交通环境振动测试数据中暗振动去除的ANFIS法
ANFIS Method to Remove Background Vibration from Traffic Environment Vibration Measured Data

轨道交通引起的环境振动测试数据中混杂着暗振动的成分。提出了一种去除暗振动的自适应神经模糊推理系统（adaptive neuro-fuzzy inference system,简称ANFIS）法，阐述了其基本原理，给出了该法的具体实现步骤。通过一条列车引起的地面振动加速度时程与一条暗振动加速度时程叠加得到现场实测振动加速度时程，采用提出的ANFIS法及其他几种已有方法对该算例进行了去除暗振动的计算，并进行了对比分析。几种方法计算的时程均方根误差分别为：谱幅值修正法0.414 mm/s2，自功率谱法0.363 mm/s2，自互功率谱法0.261 mm/s2，ANFIS法0.074 mm/s2，可见，ANFIS法均方根误差最小；几种方法计算的加权振级VLz分别为：振动级修正法63.842 dB，谱幅值修正法62.894 dB，自功率谱法63.859 dB，自互功率谱法63.802 dB，ANFIS法63.805 dB，ANFIS法计算结果与真实交通振动值63.815 dB最接近。结果表明，在时程、傅里叶谱、功率谱密度及振动级的计算上，ANFIS法计算结果都与真实交通振动值非常接近，产生的误差比其他已有方法更小。
In the field of data test, environment vibration signals induced by rail transit and background vibration signals are inseparable. A new adaptive neuro-fuzzy inference system (ANFIS) method is put forward. Basic principle to banish background vibration is discussed, and the implementation steps of ANFIS are determined. A background vibration acceleration time history is superimposed on a ground vibration induced by a train to synthesize a testing field vibration record, which is used to remove background vibration by the proposed ANFIS method and other existing methods. The comparative analysis of results is carried out. The root mean square errors of time history curves calculated by these methods are 0.414 mm/s2 with the Fourier amplitude revising method, 0.363 mm/s2 with the auto power spectral density(PSD) method, 0.261mm/s2 with auto cross PSD method and 0.074 mm/s2 with the proposed ANFIS method, respectively. The error of ANFIS method is minimal. Also the weighted vibration level VLz values are 63.842 dB with vibration level revising method, 62.894 dB with Fourier amplitude revising method, 63.859 dB with auto PSD method, 63.802 dB with auto-cross PSD method and 63.805 dB with ANFIS method, respectively. The calculation value of ANFIS method is the closest to real value 63.815 dB. The results show that the time history, Fourier spectrum, power spectral density and vibration level obtained with the new ANFIS method are extremely close to true ones of traffic vibration, and the errors are relatively smaller than those of other existing approaches.