High-fidelity noise-reconstructed empirical mode decomposition for mechanical multiple and weak fault extractions

Yuan, Jing; Xu, Chong; Zhao, Qian; Jiang, Huiming; Weng, Yihang

doi:10.1016/j.isatra.2022.02.017

Cited by 18 publications

(7 citation statements)

References 29 publications

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“…Overall, the fidelity of reconstruction increases with an increase in the frequency of items used. However, the fidelity is about 99% when using only the first eight frequency items, as shown in figure 3(e); in fact, 100% reconstruction is unnecessary [39]. Selecting a sufficiently high fidelity combined with specific engineering applications is recommended.…”

Section: Template Design and Optimization For A Complex Uneven Substr...mentioning

confidence: 99%

Discretely-supported transfer nanoimprint anti-reflection nanostructures on complex uneven surface of Fresnel lenses

Wang,

Sun,

Fan

et al. 2023

Nanotechnology

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Nanopatterning complex uneven surface of numerous functional devices to improve their performance is significantly appealing; however, it is extremely challenging. This study proposes a discretely-supported transfer nanoimprint technique to fabricate nanostructures on complex device surfaces containing multi-spatial frequencies. First, a discretely-supported nanoimprint template was designed based on the built energy criterion. A contact fidelity of over 99% was achieved between the designed template and the targeted complex uneven substrate surface. Next, the prefilled nanostructures on the template were transferred to the target surface after contact. By precisely controlling the amount of micro-droplet jetting on the template on-demand, the accumulation of the polymer in the micro-valley sites on the complex substrate was avoided, thus maintaining the morphology and generating function of the devices. Finally, high-quality Fresnel lenses with broadband wide-directional antireflection and excellent imaging performance were developed by imprinting subwavelength-tapered nanostructures on the relief surface.

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Section: Template Design and Optimization For A Complex Uneven Substr...mentioning

confidence: 99%

Discretely-supported transfer nanoimprint anti-reflection nanostructures on complex uneven surface of Fresnel lenses

Wang,

Sun,

Fan

et al. 2023

Nanotechnology

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“…The resonance frequency bands appear at 3000 Hz and 4000 Hz, as shown in figure 4(a). The spectral lines in the spectrum are relatively confusing, so it is impossible to identify the fault types of the bearing and obtain effective information [28]. The inner and outer race fault feature frequencies cannot be found in the envelope spectrum as shown in figure 4(b), and these pulses are quite random and interfered with by redundant spectral lines, which makes it more difficult to separate multiple faults.…”

Section: Simulation Analysismentioning

confidence: 99%

Research on feature extraction and separation of mechanical multiple faults based on adaptive variational mode decomposition and comprehensive impact coefficient

Zhang¹,

Li²,

Li³

et al. 2022

Meas. Sci. Technol.

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Because it is difficult to extract multiple fault features from mechanical equipment under the interference of background noise and the parameters used in variational mode decomposition (VMD) must be determined in advance, a multiple fault separation method based on adaptive variational mode decomposition (AVMD) is proposed in this research to address these issues. Firstly, a novel index, known as the comprehensive impact coefficient (CIC), is established to properly identify the signal's fault features. Thereafter, the fitness function of the sparrow search algorithm is developed based on the CIC, and the VMD parameters selection problem is solved. Finally, the decomposed modal components are subjected to envelop demodulation analysis, and the failure type of the bearing is assessed through the envelope spectrum. The simulation and experimental results reveal that the AVMD method can effectively separate all single faults from multiple faults, thus accurately diagnosing bearing faults.

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“…Therefore, “timely” bearing weak fault detection is quite important for the safe operation of high-end mechanical equipment. The vibration signal contains abundant operation status information on mechanical equipment, and early fault detection based on the vibration signal has been widely researched [ 1 , 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Low-Rank Tensor Estimation Model Based Multichannel Weak Fault Detection for Bearings

Jiang,

Wu,

Yuan

et al. 2024

Sensors

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Multichannel signals contain an abundance of fault characteristic information on equipment and show greater potential for weak fault characteristics extraction and early fault detection. However, how to effectively utilize the advantages of multichannel signals with their information richness while eliminating interference components caused by strong background noise and information redundancy to achieve accurate extraction of fault characteristics is still challenging for mechanical fault diagnosis based on multichannel signals. To address this issue, an effective weak fault detection framework for multichannel signals is proposed in this paper. Firstly, the advantages of a tensor on characterizing fault information were displayed, and the low-rank property of multichannel fault signals in a tensor domain is revealed through tensor singular value decomposition. Secondly, to tackle weak fault characteristics extraction from multichannel signals under strong background noise, an adaptive threshold function is introduced, and an adaptive low-rank tensor estimation model is constructed. Thirdly, to further improve the accurate estimation of weak fault characteristics from multichannel signals, a new sparsity metric-oriented parameter optimization strategy is provided for the adaptive low-rank tensor estimation model. Finally, an effective multichannel weak fault detection framework is formed for rolling bearings. Multichannel data from the repeatable simulation, the publicly available XJTU-SY whole lifetime datasets and an accelerated fatigue test of rolling bearings are used to validate the effectiveness and practicality of the proposed method. Excellent results are obtained in multichannel weak fault detection with strong background noise, especially for early fault detection.

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High-fidelity noise-reconstructed empirical mode decomposition for mechanical multiple and weak fault extractions

Cited by 18 publications

References 29 publications

Discretely-supported transfer nanoimprint anti-reflection nanostructures on complex uneven surface of Fresnel lenses

Discretely-supported transfer nanoimprint anti-reflection nanostructures on complex uneven surface of Fresnel lenses

Research on feature extraction and separation of mechanical multiple faults based on adaptive variational mode decomposition and comprehensive impact coefficient

Adaptive Low-Rank Tensor Estimation Model Based Multichannel Weak Fault Detection for Bearings

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