Fault impulses induced by localized failure is critical for fault diagnosis and degradation prognostics of rotating machinery, however, weak transient fault impulses are always submerged in heavy noise and unrelated components. To overcome this bottleneck issue, a novel approach named smoothing sparse low-rank matrix (SSLRM) associated with asymmetric and singular value decomposition (SVD) penalty regularizers is proposed, for the first time. To be specific, the asymmetric and SVD penalty regularizers are utilized to alleviate the issues of underestimation deficiency of traditional sparse regularization, where the Parseval theorem in wavelet framework and smoothing operation are conducted, so as to enhance the sparsity of the estimated transient impulses while restraining the unrelated interference. Meanwhile, the strict convexity of the cost function is proved theatrically, and the effective optimization algorithm with fast convergence speed based upon the alternating direction method of multipliers (ADMM) is proposed through splitting the optimization function into two simple sub-parts. The simulated case and two experimental cases regarding bearings failure in corn thresher and gear-root crack failure in reducer are investigated for theoretical verification of the proposed approach, the results indicate that diagnosis accuracy and fault impulses amplitude of the algorithm are superior to the state-of-the-art benchmarks. 

New sparse regularization approach for extracting transient impulses from fault vibration signal of rotating machinery

This study examined recent advances in remote sensing (RS) techniques used for the quantitative monitoring of groundwater storage changes and assessed their current capabilities and limitations. The evolution of the techniques analyses spans from empirical reliance on sparse point data to the assimilation of multi-platform satellite measurements using sophisticated machine learning algorithms. Key developments reveal enhanced characterisation of localised groundwater measurement by integrating coarse-resolution gravity data with high-resolution ground motion observations from radar imagery; Notable advances include improved accuracy achieved by integrating Gravity Recovery and Climate Experiment (GRACE) and Interferometric Synthetic Aperture Radar (InSAR) data. Cloud computing now facilitates intensive analysis of large geospatial datasets to address groundwater quantification challenges. While significant progress has been made, ongoing constraints include coarse spatial and temporal resolutions limiting basin-scale utility, propagation of uncertainties from sensor calibrations and data merging, and a lack of systematic validation impeding operational readiness. Addressing these limitations is critical for continued improvement of groundwater monitoring techniques. This review identifies promising pathways to overcome these limitations, emphasising standardised fusion frameworks for satellite gravimetry, radar interferometry, and hydrogeophysical techniques. The development of robust cloud-based modelling platforms for multi-source subsurface information assimilation is a key recommendation, highlighting the potential to significantly advance groundwater quantification accuracy. This comprehensive review serves as a valuable resource for water resource and remote sensing experts, providing insights into the evolving landscape of methodologies and paving the way for future advancements in groundwater storage monitoring tools. 

Remote sensing technologies for modelling groundwater storage dynamics: Comprehensive review

To address the challenge of detecting early faults in rolling bearings, where weak fault features are occasionally obscured by background noise, an innovative early fault diagnosis method based on adaptive minimum noise amplitude deconvolution (MNAD) is introduced. Initially, a correlation Gini index function is defined to estimate the fault period, while leveraging the iterative advantages of MNAD to progressively approach the true fault period. This resolution effectively overcomes the requirement of prior knowledge regarding the fault period, thus reducing the number of input parameters. Subsequently, a composite index, combining square envelope Gini index and square envelope entropy, is constructed as the fitness function. The sand cat swarm optimization algorithm is employed to adaptively determine the optimal noise ratio and filter length for deconvolution, ensuring the acquisition of the finest filtered signal. Ultimately, envelope spectrum analysis is conducted on the filtered signal to extract fault features and facilitate early fault diagnosis. The effectiveness of the proposed method is validated through simulated and experimental data, highlighting its superior feature extraction capabilities and robustness compared to original MNAD and other sophisticated blind deconvolution methods. 

Adaptive minimum noise amplitude deconvolution and its application for early fault diagnosis of rolling bearings

The estimation of individual biomass components within tree crowns, such as dead branches (DB), needles (NB), and branch biomass (BB), has received limited attention in the scientific literature despite their significant contribution to forest biomass. This study aimed to assess the potential of multispectral LiDAR data for estimating these biomass components in a multi-layered Abies borissi-regis forest. Destructive (i.e., 13) and non-destructive (i.e., 156) field measurements were collected from Abies borisii-regis trees to develop allometric equations for each crown biomass component and enrich the reference data with the non-destructively sampled trees. A set of machine learning regression algorithms, including random forest (RF), support vector regression (SVR) and Gaussian process (GP), were tested for individual-tree-level DB, NB and BB estimation using LiDAR-derived height and intensity metrics for different spectral channels (i.e., green, NIR and merged) as predictors. The results demonstrated that the RF algorithm achieved the best overall predictive performance for DB (RMSE% = 17.45% and R2 = 0.89), NB (RMSE% = 17.31% and R2 = 0.93) and BB (RMSE% = 24.09% and R2 = 0.85) using the green LiDAR channel. This study showed that the tested algorithms, particularly when utilizing the green channel, accurately estimated the crown biomass components of conifer trees, specifically fir. Overall, LiDAR data can provide accurate estimates of crown biomass in coniferous forests, and further exploration of this method’s applicability in diverse forest structures and biomes is warranted.

