铝锭铸模裂纹的检测方法与装置研究
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摘要
铝锭连续铸造生产线是由很多设备构成的生产链,是用于生产重熔铝锭的自动化生产线。这种链式的生产设备,一旦其中的某一部分发生故障就会导致整个系统不能正常运行,所以铝锭连续铸造生产线的故障诊断工作也迫在眉睫。基于这个庞大的系统工程,我们选择铸造机上所用铸模产生的故障进行研究。
在铸造机生产过程中,铸模由链条传动连续运转,在高温铝水浇注到铸模后,为了使铝液尽快凝固,需要让铸模外壳在冷却水槽的循环冷水中快速冷却。由于承载铝锭的铸模长期处于冷热变化剧烈的工作环境,铸模壳体难免会因为疲劳而产生裂纹,甚至裂缝。如果裂缝达到一定深度,当高温铝水浇注到铸模后,由于裂缝导致高温铝水漏入水中产生爆炸,这样就会使整个生产系统中断,造成重大安全生产事故。因此,铝锭铸模的安全检测就显得尤为重要。
传统方法是根据人的眼睛、耳朵识别以及经验来判断,’这种方法既不科学而且效率低,本课题准备引用小波分析、模糊滤波、FFT等方法,对铸模进行裂纹故障诊断。具体步骤为在实验室构建一个包括激振装置、传感器、测振仪、微型计算机的检测系统平台,利用系统中的传感器采集敲击铸模产生的振动信号,然后运用MATLAB软件的分析和数据处理技术对采集到的敲击振动信号进行分析处理,从而来辨别铝锭铸模的疲劳损伤程度,并判别裂纹发展的趋势,使故障检测及时可靠。
本方法脱离了人为因素的干扰,提高裂纹识别速度和效率,从而达到避免事故、提高生产效率的目的。可见,进行故障诊断的研究,对于保障设备安全可靠运行,促进国民经济发展具有重大意义,同时对提高企业的竞争力也具有很大帮助。因此,本研究对象将具有可观的经济价值和明显的社会意义。
Aluminum ingot continuous casting production line is composed by a number of equipment production chain, which is melted aluminum ingots for the production of automated production lines. This chain of production equipment, once a part of the failure of which would cause the entire system can not operate normally. Therefore, aluminum ingot continuous casting production line fault diagnosis is imminent, According to this huge project, we chose to mold failures in casting machine conduct the research.
During the production process of the casting machine, the mold continuous operation by the chain of transmission, after the high-temperature aluminum water pouring into the mold, in order to solidify the aluminum water as soon as possible, we made the molten aluminum in the cooling tank shell mold cycle of cold water to quickly cool. Due to the mold used in carrying aluminum ingots in the hot and cold working environment long time, because of the fatigue in the mold surface, which arises inevitably the crack, even rift. if the crack reaches a certain depth, after the high temperature aluminum water is poured in aluminum mold, cracks caused by the high-temperature aluminum water leakage into the water to produce an explosion, which will result in interruption of the production system, even cause serious industrial accidents. Therefore, the safety of aluminum ingot mold detection is particularly important.
The traditional method is according to the eye, ear recognition, and the experience to judge, this method is neither scientific nor efficient. This topic prepares to quote wavelet analysis, fuzzy filtering, FFT and other methods to casting for crack diagnosis. The concrete steps as follows, we construct one examination system platform in the laboratory, including the exciting shake the device, sensor, vibration meter, micro-computer, using sensors collecting knocking vibration signals generated by the mold, and then analyze by using MATLAB software and data processing Technology on the collected vibration signal analysis and processing knocking, then distinguishe the aluminum ingot mold the weary degree, and detection the development trend of crack, so that make the fault detection timely and reliably.
This method get rid of artificial factor interference and improve the crack distinguish of speed and efficiency, so as to achieve the purpose, which to avoid accidents and improve production efficiency. Thus, the research of the fault diagnosis method has great significance to support equipment for safe and reliable operation and to promote the development of national economy. The method also can improve the competitiveness of enterprises at the same time. Therefore, this study will have considerable economic value and obvious the social significance.
