基于BLCD分解与ACO-DBN网络的光纤振动信号识别

针对光纤振动信号受噪声干扰严重、特征提取单一和识别时间长的问题,提出了改进的局部特征尺度分解和蚁群算法优化深度置信网络的识别方法。首先,采用三次B样条函数插值拟合均值曲线改进局部特征尺度分解算法,并对原始信号进行分解得到一系列内禀尺度分量之和。其次,利用峭度因子和能谱系数构成融合指标筛选有效分量。然后,分别提取有效分量在时域、频域和时-频域的熵值特征进行融合并降维。最后,将综合特征向量馈入蚁群优化后的深度置信网络进行训练和识别,提高算法效率和识别率。采用实测数据进行实验验证,结果表明,信噪比平均提升8 dB,信号平均识别率可达95.83%,平均识别时间为0.715 s。

Fiber-optic Vibration Signal Recognition Based on BLCD Decomposition and ACO-DBN Network

Aiming at the problems of severe noise interference of fiber-optic vibration signals,single feature extraction and long recognition time,an improved local characteristic-scale decomposition and ant colony algorithm optimize deep belief network are proposed.Firstly,cubic B-spline function interpolation is used to fit the mean curve to improve the local characteristic-scale decomposition algorithm,and the sum of a series of intrinsic scale components is obtained by decomposing the original signal.Secondly,the fusion index is formed by kurtosis factor and energy spectrum coefficient to screen the effective component.Then,the entropy features of the effective components in the time domain,frequency domain and time-frequency domain are extracted respectively to perform feature fusion and dimensionality reduction.Finally,the integrated feature vectors are fed into ant colony algorithm optimized deep belief network for training and recognition to improve the algorithm efficiency and recognition rate.Experimental verification using measured data shows that the signal-to-noise ratio is increased by 8 dB on average,the average signal recognition rate can reach 95.83%,and the average recognition time is 0.715 s.