TB52 + 9 TH701
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.
徐 锋,吴 寅,刘云飞,林海峰.阔叶材原木声信号特征提取及内部缺陷检测[J].仪器仪表学报,2022,43(10):205-214复制