摘要
针对隧道掘进机TBM滚刀在施工过程中磨损快、磨损严重,且缺乏有效检测方法问题,提出基于声发射和改进灰关联度分析的TBM滚刀磨损评估方法。选取无磨损和不同磨损程度的滚刀,通过TBM岩机作用试验平台进行滚刀磨损声波检测,采用声发射数据采集系统采集滚刀的声波;采用最小均方根自适应滤波算法去除声波中的干扰噪声,利用改进灰关联度分析算法计算散点声波多特征参量的综合状态评估值,建立滚刀状态与综合状态评估值区间一一对应的原始数据库。在工程现场TBM滚刀刀座上搭载声发射传感器,对滚刀进行声波检测,然后采用上述方法计算其综合状态评估值;将其在原始数据库中进行比对,找到对应的滚刀状态,实现对TBM滚刀磨损的评估。该方法能有效消除声发射异常散点样本和单一特征参量的影响,准确反映TBM滚刀磨损状态。
Due to the rapid and severe wear of hob of tunnel boring machine(TBM) in construction process and lack of effective detection methods, a wear assessment method for TBM hobs based on acoustic emission and improved grey correlation analysis was proposed. Hobs without wear and with different wear degrees were chosen. The acoustic wave of hob wear was detected by TBM rock machine action test platform, and the acoustic wave of hob was collected by acoustic emission data acquisition system. The least mean square root adaptive filtering algorithm was adopted to remove the interference noise in acoustic wave. The improved grey correlation analysis algorithm was used to calculate the comprehensive state evaluation values of scatter acoustic wave multi-characteristic parameters, and the original database of the hob state corresponding to the comprehensive state evaluation value one by one was established. Acoustic emission sensors were mounted on the base of TBM hob in the engineering site to detect the acoustic wave of the hob, and then the comprehensive state evaluation values were calculated by the above method. The corresponding hob state was found by comparing them in the original database, and the wear evaluation of TBM hob was realized. This method can effectively eliminate the influence of anomalous scatter samples and single characteristic parameters of acoustic emission, and accurately reflect the wear state of TBM hob.
引文
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