New Approach for Failure Prognosis Using a Bond Graph, Gaussian Mixture Model and Similarity Techniques

Mebarki, Nassima; Benmoussa, Samir; Djeziri, Mohand; Mouss, Leïla Hayet

doi:10.3390/pr10030435

Cited by 6 publications

(4 citation statements)

References 38 publications

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“…The variational problem can be effectively solved by applying alternate direction method of multipliers (ADMM). Then, the modes u k,c (t) in the frequency domain are updated as Equation ( 3) and the estimated center frequency ω k of the mode can be obtained by Equation (4).…”

Section: Multivariate Variational Mode Decompositionmentioning

confidence: 99%

“…Traditional bearing fault diagnosis methods based on mathematical models and experience require specialized background knowledge and complex signal processing techniques [4]. With the development of artificial intelligence (AI) and big data technology, intelligent bearing fault diagnosis methods based on machine learning, such as artificial neural network (ANN) [5], K nearest neighbor (KNN) [6], and support vector machines (SVMs), have been universally applied.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Bearing Fault Diagnosis Based on Multivariate Symmetrized Dot Pattern and LEG Transformer

et al. 2022

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Deep learning based on vibration signal image representation has proven to be effective for the intelligent fault diagnosis of bearings. However, previous studies have focused primarily on dealing with single-channel vibration signal processing, which cannot guarantee the integrity of fault feature information. To obtain more abundant fault feature information, this paper proposes a multivariate vibration data image representation method, named the multivariate symmetrized dot pattern (M-SDP), by combining multivariate variational mode decomposition (MVMD) with symmetrized dot pattern (SDP). In M-SDP, the vibration signals of multiple sensors are simultaneously decomposed by MVMD to obtain the dominant subcomponents with physical meanings. Subsequently, the dominant subcomponents are mapped to different angles of the SDP image to generate the M-SDP image. Finally, the parameters of M-SDP are automatically determined based on the normalized cross-correlation coefficient (NCC) to maximize the difference between different bearing states. Moreover, to improve the diagnosis accuracy and model generalization performance, this paper introduces the local-to-global (LG) attention block and locally enhanced positional encoding (LePE) mechanism into a Swin Transformer to propose the LEG Transformer method. Then, a novel intelligent bearing fault diagnosis method based on M-SDP and the LEG Transformer is developed. The proposed method is validated with two experimental datasets and compared with some other methods. The experimental results indicate that the M-SDP method has improved diagnostic accuracy and stability compared with the original SDP, and the proposed LEG Transformer outperforms the typical Swin Transformer in recognition rate and convergence speed.

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Section: Multivariate Variational Mode Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Bearing Fault Diagnosis Based on Multivariate Symmetrized Dot Pattern and LEG Transformer

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Some researchers have developed lots of advanced prediction algorithms by using statistical methods [27] or artificial intelligence techniques [28] to establish mapping relationships between inputs and RUL. The former relies on statistical models to determine the RUL probabilistically, like Markov process models [29] and Gaussian mixture models [30]. The latter utilizes machine learning tools such as support vector machines [31] and neural networks (NNs) [32] to implement it.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction for space bearing with cage friction fault based on data pre-screening and gated recurrent unit

Wang,

Pan,

et al. 2023

Meas. Sci. Technol.

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The remaining useful life (RUL) of bearings in space inertia actuators is crucial for performance maintenance requirements. But it is quite difficult to accurately predict the RUL of space bearings due to the significant intermittency and nonstationary properties caused by cage friction faults commonly occurring during the operation of the actuator. This paper proposes a data-driven method for RUL prediction of space bearings by incorporating the gated recurrent unit network with a novel data pre-screening approach. In the proposed method, a stacked autoencoder and clustering approach are introduced into the data pre-processing method, and a health index called Overrun-Distance is constructed for lifetime assessment. To verify the proposed method, a series of vibration tests on flywheels equipped with space bearings are conducted and used for RUL evaluation. The results show that the proposed RUL prediction method is applicable to space bearings for RUL prediction with high accuracy and effectiveness.

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“…Lounici et al proposed a hybrid bond graph model based on interval uncertainty, which overcomes the limitations of existing methods and improves the robustness of fault diagnosis for a controlled two-tank hybrid system [7]. In recent years, some scholars have combined bond graph theory with Gaussian mixture models, similarity techniques, finite state machine, optimized extreme learning machine, and other intelligent algorithms to realize the function of state and failure prediction [8,9]. These applications of the bond graph are based on the establishment of the analytical model of the system.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Using Bond Graphs in an Expert System

Zhou

Jiang

et al. 2022

Energies

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A fault diagnosis method using bond graphs in an expert system is proposed for a reactor coolant system. Firstly, the time causality graph and the variable relationship graph are derived from the bond graph. Secondly, the fault signature matrix is obtained by combining the change relationship of fault parameters. Finally, the fault signature matrix is used as the rule of the inference engine design in the expert system for fault diagnosis. In this paper, the key equipment of the reactor coolant system is used to verify the fault diagnosis method of the bond graph expert system, and the path reasoning relationship between alarms is obtained, which can accurately obtain the deep knowledge required by the operators. A new idea for fault diagnosis in a nuclear power plant’s expert system is provided by this method.

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New Approach for Failure Prognosis Using a Bond Graph, Gaussian Mixture Model and Similarity Techniques

Cited by 6 publications

References 38 publications

Intelligent Bearing Fault Diagnosis Based on Multivariate Symmetrized Dot Pattern and LEG Transformer

Intelligent Bearing Fault Diagnosis Based on Multivariate Symmetrized Dot Pattern and LEG Transformer

Remaining useful life prediction for space bearing with cage friction fault based on data pre-screening and gated recurrent unit

Fault Diagnosis Using Bond Graphs in an Expert System

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