Fault diagnosis of anti-friction bearings based on Bi-dimensional ensemble local mean decomposition and optimized dynamic least square support vector machine

Xiong, Zhengqiang; Han, Chang; Zhang, Guorong

doi:10.1038/s41598-023-44996-6

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Zhengqiang Xiong et al proposed a fault diagnosis method for friction-reducing bearings based on two-dimensional ensemble local mean decomposition and optimized dynamic least squares support vector machine (LSSVM). 8 . Shan Guan et al proposed a transformer fault diagnosis method based on TLR-ADASYN balance data set.…”

Section: Introductionmentioning

confidence: 99%

A fault diagnosis method for railway turnout systems based on Convolution Transformer through data augmentation

Fu,

Yang,

et al. 2024

Preprint

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In this work, we tackle the challenge of improving fault diagnosis in railway turnout systems (RTS), a critical component of railway infrastructure. Addressing limitations in existing fault-diagnosis techniques, especially their efficacy with small training datasets, we focus on meticulous identification of RTS failure modes. The limited availability of fault samples and the issue of dataset imbalance further compound this challenge. To this end, we propose an innovative fault-diagnosis approach leveraging the Convolution Transformer (ConvTran) combined with advanced data augmentation techniques. This method is uniquely designed to ensure precise fault detection in situations characterized by restricted and skewed data. Existing augmentation techniques often inadequately capitalize on the specific features of time series data. To counteract this, our study proposes an innovative pseudo-synthetic data generation algorithm, utilizing feature combination to effectively overcome the challenges of sample imbalance and small sample sizes. The effectiveness of our model is rigorously tested and validated through comprehensive experiments using an actual dataset from a railway station monitoring system in China. The experimental outcomes decisively demonstrate that our proposed data augmentation method significantly improves the precision of turnout fault diagnosis, especially in contexts with limited sample sizes and imbalanced data. Impressively, our approach achieves a 92% accuracy rate in fault classification on small datasets, which further escalates to 99.7% following data augmentation. This exceptional performance not only outstrips that of other benchmark models but also underscores the superior efficiency and practical applicability of our proposed methodology in the field of RTS fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

A fault diagnosis method for railway turnout systems based on Convolution Transformer through data augmentation

Fu,

Yang,

et al. 2024

Preprint

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“…The next (third) group of systems used to diagnose hydraulic systems are systems with elements of the so-called intelligent damage identification based on the use of machine learning and deep learning algorithms 11 – 13 . In the literature, there are many papers describing the use of such solutions in the diagnostics of hydraulic systems 14 – 16 .…”

Section: Introductionmentioning

confidence: 99%

Application of analysis of variance to determine important features of signals for diagnostic classifiers of displacement pumps

Konieczny,

Łatas,

Stojek

2024

Sci Rep

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This paper presents the use of one-way analysis of variance ANOVA as an effective tool for ranking the features calculated from diagnostic signals and evaluates their impact on the accuracy of the machine learning system's classification of displacement pump wear.The first part includes a review of contemporary diagnostic systems and a description of typical damage of multi-piston displacement pumps and Its causes. The work also contains description of a diagnostic experiment which was conducted in order to obtain the matrix of vibration signals and the matrix of pressures measured at selected locations on the pump housing and at the pump pressure line. The measured signals were subjected to time–frequency analysis. The features of signals calculated in the time and frequency domains were ranked using the ANOVA. The next step involved the use the available classifiers in pump wear evaluation, conducting tests and assessing their effectiveness in terms of the ranking of features and the origin of diagnostic signals.

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“…However, EMD methods come with drawbacks like endpoint effects, underenvelopment, and modal aliasing. To address these issues, scholars have introduced enhanced versions of EMD methods, such as ensemble local mean decomposition (ELMD) [13,14], ensemble empirical modal decomposition (EEMD) [15,16], et al These improved methods have demonstrated success in enhancing the stability of EMD and overcoming challenges like mode aliasing. Nevertheless, the above methods also suffer from the disadvantage that the modal components of the decomposition lack physical significance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive mode decomposition method based on fault feature orientation and its application to compound fault diagnosis of planetary gearboxes

Li,

Cao,

Zhang

et al. 2024

Meas. Sci. Technol.

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Planetary gearboxes often experience multiple component failures during service, which can accelerate the degradation and failure of industrial equipment. Accurate separation and identification of multiple faults is an important means of ensuring the safe and stable operation of equipment. However, different faults can interact with each other, along with the influence of background noise, making it challenging to accurately extract faults with relatively weak energy among multiple faults. This difficulty leads to the problems of potential misdiagnosis and underdiagnosis. To address this issue, an adaptive mode decomposition method based on fault feature orientation (AMD-FF) is proposed in this paper. Initially, a fault impact indicator (FII) is constructed based on period-weighted kurtosis of envelope spectral and correlated combination negentropy to effectively characterize the impulsiveness and periodicity of fault features. Furthermore, with the objective of maximizing the FII, an adaptive decomposition of the original signal is designed based on blind convolution theory using a finite-impulse response filter group. Subsequently, a variable weight particle swarm optimization is employed to adaptively optimize the key decomposition parameters. Finally, the data of industrial-grade planetary gear transmission test rig are collected to validate the proposed method for compound fault diagnosis of planetary gearboxes. The results indicate that the AFMD-FF can effectively separate and extract compound faults in planetary gearboxes, demonstrating superior fault separation and diagnostic performance compared to the fault mode decomposition (FMD) and adaptive FMD. This method offers a novel approach to diagnosing compound faults in rotating equipment in industrial scenarios.

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Fault diagnosis of anti-friction bearings based on Bi-dimensional ensemble local mean decomposition and optimized dynamic least square support vector machine

Cited by 4 publications

References 20 publications

A fault diagnosis method for railway turnout systems based on Convolution Transformer through data augmentation

A fault diagnosis method for railway turnout systems based on Convolution Transformer through data augmentation

Application of analysis of variance to determine important features of signals for diagnostic classifiers of displacement pumps

Adaptive mode decomposition method based on fault feature orientation and its application to compound fault diagnosis of planetary gearboxes

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