：2896-2903. YAN Junwei，LU Zedong，ZHOU Xuan. Multi-objective optimization of operating parameters of central air conditioning cold source equipment room based on NSGA-Ⅱ[J]. Science Technology and Engineering，2021，21(7)：2896-2903.
[53] MA Rongjiang，YANG Shen，WANG Xianlin，et al. Systematic method for the energy-saving potential calculation of air-conditioning systems via data mining. part I：Methodology[J]. Energies，2020，14(1)：81.
[54] MA Rongjiang，YANG Shen，WANG Xianlin，et al. Systematic method for the energy-saving potential calculation of air conditioning systems via data mining. part II：A detailed case study[J]. Energies，2020，14(1)：86.
[55] 刘佳慧，刘江岩，陈焕新，等. 基于SVR-OCSVM模型的多联机系统用能评估与诊断[J]. 制冷学报，2020，41(4)：75-81. LIU Jiahui，LIU Jiangyan，CHEN Huanxin，et al. Energy assessment and diagnosis of variable refrigerant flow system based on SVR-OCSVM model[J]. Journal of Refrigeration，2020，41(4)：75-81.
[56] SHIN S J，WOO J，RACHURI S. Energy efficiency of milling machining：Component modeling and online optimization of cutting parameters[J]. Journal of Cleaner Production，2017，161：12-29.
[57] LI Congbo，XIAO Qinge，TANG Y，et al. A method integrating Taguchi，RSM and MOPSO to CNC machining parameters optimization for energy saving[J]. Journal of Cleaner Production，2016，135：263-275.
[58] LI Congbo，LI Lingling，TANG Y，et al. A comprehensive approach to parameters optimization of energy-aware CNC milling[J]. Journal of Intelligent Manufacturing，2019，30(1)：123-138.
[59] JIA Shun，YUAN Qinghe，CAI Wei，et al. Energy modeling method of machine-operator system for sustainable machining[J]. Energy Conversion and Management，2018，172(1)：265-276.
[60] SONG Lijun，SHI J，PAN Anda，et al. A dynamic multi-swarm particle swarm optimizer for multi-objective optimization of machining operations considering efficiency and energy consumption[J]. Energies，2020，13(10)：2616.
[61] 鄢威，张华，江志刚，等. 面向节能高效需求的数控加工系统多目标优化模型[J]. 中国机械工程，2018，29(21)：2571-2580. YAN Wei，ZHANG Hua，JIANG Zhigang，et al. Multi-objective optimization model faced to demands of energy saving and high efficiency for CNC machining systems[J]. China Mechanical Engineering，2018，29(21)：2571-2580.
[62] MA Feng，ZHANG Hua，HON K K B，et al. An optimization approach of selective laser sintering considering energy consumption and material cost[J]. Journal of Cleaner Production，2018，199：529-537.
[63] XIAO Yongmao，ZHANG Hua，JIANG Zhigang，et al. Multiobjective optimization of machining center process route：Tradeoffs between energy and cost[J]. Journal of Cleaner Production，2021，280：124171.