CAJ | 학술논문

为有效控制工程机械驾驶室内噪声，利用集合经验模态分解（ ensemble empirical mode decomposition， EEMD）后的本征模函数作为稳定独立成分分析（independent component analysis，ICA）算法中的多个虚拟通道，提出了基于 EEMD 和 ICA 相结合的驾驶室内噪声盲源分离方法。通过分析仿真信号验证了 EEMD-ICA 方法研究复杂非平稳信号可行。结合相干分析、时频分析方法研究推土机驾驶室内噪声特性。结果表明，柴油机的1/2阶、1阶转动频率是驾驶室内相关零部件的振动辐射噪声的主要激励来源，柴油机的燃烧噪声也是室内噪声的来源。通过相干分析与时频分析相结合的技术可较准确实现噪声源定位，结合测试对象的相关常识可实现对噪声类型判别、噪声传入途径等复杂的问题进行研究，为进一步实现驾驶室内噪声治理、故障诊断，提供经济实用的分析手段。
위유효공제공정궤계가사실내조성，이용집합경험모태분해（ ensemble empirical mode decomposition， EEMD）후적본정모함수작위은정독립성분분석（independent component analysis，ICA）산법중적다개허의통도，제출료기우 EEMD 화 ICA 상결합적가사실내조성맹원분리방법。통과분석방진신호험증료 EEMD-ICA 방법연구복잡비평은신호가행。결합상간분석、시빈분석방법연구추토궤가사실내조성특성。결과표명，시유궤적1/2계、1계전동빈솔시가사실내상관령부건적진동복사조성적주요격려래원，시유궤적연소조성야시실내조성적래원。통과상간분석여시빈분석상결합적기술가교준학실현조성원정위，결합측시대상적상관상식가실현대조성류형판별、조성전입도경등복잡적문제진행연구，위진일보실현가사실내조성치리、고장진단，제공경제실용적분석수단。
In order to effectively control cabin noise and improve noise quality,combined techniques of ensemble empir-ical mode decomposition(EEMD)and an improvement of independent component analysis( ICA)were applied to analysze cabin noise. Intrinsic mode functions resulting from EEMD analysis were used as the virtual channels of ICA, which solved an undetermined problem of blind source identification from single sampling signal. The feasibility of EE-MD-ICA method for complex non-stationary signals was verified by analyzing the simulated signals. With the experi-ment and coherence analysis for cabin interior and outside noise and vibration signals of a bulldozer,the technique of EEMD-ICA was effective on the noise source separation and identification. Meanwhile,techniques of coherence analy-sis and time-frequency analysis were used for accurate noise sources identification. The results showed that the main cabin noise source was combustion noise of diesel engine and mechanical radiation noise of cabin parts caused by 1 / 2 order and 1 order rotation frequency of the diesel engine. And generation details of noise source,transmission paths of air-borne or structure-borne noise were well analyzed in order to control the interior noise and identify faults by the tech-niques of coherence analysis and time-frequency analysis.