基于FESN的結構健康狀態(tài)智能預測研究
發(fā)布時間：2018-12-30 20:50
【摘要】：在實際生活中,大型建筑結構和設備在服役過程中會或多或少的出現損傷問題,如果沒有被及時發(fā)現和處理,往往會造成人財兩盡的嚴重后果,所以結構的健康監(jiān)測、診斷、評估和預測顯得尤為重要。本文在結構健康監(jiān)測的前提下,以結構健康狀態(tài)趨勢預測為目的,進行結構損傷特征的分析與提取,研究結構的損傷趨勢。具體的研究內容如下:研究了基于經驗小波變換(Empirical Wavelet Transform,簡稱EWT)的信號預處理方法,利用EWT對采集的結構損傷加速度振動信號的頻譜進行自適應分割,構造出合適且正交的小波帶通濾波器組,得到具有緊支撐傅里葉頻譜的調幅-調頻(Amplitude Modulated-Frequency Modulated,簡稱AM-FM)分量,再提取出包含豐富損傷信息的分量,對其進行Hilbert變換,計算出瞬時頻率和瞬時幅值。實驗結果表明:瞬時頻率能夠反映出結構發(fā)生損傷前后的剛度變化形式,而且檢測節(jié)點位置不同或者損傷工況不同其瞬時頻率都會有明顯的差異,故將其作為結構健康狀態(tài)的預測指標,可以很好地反映結構健康狀態(tài)的變化趨勢,為進一步的損傷趨勢預測奠定了基礎。研究了模糊理論和回聲狀態(tài)網絡相結合的非線性時間序列預測方法,對其推理算法、訓練過程和網絡的關鍵參數進行了詳細地研究說明,并對該網絡算法的穩(wěn)定性能進行嚴格的定義。實驗結果表明:選取合適的參數對預測精度有一定影響,采用雙曲正切(tanh)型神經元激活函數的預測精度比泄露(leaky)型更高,相比于傳統的回聲狀態(tài)網絡,模糊回聲狀態(tài)網絡(Fuzzy Echo State Network,簡稱FESN)的非線性逼近能力強,預測精度高且能夠處理較大的樣本數據,訓練效率也有一定的提高,為實際工程結構的健康狀態(tài)預測提供了理論依據。研究了基于FESN的結構健康狀態(tài)趨勢預測方法,應用EWT方法提取出結構內部具有損傷信息的AM-FM分量,并進行Hlibert變換,得到瞬時頻率,再將其作為預測模型的輸入。應用FESN分別對單自由度結構和多自由度結構模型進行工程仿真預測,并應用于實際工程數據的預測。實驗結果表明:FESN預測模型更加逼近真實值,預測精度更高。
[Abstract]:In real life, large building structures and equipment will be damaged more or less in the course of service. If they are not detected and dealt with in time, they will often result in serious consequences for both human and financial resources. Therefore, structural health monitoring and diagnosis, Evaluation and prediction are particularly important. On the premise of structural health monitoring, this paper analyzes and extracts the structural damage characteristics and studies the damage trend of the structure in order to predict the trend of structural health state. The specific research contents are as follows: the signal preprocessing method based on empirical wavelet transform (Empirical Wavelet Transform,) is studied, and the spectrum of structural damage acceleration vibration signal collected by EWT is segmented adaptively by EWT. An appropriate and orthogonal wavelet bandpass filter bank is constructed to obtain the amplitude modulation-frequency modulation (AM-FM) component with compact support Fourier spectrum, and then extract the component which contains abundant damage information, and then carry on the Hilbert transform to it. The instantaneous frequency and amplitude are calculated. The experimental results show that the instantaneous frequency can reflect the stiffness change of the structure before and after the damage, and the instantaneous frequency will be obviously different with the different location of the detection node or the different damage condition. Therefore, taking it as a predictor of structural health state can well reflect the changing trend of structural health state and lay a foundation for further prediction of damage trend. The nonlinear time series prediction method based on fuzzy theory and echo state network is studied. The reasoning algorithm, the training process and the key parameters of the network are studied in detail. And the stability of the network algorithm is strictly defined. The experimental results show that choosing appropriate parameters has certain influence on the prediction accuracy. The prediction accuracy of hyperbolic tangent (tanh) neuron activation function is higher than that of leaking (leaky) type, compared with the traditional echo state network. Fuzzy echo state network (Fuzzy Echo State Network,) has strong nonlinear approximation ability, high prediction accuracy and ability to deal with large sample data, and the training efficiency is also improved to a certain extent. It provides a theoretical basis for the prediction of the health state of practical engineering structures. In this paper, the structure health trend prediction method based on FESN is studied. The AM-FM component with damage information is extracted by EWT method, and the instantaneous frequency is obtained by Hlibert transform, which is used as the input of the prediction model. The single degree of freedom structure and multi-degree-of-freedom structure model are simulated by FESN and applied to the prediction of practical engineering data. The experimental results show that the FESN prediction model is more close to the real value and the prediction accuracy is higher.
【學位授予單位】：長安大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TU317
本文編號：2396119
[Abstract]:In real life, large building structures and equipment will be damaged more or less in the course of service. If they are not detected and dealt with in time, they will often result in serious consequences for both human and financial resources. Therefore, structural health monitoring and diagnosis, Evaluation and prediction are particularly important. On the premise of structural health monitoring, this paper analyzes and extracts the structural damage characteristics and studies the damage trend of the structure in order to predict the trend of structural health state. The specific research contents are as follows: the signal preprocessing method based on empirical wavelet transform (Empirical Wavelet Transform,) is studied, and the spectrum of structural damage acceleration vibration signal collected by EWT is segmented adaptively by EWT. An appropriate and orthogonal wavelet bandpass filter bank is constructed to obtain the amplitude modulation-frequency modulation (AM-FM) component with compact support Fourier spectrum, and then extract the component which contains abundant damage information, and then carry on the Hilbert transform to it. The instantaneous frequency and amplitude are calculated. The experimental results show that the instantaneous frequency can reflect the stiffness change of the structure before and after the damage, and the instantaneous frequency will be obviously different with the different location of the detection node or the different damage condition. Therefore, taking it as a predictor of structural health state can well reflect the changing trend of structural health state and lay a foundation for further prediction of damage trend. The nonlinear time series prediction method based on fuzzy theory and echo state network is studied. The reasoning algorithm, the training process and the key parameters of the network are studied in detail. And the stability of the network algorithm is strictly defined. The experimental results show that choosing appropriate parameters has certain influence on the prediction accuracy. The prediction accuracy of hyperbolic tangent (tanh) neuron activation function is higher than that of leaking (leaky) type, compared with the traditional echo state network. Fuzzy echo state network (Fuzzy Echo State Network,) has strong nonlinear approximation ability, high prediction accuracy and ability to deal with large sample data, and the training efficiency is also improved to a certain extent. It provides a theoretical basis for the prediction of the health state of practical engineering structures. In this paper, the structure health trend prediction method based on FESN is studied. The AM-FM component with damage information is extracted by EWT method, and the instantaneous frequency is obtained by Hlibert transform, which is used as the input of the prediction model. The single degree of freedom structure and multi-degree-of-freedom structure model are simulated by FESN and applied to the prediction of practical engineering data. The experimental results show that the FESN prediction model is more close to the real value and the prediction accuracy is higher.
【學位授予單位】：長安大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TU317
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