Leakage identification for mineral air supply pipeline system based on joint noise reduction and ELM

Lu, Wenjuan; Ye, Chenglong; Zhao, Chang-Jian; Wang, Jinxi; Zhao, Hongliang

doi:10.1016/j.measurement.2023.113304

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…On the basis of this, Wang et al proposed an adaptive parameter optimized variational mode decomposition (AVMD) theory, which strengthened the performance of vibration signal decomposition and fault diagnosis accuracy [14]. And Lu et al proposed a joint noise reduction method that combines parameteroptimized variational mode decomposition (POVMD) and the wavelet denoising method to decompose the vibration signals and performs better denoising [15]. On another direction of research, Lin et al proposed an Iterative Filter (IF) method [16].…”

Section: Introductionmentioning

confidence: 99%

Improved ALIF and its application to rolling bearing fault diagnosis

Wu,

Cao,

Yuan

et al. 2023

Meas. Sci. Technol.

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Adaptive Local Iterative Filtering (ALIF) is a commonly used signal processing method. However, when applied to rolling bearing fault diagnosis, ALIF has been found to have problems such as abnormal interruption, serious mode aliasing, and inconvenient parameter setting. To address these issues, this study proposed an improved Adaptive Local Iterative Filtering (IALIF) method. Based on another assumption, IALIF not only effectively guarantees the stability of the decomposition and the reliability of the results by adaptively dividing the distance and de-oscillation by extreme envelope form, but also provides more convenient parameter setting and effectively suppresses the problem of modal aliasing. In addition, IALIF adds a stop criterion, which greatly improves the efficiency of the algorithm. Finally, the analysis results of simulation signals and experimental signals show that IALIF has good decomposition performance and is practical for rolling bearing fault diagnosis.

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Section: Introductionmentioning

confidence: 99%