古建筑木结构监测数据异常诊断
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摘要
针对监测数据中异常因素所引起的响应,提出了基于统计过程控制的古建筑木结构应变监测数据异常变化的诊断方法。以某藏式古建筑木结构监测数据为对象,建立了应变增量与温度增量、时间效应的多元滞后回归模型,进而消除温度增量和时间效应对响应的影响,采用季节乘积ARIMA-GARCH模型对序列中显著存在的自相关性、周期性和异方差性进行控制,最后采用均值-移动极差控制图、EWMA控制图和EWRMS控制图对异常进行诊断。分析结果表明,异常诊断结果具有较高的可靠性,通过游客数量与异常结果的对比,说明游客数量对于结构的稳定存在不可忽略的影响,此结果可为游客数量的控制提供参考。
Due to the responses caused by the abnormal factors in monitoring data,the present study develops the diagnosis method for abnormal change of strain data of the ancient wood structure based on statistical process control.The monitoring data of an ancient Tibetan wood structure is used to establish the multiple linear regression model between the structure strain and the temperature or time,thus eliminating the temperature and time effects in structure strain.Then the estimation of multiplicative seasonal ARIMA-GARCH model is adopted to take control of the autocorrelation,periodicity and heteroscedasticity in the data.Finally,the Individual-Moving Range control chart,EWMA control chart and EWRMS control chart are used to diagnose the abnormal events.The analysis results indicate the high reliability of the method.Through the comparison of the number of tourists and the number of abnormalities,it shows that the number of tourists has nonnegligible influence on the stability of the structure,which can provide a reference for the control of the number of the tourists.
Due to the responses caused by the abnormal factors in monitoring data,the present study develops the diagnosis method for abnormal change of strain data of the ancient wood structure based on statistical process control.The monitoring data of an ancient Tibetan wood structure is used to establish the multiple linear regression model between the structure strain and the temperature or time,thus eliminating the temperature and time effects in structure strain.Then the estimation of multiplicative seasonal ARIMA-GARCH model is adopted to take control of the autocorrelation,periodicity and heteroscedasticity in the data.Finally,the Individual-Moving Range control chart,EWMA control chart and EWRMS control chart are used to diagnose the abnormal events.The analysis results indicate the high reliability of the method.Through the comparison of the number of tourists and the number of abnormalities,it shows that the number of tourists has nonnegligible influence on the stability of the structure,which can provide a reference for the control of the number of the tourists.
引文
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[2]陆铮.光纤光栅用于木结构健康监测技术[J].住宅科技,2005,(11):25-28.Lu Zheng.FBG used in health monitor of timberwork[J].Housing Science,2005,(11):25-28.
[3]符映红,柳盛霖,毛江鸿,等.台风作用下木构古建筑变形监测与特征研究[J].山西建筑,2016,4(11):30-31.Fu Yinghong,Liu Shenglin,Mao Jianghong,et al.Deformation monitoring and features study for ancient wooden building under typhoon action[J].Shanxi Architecture,2016,4(11):30-31.
[4]周纪芗,茆诗松.质量管理统计方法[M].北京:中国统计出版社,2006.Zhou Jixiang,Mao Shisong.Quality Management Statistical Methods[M].Beijing:China Statistics Press,2006.
[5]王真,程远胜.基于受控结构振动响应的控制图损伤识别[J].工程力学,2009,26(8):194-200.WANG Zhen,CHENG Yuansheng.Damage identification using time domain response of structural under control and control charts[J].Engineering Mechanics,2009,26(8):194-200.
[6]Jin R R,Xu B.Structural damage detection based on principal component analysis and Hotelling T2 control chart[C].Changsha:Institute of Electrical and Electronics Engineers(IEEE),2010:257-263
[7]邓杨,李爱群,丁幼亮,等.基于长期监测数据的大跨桥梁结构伸缩缝损伤识别[J].东南大学学报(自然科学版),2011,41(2):336-341.Deng Yang,Li Ai-qun,Ding Youliang,et al.Damage identification of expansion joints in long span bridge using long-term monitoring data[J].Journal of Southeast University(Natural Science Edition),2011,41(2):336-341.
[8]邓杨,李爱群,刘扬.桥梁模态频率异常变化的概率检测方法及例证[J].振动工程学报,2015,28(6):887-895.Deng Yang,Li Ai-qun,Liu Yang.Probabilistic detection method and its illustration for abnormal change of bridge’s modal frequencies[J].Journal of Vibration Engineering,2015,28(6):887-895.
[9]Bai X B,Yang N,Yang Q S.Study on model of strain and temperature for Tibetan ancient building based on long-term monitoring data[C].The 14th International Symposium on Structural Engineering,Beijing China,2016.
[10]庄楚强,何春雄.应用数理统计基础[M].广州:华南理工大学出版社,2006.Zhuang Chuqiang,He Chunxiong.Apply Mathematical Statistics[M].Guangzhou:South China University of Technology Press,2006.
[11]杨叔子,吴雅.时间序列分析的工程应用[M].武汉:华中理工大学出版社,1992.Yang Shuzi,Wu Ya.Engineering Application of Time Series Analysis[M].Wuhan:Huazhong University of Science and Technology Press,1992.
[12]Alwan L.Statistical Process Analysis[M].McGrawHill,New York,NY,2000.
[13]Brockwell P,Davis R.Introduction to time series and forecasting[J].Springer-Verlag,1996,39(4):426.
[14]Ducan A L.Quality Control And Industrial Statistics[M].Irwin Homewood,IL,1986.
[15]Nelson L.Technical aids:The shewhart control charttests for special causes[J].Journal of Quality Technology,1984,16(4):238-239.
[16]Gregor J F,Harris T J.The exponentially weighted moving variance[J].Journal of Quality Control Technology,1993,25(2):106-118.
[17]Rashid K M J.Design of an exponentially weighted moving average(EWMA)and an exponentially weignted root mean square(EWRMS)control chart[J].International Journal of Advanced Engineering Research and Science,2017,4(3):88-91.