组分(热力学)
方位(导航)
故障检测与隔离
信号(编程语言)
选择(遗传算法)
计算机科学
断层(地质)
模式识别(心理学)
控制理论(社会学)
独立成分分析
人工智能
工程类
执行机构
物理
控制(管理)
地质学
地震学
热力学
程序设计语言
作者
Yubin Pan,Hua Wang,Jie Chen,Rongjing Hong
标识
DOI:10.1177/01423312241234409
摘要
Slewing bearing is a critical transmission component in large-size construction machinery due to its low-speed and heavy-load conditions. Fault prognostics and health management of slewing bearing are crucial for ensuring their high availability and profitable operation. However, the presence of background noise in construction machinery signals restricts the applicability of existing signal processing approaches in prognostics and health management. To address this challenge, a novel signal de-noising method is proposed based on adaptive decomposition, along with a new strategy for recognizing fault components using statistic detection through kernel principal component analysis (KPCA). First, robust local mean decomposition is utilized to adaptively decompose the fault and normal vibration signal over the entire service life. Then, product functions (PFs) decomposed by fault and normal vibration signal are used for KPCA anomaly detection. Finally, the fault PFs are reconstructed to obtain the de-noised signal. The effectiveness of the proposed method is validated through the use of both simulated and experimental vibration signals obtained from a slewing-bearing life-cycle test. The results illustrate that the proposed method has superior de-noising capability and decomposition efficiency, making it an effective signal preprocessing technique for prognostics and health management.
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