Energy efficiency state identification in milling processes based on information reasoning and Hidden Markov Model

被引：19

作者：

Cai, Yun ^{[1
]}

Shi, Xinhua ^{[1
]}

Shao, Hua ^{[1
]}

Wang, Ran ^{[2
]}

Liao, Shuheng ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China

[2] Shanghai Maritime Univ, Coll Logist Engn, Shanghai 201306, Peoples R China

来源：

JOURNAL OF CLEANER PRODUCTION | 2018年 / 193卷

基金：

中国国家自然科学基金;

关键词：

Energy efficiency; Specific energy consumption; Milling process; Condition monitoring; Information reasoning; Hidden markov model; SUSTAINABLE MANUFACTURING SYSTEMS; ELECTRICITY DEMAND REDUCTION; CONTROL MACHINE-TOOLS; ACOUSTIC-EMISSION; CONSUMPTION; WEAR; MALFUNCTIONS; INVENTORY; ALGORITHM;

D O I：

10.1016/j.jclepro.2018.04.265

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Energy efficiency state identification of milling process plays an important role in energy saving efforts for manufacturing systems. However, it is very difficult to track energy efficiency state in machining processes based on traditional signal processing strategies due to the fact that energy state is usually coupled with a lot of factors like machine tool states, tool conditions, and cutting conditions. An identification method of information reasoning and Hidden Markov model for energy efficiency state is proposed in this paper. Utilizing cutting conditions, empirical models of the energy efficiency, experimental data and signal features, an expert system is established for initial probability optimization and the state is further identified by Hidden Markov model. The experiments show that energy efficiency state can be identified with this method. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：397 / 413

页数：17

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