Vibration Analysis of a Roller Bearing Condition Used in a Tangential Threshing Drum of a Combine Harvester for the Smooth and Continuous Performance of Agricultural Crop Harvesting

Bhandari, Shankar; Jotautienė, Eglė

doi:10.3390/agriculture12111969

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…In equation (2), U ∈ R (n−1)×(n−1) and V ∈ R 2×2 are orthogonal matrices; Λ is a diagonal matrix; and Λ = (diag (σ 1 , σ 2 ) , 0) or its transpose with rank 2. (3) After multilayer decomposition of the signal, the signal features under complex interference can be extracted, and noise reduction is achieved by inverse reconstruction of the extracted signal.…”

Section: Singular Value Decomposition and Improvementmentioning

confidence: 99%

“…With the rapid development of industrial manufacturing and scientific and technological breakthroughs, machinery and equipment in modern manufacturing are more sophisticated and intelligent than ever. As an indispensable component of rotating machinery, rolling bearings are widely used in machine tools, agricultural machinery, automotive components, large motors, and other fields because of their high precision, low frictional resistance, standardized dimensions, etc [1][2][3][4]. Still, their failures can affect the operating status of * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

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Research on bearing fault diagnosis based on novel MRSVD-CWT and improved CNN-LSTM

Guo,

Zhou,

Dong

et al. 2024

Meas. Sci. Technol.

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As a critical component in mechanical equipment, rolling bearings play a vital role in industrial production. Effective bearing fault diagnosis provides a more reliable guarantee for the safe operation of the industrial output. Traditional data-driven bearing fault diagnosis methods often have problems such as insufficient fault feature extraction and poor model generalization capabilities, resulting in reduced diagnostic accuracy. To solve these problems and significantly improve the diagnosis accuracy, this paper proposes a novel fault diagnosis method based on multi-resolution singular value decomposition (MRSVD), continuous wavelet transform (CWT), improved convolutional neural network (CNN) enhanced by convolutional block attention module (CBAM), and long short-term memory (LSTM). Through MRSVD, the vibration signal is decomposed layer by layer into multiple denoised signals, thus signal noise can be eliminated to the greatest extent to gain the optimal denoised signals; then through CWT, the optimal denoised signals are converted into two-dimensional time-frequency images so that the local and global characteristic information can be fully captured. Finally, through improved CNN-LSTM, feature extraction is greatly enhanced, resulting in high accuracy of fault diagnosis. Lots of experiments are organized to test the performance, and the experimental results show that the proposed method on various datasets has better diagnosis accuracy and generalization ability under different working conditions than other methods.

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Section: Singular Value Decomposition and Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on bearing fault diagnosis based on novel MRSVD-CWT and improved CNN-LSTM

Guo,

Zhou,

Dong

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…In reference [26], a fault diagnosis method utilizing composite scale variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is presented, showcasing superiority in balancing datasets but lacking applicability to small sample data. Moving on to reference [27], DDS Adash software is employed for signal processing, utilizing the Demodulation Fast Fourier Transform (FFT) root mean square (RMS) method and the DDS Adash Fault Source Identification Tool (FASIT) technique. However, this method falls short in early fault diagnosis of bearings.…”

Section: Comparison With Other Studies On Similar Topicsmentioning

confidence: 99%

Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement

Xie,

Li,

Liu

et al. 2024

Agriculture

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In the context of addressing the challenge posed by limited fault samples in agricultural machinery rolling bearings, especially when early fault characteristics are subtle, this study introduces a novel approach. The proposed multi-domain fault diagnosis method, anchored in data augmentation, aims to discern early faults in agricultural machinery rolling bearings, particularly within an imbalanced sample framework. The methodology involves determining early fault signals throughout the life cycle, constructing early fault datasets with varying imbalance rates for different fault types, and subsequently employing the Synthetic Minority Oversampling Technique (SMOTE) to balance the fault data. The study then extracts relative wavelet packet energy and time-domain sensitive features (variance, peak to peak) from the original and generated fault data to form a multi-domain fault feature vector. This vector is utilized for fault state recognition using a Support Vector Machine (SVM). Evaluation metrics such as accuracy, recall, and F1 values assess the recognition effectiveness for each rolling bearing state, with the overall model recognition evaluated based on accuracy. The proposed method is rigorously analyzed and validated using the XJTU-SY rolling bearing accelerated life test dataset. Comparative analysis is conducted with non-data enhanced fault feature vectors, specifically the relative energy of the wavelet packet, both with and without time-domain features. Experimental results underscore the superior performance of multi-domain fault features in providing a comprehensive description of signal information, leading to enhanced classification performance. Furthermore, the study demonstrates improved classification accuracy and recall rates for the balanced dataset compared to the imbalanced dataset. This research significantly contributes to an effective identification method for the early fault diagnosis of small sample rolling bearings in agricultural machinery.

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“…Consequently, the application of Kalman filtering in UAV fault diagnosis has become a crucial aspect of assuring the sustained success and efficacy of UAV missions in a variety of industries [34]. In [35], In terms of multiresolution analysis, FFT (Fast Fourier Transform) [36,37] signal processing has proved to be a valuable technique for UAV fault diagnosis [38]. In this context, FFT is used to analyze vibration signals recorded during operation from various UAV components.…”

Section: Fault Diagnosis Strategies For Predictive Maintenance Ismentioning

confidence: 99%

Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis?

Al-Haddad,

Giernacki,

Shandookh

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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In the modern technological advancements, Unmanned Aerial Vehicles (UAVs) have emerged across diverse applications. As UAVs evolve, fault diagnosis witnessed great advancements, with signal processing methodologies taking center stage. This paper presents an assessment of vibration-based signal processing techniques, focusing on Kalman filtering (KF) and Discrete Wavelet Transform (DWT) multiresolution analysis. Experimental evaluation of healthy and faulty states in a quadcopter, using an accelerometer, are presented. The determination of the 1024 Hz sampling frequency is facilitated through finite element analysis of 20 mode shapes. KF exhibits commendable performance, successfully segregating faulty and healthy peaks within an acceptable range. While the six-level multi-decomposition unveils good explanations for fluctuations eluding KF. Ultimately, both KF and DWT showcase high-performance capabilities in fault diagnosis. However, DWT shows superior assessment precision, uncovering intricate details and facilitating a holistic understanding of fault-related characteristics.

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Vibration Analysis of a Roller Bearing Condition Used in a Tangential Threshing Drum of a Combine Harvester for the Smooth and Continuous Performance of Agricultural Crop Harvesting

Cited by 9 publications

References 20 publications

Research on bearing fault diagnosis based on novel MRSVD-CWT and improved CNN-LSTM

Research on bearing fault diagnosis based on novel MRSVD-CWT and improved CNN-LSTM

Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement

Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis?

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