摘要
挤压机作为铝型材生产的关键设备,其稳定性和可靠性是保障铝型材正常生产的前提。传统的设备异常检测方法属于侵入式检测,需要依靠外围仪器嵌入生产设备进行分析,检测成本高、自适应能力差。而能耗异常检测作为一种新颖的非侵入式检测手段,能够有效反映设备的运行工况,及时发现挤压过程的异常情况。为此将支持向量回归(SVR)与遗传算法(GA)相结合,提出一种基于GA-SVR的挤压机能耗异常检测模型。首先,根据挤压生产特点分析影响挤压能耗的关键因素,建立关键能耗因素为输入、电耗为输出的GA-SVR能耗预测模型。其次,考虑异常点的不确定性,基于GA-SVR模型构建单产能耗的置信区间并将其作为能耗异常区间。最后,以SY-1000Ton型挤压机为对象进行数值实验,验证所提模型的有效性。实验结果表明:置信度达到97%以上时所提模型检测能够准确地检测到挤压异常,这对保证挤压机稳定生产具有重要意义。
As the key equipment for the production of aluminum, the stability and reliability of aluminum extrusion machine is fundamental for normal production. Traditional equipment anomaly detection method belongs to intrusion detection, which requires other instrument to be embedded in production equipment for analysis, with the disadvantages of high-cost and low adaptive-capacity. As a new non-intrusion detection method, energy consumption anomaly detection can reflect operating conditions of equipment and find out anomaly situation timely. Therefore, support vector regression ( SVR) and genetic algorithm ( GA) were combined to provide an anomaly detection model. Through analyzing the key factors of extrusion energy consumption, a GA-SVR energy consumption prediction model was built, whose input was the key energy consumption factor and output was power consumption. Then, by considering the uncertainty of the anomaly point, a confidence interval of energy consumption was founded based on GA-SVR model and the confidence interval was seen as energy consumption anomaly interval. At last, an experiment was accomplished to verify the effectiveness of the model for SY-1000Ton extruder. The results show that the model mentioned above can be used to find out anomaly accurately when confidence coefficient is more than 97%. So, it can be used to ensure the steady production of extruder.
作者
杨海东
江海昌
方华
李洪丞
印四华
朱成就
YANG Haidong;JIANG Haichang;FANG Hua;LI Hongcheng;YIN Sihua;ZHU Chengjiu(Mechanical Science and Engineering College,Huazhong University of Science and Technology,Wuhan Hubei 430074,China;School of Mechatronic Engineering,Guangdong Universityof Technology,Guangzhou Guangdong 510006,China)
出处
《机床与液压》
北大核心
2019年第5期163-168,共6页
Machine Tool & Hydraulics
基金
国家自然科学基金联合基金项目(U1501248)
国家自然科学基金项目(51475096)
中国博士后科学基金(2016M602443)
广东省计算机集成制造重点实验室开放基金(CIMSOF2016011)
关键词
铝型材挤压机
能耗异常
GA-SVR
置信区间
Aluminum extrusion machine
Energy consumption anomaly
Genetic algorithm support vector regression
Confidence interval
