A study on rolling bearing fault diagnosis using RIME-VMD

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 8, 2025

To address the challenges of feature extraction in Variational Mode Decomposition (VMD) for rolling bearing fault diagnosis, this paper proposes a method optimized by RIME algorithm, called RIME-VMD. First, under various conditions, algorithm is employed to determine optimal combination decomposition components and penalty factors VMD. Next, kurtosis values each decomposed Intrinsic Function (IMF) are calculated, component with most prominent features selected noise reduction through reconstruction. Finally, sample entropy reconstructed signal calculated as input into Support Vector Machine (SVM) rapid identification diagnosis types. Simulation results indicate that, compared Whale Optimization Algorithm VMD (WOA-VMD), (RIME-VMD) achieves shorter search times higher efficiency. It facilitates faster parameters enhancing robustness detection enabling rapid, efficient faults. The findings study offer guidance reference future research on diagnosis.

Language: Английский

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Area efficient low power VLSI of 2048-Point pipelined radix 16 MDC /FFT Processer for brain tumour detection using optimized deep dilated convolutional neural network

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 116691 - 116691

Published: Jan. 1, 2025

Language: Английский

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Precision assembly error analysis of parts based on multi-constraint surface matching

Frontiers in Mechanical Engineering, Journal Year: 2025, Volume and Issue: 10

Published: Jan. 20, 2025

Existing assembly analysis methods often fail to accurately capture the complexities involved in precision of real-world parts. This paper introduces an advanced error method based on multi-constraint surface matching, aimed at overcoming these limitations. The proposed approach incorporates interference-free constraints and force stability develop positioning model that better reflects realistic process. To solve model, Spatial Pyramid Matching with chaotic mapping is employed for population initialization, thereby enhancing diversity. A nonlinear control mechanism further introduced dynamically adjust inertia weight, a simulated annealing integrated into particle swarm optimization algorithm enhance efficiency matching ultimately achieves high-precision completes comprehensive analysis. effectiveness enhanced performance methodology are validated through vibratory bowl feeder, demonstrating its potential significantly improve accuracy manufacturing contexts.

Language: Английский

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An optimal filtering frequency band search method based on MZGWO in rolling bearings fault diagnosis

Mechanical Systems and Signal Processing, Journal Year: 2025, Volume and Issue: 232, P. 112773 - 112773

Published: April 25, 2025

Language: Английский

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Oppositional chaotic artificial hummingbird algorithm on engineering design optimization

Frontiers in Mechanical Engineering, Journal Year: 2025, Volume and Issue: 11

Published: April 28, 2025

This paper proposes an enhanced-search form of the newly designed artificial hummingbird algorithm (AHA), named oppositional chaotic algorithm. The proposed OCAHA methodology incorporates learning (OBL) in population-initialization and at ending event each iteration for a faster convergence, chaos-embedded sequences Gauss/mouse map to replace random three population-updating iterative stages AHA, viz. guided, territorial migration foraging employ more diverse population solutional accuracy. effectiveness method has been evaluated two phases. OCAHA, four state art algorithms, namely, PSO, DE, GWO WOA, their recently developed effective variants, SLPSO, MTDE, SOGWO EWOA, inspiring optimizer AHA have implemented on 29 unconstrained CEC 2017 benchmark functions first phase. In second phase, verified 10 challenging engineering cases, compared with concerned leading performances. Comprehensive analysis simulated outcomes using various statistical metrics convergence profiles demonstrates that, optimization ability is superior all comparing algorithms except MTDE. For provides better searching performance, solution precision, robustness rate than competing designs, and, average, it lowered computational cost by 57.5% 22.63% term function evaluations fitness objective 2.4% 0.23% comparison version CAHA, respectively.

Language: Английский

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Knowledge-informed multiplication convolution generalization network for interpretable equipment diagnosis under unknown speed domains

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113263 - 113263

Published: May 1, 2025

Language: Английский

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Fault diagnosis method of rolling bearing based on SSA-VMD and RCMDE

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 24, 2024

To address the limitations of weak information extraction rolling bearing fault features and poor generalization performance diagnostic methods, a novel method was proposed based on sparrow search algorithm (SSA)-Variational Mode Decomposition (VMD) refined composite multi-scale dispersion entropy (RCMDE). Firstly, SSA optimized key parameters VMD to decompose signal. The time-frequency domain comprehensive evaluation factor then employed select sensitive intrinsic mode function (IMF) components for reconstruction. Then, RCMDE extracted from reconstructed signals create state feature set, which input into K-means KNN (KKNN) classifier classification. verify effectiveness method, comparative decomposition methods were established: EMD-RCMDE, EEMD-RCMDE, CEEMDAN-RCMDE, RCMDE. Various also evaluated, including MDE, MFE, MPE, along with classifiers such as DT, RF, SVM. Experimental verification different types single compound faults demonstrated method's excellent identification capability. In order further assess ability robustness, noise artificially added element varying damage levels. results show that even under-noise interference, maintained high accuracy anti-noise good ability, provides certain reference solution problems.

Language: Английский

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Feature Transfer Learning for Fatigue Life Prediction of Additive Manufactured Metals With Small Samples

Fatigue & Fracture of Engineering Materials & Structures, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 4, 2024

ABSTRACT A feature transfer learning (FTL)‐based model is proposed to address small‐sample problems in fatigue life prediction of additively manufactured (AM) metals. Transfer component analysis (TCA) studied for data alignment before training. Correspondingly, two TCA improvement strategies are further considered aggregate training from distinct AM processing conditions. An experimental database consisting 103 built evaluation. The results demonstrate that the outperforms conventional machine models and other learning‐based terms accuracy demand, showing good applicability assessment.

Language: Английский

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Adaptive Tracking Method for Time-Varying Underwater Acoustic Channel Based on Dynamic Gaussian Window

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(12), P. 2185 - 2185

Published: Nov. 29, 2024

The traditional recursive least squares (RLS) algorithm is limited in highly dynamic and noisy underwater channels. To overcome this, we introduce the time-varying Gaussian sliding window-based RLS (VGSRLS) algorithm, designed for enhanced channel tracking. VGSRLS adaptively adjusts window length based on signal’s instantaneous frequency variation. A rotation matrix reorients toward highest signal-to-noise ratio (SNR) direction, increasing tracking accuracy. Further, adapts shape along SNR direction by combining anisotropic adjustments, effectively suppressing noise from other directions enhancing SNR. Simulation results confirm that achieves superior estimation accuracy, showing reduced mean squared deviation (MSD) under typical conditions environments compared to SRLS-DCD algorithm.

Language: Английский

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