阔叶材原木声信号特征提取及内部缺陷检测  

Feature extraction of acoustic signal and internal defect detection of hardwood logs based on IPSO-VMD

作　　者：徐锋[1] 吴寅 刘云飞[1] 林海峰[1] Xu Feng;Wu Yin;Liu Yunfei;Lin Haifeng(College of Information Science and Technology,Nanjing Forestry University,Nanjing 210037,China)

机构地区：[1]南京林业大学信息科学技术学院,南京210037

出　　处：《仪器仪表学报》2022年第10期205-214,共10页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(32171788);江苏省重点研发计划(BE2021716)项目资助。

摘　　要：阔叶材原木质量精准检测可实现木材的高效利用和利润最大化,然而因声信号特征参数提取原理及参数与木材性质对应机理不同,致使分析结果存在差异。基于此,提出一种改进型粒子群优化-变分模态分解(IPSO-VMD)的特征参数提取及缺陷检测方法。通过对缺陷信号稀疏特征分析,将最小平均包络熵确定为PSO优化VMD的适应度函数,实现对最优参数(K,α)的搜索,并通过改进惯性权值及学习因子,加快PSO搜索速度并实现全局最优解。基于边际谱及频带能量率实现对IPSO-VMD有效子模的选取,并将其频带分布及能量率作为表征缺陷信号的特征参数,实现对阔叶材原木内部质量的精准检测。实际锯切结果表明,IPSO-VMD方法对原木主要缺陷类型和主次的预测准确率分别达88.6%和72.7%,且对全局参数无法识别的缺陷同样有效。新特征参数的有效性可为后续融合多参数特征,构建人工智能识别系统,实现原木质量精准检测提供可靠依据。The accurate quality detection of hardwood logs can realize efficient utilization and profit maximization of wood.However,due to the difference in extraction principle of acoustic parameters and interaction mechanism between parameters and wood properties,the evaluation results in log quality are different to some extent.On this basis,a method for acoustic feature extraction and defect detection is proposed,which is based on the improved particle swarm optimization-variational modal decomposition(IPSO-VMD).By analyzing the sparse characteristics of defect signals,the minimum average envelope entropy is determined as the fitness function of PSO optimized VMD to search for the optimal combination parameters(K,α).And the search of PSO is accelerated and the global optimal solution is achieved through improving the inertia weight and learning factor of PSO.Then,the effective sub-modes from the IPSO-VMD are selected based on marginal spectrum and energy ratio of sub-band components,and the frequency band distribution and energy ratio of each effective mode are used as the characteristic parameters characterized the defect signal to realize the accurate quality detection of hardwood logs.The actual sawing results show that the major defect types and priorities of logs are detected with accuracy of 88.6%and 72.7%,respectively,and which not identifiable by global parameters could be examined effectively based on the IPSO-VMD method.The effectiveness of the new feature parameters can provide a reliable basis for the accurate detection in log quality through fusing the multi-parameter features and constructing the artificial intelligence recognition system.

关 键 词：阔叶材原木 缺陷检测 变分模态分解 粒子群优化算法 特征提取 

分 类 号：TB529[理学—物理] TH701[理学—声学]