在铸造机生产过程中,铸模由链条传动连续运转,在高温铝水浇注到铸模后,为了使铝液尽快凝固,需要让铸模外壳在冷却水槽的循环冷水中快速冷却。由于承载铝锭的铸模长期处于冷热变化剧烈的工作环境,铸模壳体难免会因为疲劳而产生裂纹,甚至裂缝。如果裂缝达到一定深度,当高温铝水浇注到铸模后,由于裂缝导致高温铝水漏入水中产生爆炸,这样就会使整个生产系统中断,造成重大安全生产事故。因此,铝锭铸模的安全检测就显得尤为重要。
传统方法是根据人的眼睛、耳朵识别以及经验来判断,’这种方法既不科学而且效率低,本课题准备引用小波分析、模糊滤波、FFT等方法,对铸模进行裂纹故障诊断。具体步骤为在实验室构建一个包括激振装置、传感器、测振仪、微型计算机的检测系统平台,利用系统中的传感器采集敲击铸模产生的振动信号,然后运用MATLAB软件的分析和数据处理技术对采集到的敲击振动信号进行分析处理,从而来辨别铝锭铸模的疲劳损伤程度,并判别裂纹发展的趋势,使故障检测及时可靠。
本方法脱离了人为因素的干扰,提高裂纹识别速度和效率,从而达到避免事故、提高生产效率的目的。可见,进行故障诊断的研究,对于保障设备安全可靠运行,促进国民经济发展具有重大意义,同时对提高企业的竞争力也具有很大帮助。因此,本研究对象将具有可观的经济价值和明显的社会意义。
Aluminum ingot continuous casting production line is composed by a number of equipment production chain, which is melted aluminum ingots for the production of automated production lines. This chain of production equipment, once a part of the failure of which would cause the entire system can not operate normally. Therefore, aluminum ingot continuous casting production line fault diagnosis is imminent, According to this huge project, we chose to mold failures in casting machine conduct the research.
During the production process of the casting machine, the mold continuous operation by the chain of transmission, after the high-temperature aluminum water pouring into the mold, in order to solidify the aluminum water as soon as possible, we made the molten aluminum in the cooling tank shell mold cycle of cold water to quickly cool. Due to the mold used in carrying aluminum ingots in the hot and cold working environment long time, because of the fatigue in the mold surface, which arises inevitably the crack, even rift. if the crack reaches a certain depth, after the high temperature aluminum water is poured in aluminum mold, cracks caused by the high-temperature aluminum water leakage into the water to produce an explosion, which will result in interruption of the production system, even cause serious industrial accidents. Therefore, the safety of aluminum ingot mold detection is particularly important.
The traditional method is according to the eye, ear recognition, and the experience to judge, this method is neither scientific nor efficient. This topic prepares to quote wavelet analysis, fuzzy filtering, FFT and other methods to casting for crack diagnosis. The concrete steps as follows, we construct one examination system platform in the laboratory, including the exciting shake the device, sensor, vibration meter, micro-computer, using sensors collecting knocking vibration signals generated by the mold, and then analyze by using MATLAB software and data processing Technology on the collected vibration signal analysis and processing knocking, then distinguishe the aluminum ingot mold the weary degree, and detection the development trend of crack, so that make the fault detection timely and reliably.
This method get rid of artificial factor interference and improve the crack distinguish of speed and efficiency, so as to achieve the purpose, which to avoid accidents and improve production efficiency. Thus, the research of the fault diagnosis method has great significance to support equipment for safe and reliable operation and to promote the development of national economy. The method also can improve the competitiveness of enterprises at the same time. Therefore, this study will have considerable economic value and obvious the social significance.
