Fault detection of axle bearings is crucial to promote the safe, efficient, and reliable running of high-speed trains. In recent decades, time-frequency analysis (TFA) techniques have been widely used in mechanical equipment fault diagnoses. Time-reassigned multisynchrosqueezing transform (TMSST), as a novel time-frequency representation (TFR) algorithm, is more suitable for dealing with strong frequency-varying signals. However, TMSST TFR results are subject to noise interference. It is difficult to extract the accurate time-frequency (TF) fault feature of the axle bearing under a complex working environment. In addition, determination of the TMSST algorithm parameters depends on the personnel's subjective experience. Therefore, the TMSST result has a great randomicity and has the disadvantage of having a poor reliability. To address the above issues, a hybrid SVD-based denoising and self-adaptive TMSST is proposed for axle bearing fault detection in this paper. The main improvements of the proposed algorithm include the following two aspects: (1) An SVD-based denoising method using the maximum SV mean to determine the reasonable SV order is adopted to eliminate noise interference and to reserve useful fault impulse information. (2) A new evaluation metric, named time-frequency spectrum permutation entropy (TFS-PEn), is put forward for the quantitative evaluation of the performance of TFR for the TMSST, and then a water cycle algorithm (WCA)-based optimized TMSST can adaptively determine the optimal algorithm parameters. In both the simulation and experimental tests, the superiority and effectiveness of the proposed method is compared with the TMSST, short-time Fourier transform (STFT), MSST, wavelet transform (WT), and Hilbert-Huang transform (HHT) methods. The results show that the proposed algorithm has a better performance for extracting the weak fault features of axle bearing under a strong background noise environment.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8469842 | PMC |
| http://dx.doi.org/10.3390/s21186025 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A simulation data-driven semi-supervised framework based on MK-KNN graph and ESSGAT for bearing fault diagnosis.
ISA Trans
December 2024
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China. Electronic address:
Current supervised intelligent fault diagnosis relies on abundant labeled data. However, collecting and labeling data are typically both expensive and time-consuming. Fault diagnosis with unlabeled data remains a significant challenge.
View Article and Find Full Text PDFResearch on a Fault Feature Extraction Method for an Electric Multiple Unit Axle-Box Bearing Based on a Resonance-Based Sparse Signal Decomposition and Variational Mode Decomposition Method Based on the Sparrow Search Algorithm.
Sensors (Basel)
July 2024
School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
In light of the issue that the vibration signal from an axle-box bearing collected during the operation of an electric multiple unit (EMU) is seriously polluted by background noise, which leads to difficulty in identifying fault characteristic frequency, this paper proposes a resonance-based sparse signal decomposition (RSSD) and variational mode decomposition (VMD) method based on sparrow search algorithm (SSA) optimization to extract the fault characteristic frequency of the bearing. Firstly, the RSSD method is utilized to decompose the signal based on the obtained optimal combination of quality factors, resulting in the optimal low-resonance component with periodic fault information. Then, the VMD method is performed on this low-resonance component.
View Article and Find Full Text PDFDetection method for contact stress distribution of tapered roller bearings.
Sci Rep
May 2024
Luoyang Institute of Science and Technology, Luoyang, 471023, China.
Article Synopsis
- * This study develops detection devices using digital speckle correlation method (DSCM) and machine vision to assess the contact stress distribution of these bearings during operation.
- * By analyzing contact stress data from both new and used bearings (under lubricated and non-lubricated conditions), the research provides valuable insights for evaluating bearing modifications and assisting future bearing designs.
Train Axlebox Bearing Fault Diagnosis Based on MSC-SGMD.
Sensors (Basel)
December 2023
School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Train axlebox bearings are subject to harsh service conditions, and the difficulty of diagnosing compound faults has brought greater challenges to the maintenance of high-quality train performance. In this paper, based on the traditional symplectic geometry mode decomposition (SGMD) algorithm, a maximum spectral coherence signal reconstruction algorithm is proposed to extract the intrinsic connection between the SGMD components with the help of the frequency domain coherence idea and reconstruct the key signal components so as to effectively improve the extraction of composite fault features of axlebox bearings under different speed conditions. Firstly, based on the traditional SGMD algorithm, the vibration signal of the axle box is decomposed to extract its symplectic geometry components (SGCs).
View Article and Find Full Text PDFSolution and Solvent-Free Stopper Exchange Reactions for the Preparation of Pillar[5]arene-containing [2] and [3]Rotaxanes.
Chemistry
February 2024
Laboratoire de Chimie des Matériaux Moléculaires, Université de Strasbourg et CNRS (UMR 7042, LIMA), Ecole Européenne de Chimie, Polymères et Matériaux, 25 rue Becquerel, 67087, Strasbourg Cedex 2, France.
Article Synopsis
- Diamine reagents were used to functionalize a [2]rotaxane building block featuring a pentafluorophenyl ester stopper, resulting in an intermediate with a terminal amine.
- Depending on reaction conditions, the intermediate can either dissociate and form a [2]rotaxane or stabilize into a [3]rotaxane when using mechanochemical, solvent-free methods.
- Variable temperature NMR and density functional theory calculations were employed to analyze the shuttling motions of the pillar[5]arene subunit within these rotaxanes.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!