Intelligent fault diagnosis of rolling bearings based on LSTM with large margin nearest neighbor algorithm

Aljemely, Anas H.; Xuan, Jianhua; Al-Azzawi, Osama; Jawad, Farqad K. J.

doi:10.1007/s00521-022-07353-8

Cited by 16 publications

(10 citation statements)

References 30 publications

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“…For example, Rathore and Harsha presented a rolling bearing residual life prediction approach utilizing bidirectional LSTM and an attention mechanism [20]. Aljemely et al designed a method that combines a longduration memory network with a long-interval nearest neighbor network (LSTM-LMNN) to retain key fault information during the parameter updating process through powerful orthogonal weight initialization technology [21]. Recognizing the complementary advantages of CNN and LSTM, researchers have gradually realized their combined potential in various applications such as the nuclear power plant fault diagnosis model by Ren et al, achieving an impressive problem recognition rate of 99.6% [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Fault diagnosis of rotating machinery using novel self-attention mechanism TCN with soft thresholding method

Ding,

2024

Meas. Sci. Technol.

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Rotating machinery (e.g., rolling bearing and gearbox) are usually operated in high-risk and vulnerable environments such as time-varying loads and poor lubrication. Timely assessment of the operational status for rotating machinery is crucial to prevent damage caused by potential failure and shutdown, which significantly enhances the reliability of mechanical systems, prolongs the service life of critical components in rotating machinery, and minimizes unnecessary maintenance costs. To this regard, in this paper, a novel approach named self-attention mechanism combined time convolutional network with soft thresholding algorithm (SAM-TCN-ST) is proposed for fault intelligent recognition of rotating machinery. Specifically, the vibration signals are transformed into time-frequency graphs with distinct features utilizing the continuous wavelet transform (CWT), and then the proposed SAM-TCN-ST algorithm is employed for capturing essential data characteristics and classification performance. Eventually, the datasets from rolling bearings and gearbox are used for verifying the accuracy and effectiveness of the proposed method compared with state-of-the-art benchmark networks such as pure TCN, convolutional neural network (CNN) and long short-term memory (LSTM) models. Experimental results demonstrate that the recognition accuracy rate of the proposed SAM-TCN-ST is higher than that obtained from the benchmark methods. This research presents an intelligent and viable solution for achieving real-time monitoring of the status and detecting faults in rotating machinery, thereby expectedly enhancing the reliability of mechanical systems. Consequently, the proposed SAM-TCN-ST algorithm holds significant potential for application in prognostic and health management (PHM) practices related to rotating machinery.

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Section: Literature Reviewmentioning

confidence: 99%

Fault diagnosis of rotating machinery using novel self-attention mechanism TCN with soft thresholding method

Ding,

2024

Meas. Sci. Technol.

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“…In order to solve the problem of data imbalance, Li et al [216] proposed a scheme based on Wasserstein generative adversarial network, and combined with LSTM-full convolutional network to realize fault diagnosis of high-dimensional vibration signal. Aljemely et al [217] proposed a combination method of LSTM and large margin nearest neighbor, which effectively identified multiple fault of rotating machinery. For non-uniform vibration signal, An et al [218] adopted the method based on periodic sparse attention and LSTM to diagnose the faults of rolling bearing.…”

Section: Fault Diagnosis Based On Deep Learning Algorithmsmentioning

confidence: 99%

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

Li,

Luo,

Bai

2024

Meas. Sci. Technol.

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Rolling bearings are critical components that are prone to faults in the operation of rotating equipment. Therefore, it is of utmost importance to accurately diagnose the state of rolling bearings. This review comprehensively discusses classical algorithms for fault diagnosis of rolling bearings based on vibration signal, focusing on three key aspects: data preprocessing, fault feature extraction, and fault feature identification. The main principles, key features, application difficulties, and suitable occasions for various algorithms are thoroughly examined. Additionally, different fault diagnosis methods are reviewed and compared using the Case Western Reserve University (CWRU) bearing dataset. Based on the current research status in bearing fault diagnosis, future development directions are also anticipated. It is expected that this review will serve as a valuable reference for researchers aiming to enhance their understanding and improve the technology of rolling bearing fault diagnosis.

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“…The purpose of this classification method is to find an embedded space, and at the same time, the samples with the same label should be concentrated as much as possible, while the samples with different labels should be scattered as much as possible. Nguyen B [32], Aljemely A H [33] and other scholars introduced these works in their papers.Meanwhile, the margins of different classes were pulled away in the embedding space obtained by the LMNN [34]. The object function of the LMNN is expressed as follows:…”

Section: X X Xmentioning

confidence: 99%

A Novel Method of Efficient Max-min Metric for Classification

Liu

2023

J. Phys.: Conf. Ser.

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Distance metric learning is an important method to study distance metrics that reflect the interaction between features and labels. Because of the high computational complexity and the fact that existing studies on algorithms that measure the similarities with Euclidean distances cannot reflect the real correlations between pairs of samples, learning a suitable distance metric is quite demanding for many data mining tasks. This paper innovatively proposes an extended efficient max-min metric (EMM) that maximizes the total distance between different pairs and minimizes the total distance between similar pairs as much as possible. Simultaneously, the adoption of the local preserving projection framework changes the solution process of the algorithm and improves the speed of the algorithm without losing accuracy. Because traditional EMM only considers pairwise constraints and ignores sample distribution, this study extends EMM based on sample distribution and successfully solves the multi-manifold problem. In the process of data realization, compared with the vector representation method, the use of high-order tensors will make the image representation more accurate and natural. To maintain the structure of higher-order tensors, a tensor-efficient max-min metric (TEMM) is proposed. In order to prove the accuracy and superiority of the research method in this paper, a large number of experiments have been carried out on image processing. The experimental results show that the method proposed in this paper has a good effect.

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Intelligent fault diagnosis of rolling bearings based on LSTM with large margin nearest neighbor algorithm

Cited by 16 publications

References 30 publications

Fault diagnosis of rotating machinery using novel self-attention mechanism TCN with soft thresholding method

Fault diagnosis of rotating machinery using novel self-attention mechanism TCN with soft thresholding method

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

A Novel Method of Efficient Max-min Metric for Classification

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