引文
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[21]Ypma A. Learning methods for machine vibration analysis and health monitoring [M]. Delft University of Technology,2001,67-70
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[29]张海军,温广瑞,屈梁生.一种提高诊断信息质量的方法[J].西安交通大学学报,2002,36(3)：295-299
[30]杨国安,钟秉林,黄仁等.机械故障信号小波包分解的时域特征提取方法研究[J].振动与冲击,2001,20(2)：25-28
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[2]谢志江.设备状态监测与故障诊断技术[M].重庆：重庆大出版社,1997,20-60
[3]徐敏.设备故障诊断手册—机械设备状态监测和故障诊断[M].西安：西安交通大学出版社,1998,6-46
[4]严新平.国外状态监测和诊断工程管理技术的发展[M].北京：全国设备诊断技术会议文集,1997,16-52
[5]程松波.涡流探伤在铁路设施裂纹检测中的研究与应用[D].[武汉理工大学硕士学位论文].武汉：武汉理工大学,2006,24-60
[6]陆建湖,黄文,毛汉领.机械设备振动监测与故障诊断的发展与展望[J].仪器仪表与分析监测,1999,(3)：1-4
[7]李纯忠,熊毅刚.连铸设备[M].沈阳：东北大学出版社,1993,21-120
[8]姜永林.连铸生产概论[M].沈阳：东北工学院出版社,1992,15-123
[9]段晨东,李凌均,何正嘉.第二代小波变换在旋转机械故障诊断中的应用[J].机械科学与技术,2004,2(2)：224-229
[10]韩捷,张瑞林.旋转机械故障机理及诊断技术[M].北京：机械工业出版社,1997,8-20
[11]巨怀深,数字信号处理教程—MATLAB释义与实现[M].北京：电子工业出版社,2004,30-70
[12]卢文祥,杜润生.机械工程测试信号分析[M].武汉：华中科技大学出版社,1999,50-100
[13]杨国安,许云飞等.基于小波包和包络解调的多类故障综合诊断方法研究[J].东南大学学报,2004,34(9)：265-269
[14]钱立军,蒋东翔.小波变换在横向裂纹转子升速过程状态监测中的应用[J].中国电机工程学报,2003,5(5)：86-89
[15]徐玉秀,原培新,杨文平.复杂机械故障诊断的分形与小波方法[M].北京：机械工业出版社,2000.33-50
[16]文莉,刘正士,葛运建.小波去噪的几种方法[J].合肥工业大学学报,自然科学版,2002,25(2)：167-172
[17]杨其俊,裴峻峰,孙辉.小波消噪及其在往复泵振动监测信号处理中的应用[J].振动与冲击,2000,19(2)：20-23
[18]赵晓艳,李宏男.一种改进的小波消噪方法及其在健康诊断中的应用[J].振动 与冲击,2007,10(10)：137-139
[19]何正嘉,孟庆丰等.机械设备非平稳信号的故障诊断原理及应用[M].北京：高等教育出版社,2001,25-41
[20]朱利民,钟秉林.振动信号短时分析方法及在机械故障诊断中的应用[J].振动工程学报,2000,9(2)：33-36
[21]Ypma A. Learning methods for machine vibration analysis and health monitoring [M]. Delft University of Technology,2001,67-70
[22]冉启文.小波分析方法及其应用[M].哈尔滨：哈尔滨工业大学出版社,1995,45-123
[23]Cardoso J F, Souloumiac A. Blind beam forming for non-Gaussian signals[J]. Radar and Signal Processing, IEEE Proceedings F,1993,140(6):362-370
[24]胡昌华,周涛,贾启兵等.基于MATLAB的系统分析与设计时频分析[M].西安：西安电子科技大学出版社,2001,95-130
[25]胡昌华,张军波,夏军.基于MATLAB的系统分析与设计—小波分析[M].西安：西安电子科技大学出版社,2000,156-248
[26]Thorsen O, Dalva M,Methods of condition monitoring and fault diagnosis for induction Motors [J].European Trans on Electrical Power,1998,8(5):383-395
[27]Chui C K.An introduction to wavelets[M]. New York:a cademic Press.1992. 20-27
[28]徐晨,赵瑞敏.小波分析应用算法[M].北京：科学出版社,2004,45-123
[29]张海军,温广瑞,屈梁生.一种提高诊断信息质量的方法[J].西安交通大学学报,2002,36(3)：295-299
[30]杨国安,钟秉林,黄仁等.机械故障信号小波包分解的时域特征提取方法研究[J].振动与冲击,2001,20(2)：25-28
[31]Yang, Ruikang; Gabbouj, Moncef; Neuvo, Yrjs. Fast algorithms for analyzing and designing weighted median filters[J].5i-gnat Processing,1995,41(2): 135-152
[32]王宏.MATLAB 6.5及其在信号处理中的应用[M].北京：清华大学出版社,2004,100-145
[33]I Daubechies.Ten lectures on wavelets[M]. SIAM,Philadephia,PA,1992,20-59.
[34]张鹏,孟光,吴峰崎.小波分析降噪方法在碰磨和突加不平衡信号特征提取中的应用[J].汽轮机技术,2005,12(4)：100-104
[35]Y.Wu, R.Du. Feature extraction and assessment using wavelet for monitoring of machining processes[J].Mechanical System and Signal Process,1996,10(1):29-53
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