Fault diagnosis of rotor based on Semi-supervised Multi-Graph Joint Embedding

Self Cite

The vibration signal contains a lot of state information, and its time domain, frequency domain, and time-frequency domain features are extracted to describe the rotor’s operational state in detail. However, multi-domain and multi-angle feature extraction can lead to information redundancy, causing the "dimensionality catastrophe" problem, which also makes fault classification more difficult. Dimensionality reduction (DR) is a technique that can help with this issue, but the majority of current DR algorithms are based on a simple intrinsic structure. To accurately characterize the intrinsic structure of high-dimensional fault dataset, this study proposed a novel DR algorithm that considers local information, global information, and the hypergraph model, named Local-Global Standard Hypergraph Embedding (LGSHE). LGSHE redefines the hypergraph weight matrix’s calculation formula and constructs the local intra-class standard hypergraph, local inter-class standard hypergraph, global intra-class standard hypergraph, and global inter-class standard hypergraph to characterize the fault data structure. LGSHE can accurately characterize the intrinsic structure of high-dimensional fault dataset and increase fault classification accuracy. The performance of LGSHE is validated on two different structures of double-span rotor experimental benches, and the results show that the algorithm can achieve good fault classification accuracy.

Section: Instruction Of Lgshementioning

confidence: 99%

Section: Parameters Determinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dimensionality reduction using local-global standard hypergraph embedding for rotor fault diagnosis

Yuan¹,

Zhao²,

Chen³

et al. 2022

Self Cite

“…* Authors to whom any correspondence should be addressed. the existence of noise information [8][9][10][11][12][13][14]. Therefore, some strategies are still needed to be explored to improve the accuracy and effectiveness of fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Match-reinforcement learning with time frequency selection for bearing fault diagnosis

Wang

Gao

Zhu

et al. 2023

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Rolling bearings play a vital role in ensuring the safe operation of rotating machinery. However, in many application scenarios, the collected data has a low signal-to-noise ratio and the samples with faults are rare, which affects the generalization capability of the model, making it impossible to achieve accurate diagnosis. To solve this problem, the selection of time-frequency (TF) maps was considered in this paper through reinforcement learning. The time-frequency maps are built by four classical time-frequency characterization methods such as short-time Fourier transform (STFT) and synchro squeezing transform (SST). And the match-reinforcement learning time frequency selection (MRLTFS) fault diagnosis model is proposed to extract the fault-related features. Experiments show that the proposed MRLTFS method is superior to existing methods in robustness, generalization and feature selection capability.

“…The complete acquisition of label information for samples is quite difficult, while semi-supervised methods can perform fault diagnosis without relying heavily on a large amount of label information. To reduce dependence on a large number of labels, Yuan et al [32] proposed a semi-supervised multi-graph joint embedding fault diagnosis method that uses semi-supervised hypergraphs and ordinary graphs to portray the complex structural relationships between data.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rotating machinery via multi-structure fusion discriminative projection

Zhang

Zhu

et al. 2023

Rotating machinery is one of the important parts of industrial production equipment, and it is of great practical significance for fault diagnosis. Aiming at the problem of classification difficulty caused by feature interleaving in complex working conditions and high-dimension of rotating machinery fault samples, a rotating machinery fault diagnosis method based on Multi-structure Fusion Discriminative Projection (MFDP) is proposed. MFDP constructed intraclass and interclass hypergraph structures with multivariate relationships, fully revealing the higher-order association information among multiple samples. Besides, a tangential graph structure of MFDP is further constructed by combining the tangential affine of local samples to preserve the local tangential information of the manifold space. In the method, a unified objective optimization model of the discriminative hypergraph structures and local tangential graph structures is developed, and by solving the model, we can obtain fault structure features with well intraclass compactness and interclass separability. Extensive experiments on the Case Western Reserve University bearing dataset and Connecticut gear dataset show that the method has a good diagnostic accuracy of rotating machinery in different working conditions.