https://www.mdpi.com/2072-4292/15/11/2919/pdf?version=1685786619

Estimating Crown Biomass in a Multilayered Fir Forest Using Airborne LiDAR Data

Digital aerial photogrammetry (DAP) has emerged as an alternative to airborne laser scanning (ALS) for forest inventory applications, as it offers a low-cost and flexible three-dimensional (3D) point cloud. Unlike the forest inventory attributes (e.g., tree height and diameter at breast height), the relative ability of DAP and ALS in predicting canopy structural variables (i.e., canopy cover and leaf area index (LAI)) has not been sufficiently investigated by previous studies. In this study, we comprehensively compared the canopy cover and LAI estimates using DAP- and ALS-based methods over 166 selected tropical forest sample plots with seven different tree species and forest types. We also explored the relationship between field-measured aboveground biomass (AGB) and the LAI estimates. The airborne LAI estimates were subsequently compared with the Sentinel-2-based LAI values that were retrieved using a one-dimensional radiative transfer model. The results demonstrated that the DAP-based method generally overestimated the two canopy variables compared to ALS-based methods but with relatively high correlations regardless of forest type and species (R2 of 0.80 for canopy cover and R2 of 0.76 for LAI). Under different forest types and species, the R2 of canopy cover and LAI range from 0.64 to 0.89 and from 0.54 to 0.87, respectively. Apparently, different correlations between AGB and LAI were found for different forest types and species where the mixed coniferous and broad-leaved forest shows the best correlation with R2 larger than 0.70 for both methods. The comparison with satellite retrievals verified that the ALS-based estimates are more consistent with Sentinel-2-based estimates than DAP-based estimates. We concluded that DAP data failed to provide analogous results to ALS data for canopy variable estimation in tropical forests.

/pdf/comparison-of-canopy-cover-and-leaf-area-index-estimation-2i39lzjm.pdf

Comparison of Canopy Cover and Leaf Area Index Estimation from Airborne LiDAR and Digital Aerial Photogrammetry in Tropical Forests

Incipient detection of bearing fault using impulse feature enhanced weighted sparse representation

Forest aboveground biomass (AGB) is an important indicator for characterizing forest ecosystem structures and functions. Therefore, how to effectively investigate forest AGB is a vital mission. Airborne laser scanning (ALS) has been demonstrated as an effective way to support investigation and operational applications among a wide range of applications in the forest inventory. Moreover, three-dimensional structure information relating to AGB can be acquired by airborne laser scanning. Many studies estimated AGB from variables that were extracted from point cloud data, but few of them took full advantage of variables related to tree crowns to estimate the AGB. In this study, the main objective was to evaluate and compare the capabilities of different metrics derived from point clouds obtained from ALS. Particularly, individual tree-based alpha-shape, along with other traditional and commonly used plot-level height and intensity metrics, have been used from airborne laser scanning data. We took the random forest and multiple stepwise linear regression to estimate the AGB. By comparing AGB estimates with field measurements, our results showed that the best approach is mixed metrics, and the best estimation model is random forest (R2 = 0.713, RMSE = 21.064 t/ha, MAE = 15.445 t/ha), which indicates that alpha-shape may be a good alternative method to improve AGB estimation accuracy. This method provides an effective solution for estimating aboveground biomass from airborne laser scanning.

/pdf/crown-structure-metrics-to-generalize-aboveground-biomass-3f0purrp.pdf

Crown Structure Metrics to Generalize Aboveground Biomass Estimation Model Using Airborne Laser Scanning Data in National Park of Hainan Tropical Rainforest, China

In this paper, the magnetic field of internal linear motor in the micro compressor is simulated. Through finite element analysis and calculation of the magnetic flux density within the internal air gap of the micro linear motor under the conditions of different air gap width, air gap thickness, magnetic steel thickness and overall radial size of the motor, it is found that the initial setting size is the best size, which can not only meet the premise that the magnetic induction strength is unchanged, but also ensure uniform magnetic field distribution, The size and quality of the compressor are minimized. On the other hand, this study also verifies the rationality of the scaling principle for miniaturization.

Simulation analysis on magnetic field of internal linear motor in the micro compressor

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Chenyun Li

Author Tools

Chat about Author

Papers

Incipient detection of bearing fault using impulse feature enhanced weighted sparse representation

Crown Structure Metrics to Generalize Aboveground Biomass Estimation Model Using Airborne Laser Scanning Data in National Park of Hainan Tropical Rainforest, China

Comparison of Canopy Cover and Leaf Area Index Estimation from Airborne LiDAR and Digital Aerial Photogrammetry in Tropical Forests

Simulation analysis on magnetic field of internal linear motor in the micro compressor