多元统计
计算机科学
信号(编程语言)
断层(地质)
频道(广播)
分解
信号处理
投影(关系代数)
噪音(视频)
多元分析
模式识别(心理学)
人工智能
算法
机器学习
数字信号处理
生态学
地震学
图像(数学)
生物
程序设计语言
地质学
计算机网络
计算机硬件
作者
Jie Zhou,Junsheng Cheng,Yang Yu,Junsheng Cheng
标识
DOI:10.1088/1361-6501/ad051b
摘要
Abstract In the early stages of gear faults, the background noise in the signal is pronounced, making it challenging to fully assess the health status of equipment based on a single-channel signal. Processing multi-channel signals proves beneficial for extracting fault information comprehensively. Adaptive multivariate signal decomposition methods, such as multivariate empirical mode decomposition (MEMD) and multivariate local characteristic-scale decomposition (MLCD), employ a fixed multivariate mean curve extraction method for signal decomposition. Consequently, these methods often exhibit suboptimal performance when decomposing different multi-channel signals. This study defines nine multivariate mean curve extraction methods and introduces the multivariate intrinsic wave-characteristic decomposition (MIWD) method based on the principles of mean curve optimization and an adaptive projection method. MIWD dynamically optimizes the multivariate mean curve during the decomposition process, resulting in superior performance in terms of decomposition accuracy, capability, and orthogonality compared to MEMD and MLCD. Furthermore, we apply MIWD to gear fault diagnosis, and simulation and experimental results affirm the superiority of MIWD.
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