Actively Imaginative Data Augmentation for Machinery Diagnosis Under Large-Speed-Fluctuation Conditions

An, Zenghui; Jiang, Xingxing; Yang, Rui; Zhang, Hanyuan; Liu, Jie; Shen, Changqing

doi:10.1109/tii.2022.3218737

Cited by 14 publications

(4 citation statements)

References 25 publications

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“…An advanced fault diagnosis method, AIDA, has been added here. AIDA [33] is a data augmentation method which can increase the ability to the extension of CNN. From Table 2, it can be seen that the proposed method had the highest accuracy To further demonstrate the superiority of the proposed method, Table 2 lists the highest accuracy in diagnosing faults under time-varying conditions via different methods in the experiments above.…”

Section: Comparison With Related Methodsmentioning

confidence: 99%

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A Bearing Fault Diagnosis Method Based on a Residual Network and a Gated Recurrent Unit under Time-Varying Working Conditions

Zheng

Zhang

et al. 2023

Sensors

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The diagnosis of bearing faults is an important guarantee for the healthy operation of mechanical equipment. Due to the time-varying working conditions of mechanical equipment, it is necessary to achieve bearing fault diagnosis under time-varying working conditions. However, the superposition of the two-dimensional working conditions of speed and acceleration brings great difficulties to diagnosis via data-driven models. The long short-term memory (LSTM) model based on the infinitesimal method is an effective method to solve this problem, but its performance still has certain limitations. On this basis, this article proposes a model for fault diagnosis under time-varying operating conditions that combines a residual network model (ResNet) and a gate recurrent unit (model) (GRU). Firstly, the samples were segmented, and feature extraction was performed using ResNet. We then used GRU to process the information. Finally, the classification results were output through the output network. This model could ignore the influence of acceleration and achieve high fault diagnosis accuracy under time-varying working conditions. In addition, we used t-SNE to reduce the dimensionality of the features and analyzed the role of each layer in the model. Experiments showed that this method had a better performance compared with existing bearing fault diagnosis methods.

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Section: Comparison With Related Methodsmentioning

confidence: 99%

Section: Case Study On the Diagnosis Of Rolling Bearing Faults Under ...mentioning

confidence: 99%

A Bearing Fault Diagnosis Method Based on a Residual Network and a Gated Recurrent Unit under Time-Varying Working Conditions

Zheng

Zhang

et al. 2023

Sensors

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“…However, the current researches on bearing fault diagnosis under variable speeds still have some limitations. On the one hand, due to the bearing operating complex environment, fault characteristic frequencies and vibration amplitudes are hidden in the signal with serious noise, resulting in continuous and irregular fluctuation [29]. Therefore, it is extremely challenging to directly extract domain-invariant and fault-related characteristics from the vibration signal using deep learning methods under variable speeds.…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing fault diagnosis under time-varying speeds based on time-characteristic order spectrum and multi-scale domain adaptation network

Xu,

Tang,

Pang

et al. 2023

Meas. Sci. Technol.

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In practical industrial applications, rolling bearing generally operates under variable conditions and its vibration signal significantly fluctuates in amplitude and frequency. This increases the feature distribution differences of the bearing fault samples and makes the health status identification of the bearing more difficult. To this end, a new intelligent fault diagnosis method for bearing under time-varying speed conditions is proposed based on time-characteristic order (TCO) spectrum and multi-scale domain adaptation network (MSDAN). Firstly, by using the good noise robustness and high time-frequency aggregation properties of the synchrosqueezed wave packet transform (SSWPT), the TCO spectrum method based on SSWPT is proposed to eliminate the impact of speed fluctuation, reducing the distribution shift of bearing data under time-varying speeds. Secondly, an MSDAN model based on global-local feature fusion is established to extract the domain-invariant features closely related to the bearing fault state from the TCO spectrum. Finally, the local maximum distribution discrepancy (LMMD) is introduced to capture the discriminative fine-grained features. The feasibility of the proposed method is verified in various transfer tasks on two different bearing datasets with time-varying speeds. Compared with some state-of-art methods, the proposed method can eliminate the sample distribution differences under time-varying speeds, significantly improving its accuracy and generalization performance in cross-domain fault diagnosis of rolling bearing.

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“…However, the precision of the outcomes is contingent upon the selection of appropriate wavelet basis functions and scale parameters [9]. Extracting harmonic characteristics from signals has become an important aspect of mechanical fault diagnosis, and robust signal processing techniques such as empirical mode decomposition (EMD) and variational mode decomposition (VMD) are widely used as alternatives for this purpose [10,11]. VMD outperforms EMD in signal processing, specifically in the areas of composite fault separation in bearings and robustness.…”

Section: Introductionmentioning

confidence: 99%

Fast nonlinear cross-sparse filtering for rolling bearings compound fault diagnosis

Yao,

Zhang,

Han

et al. 2023

Meas. Sci. Technol.

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The investigation of faults in rotating machinery has been thoroughly examined. Among the different methods under exploration, sparse optimization-based techniques have arisen as a highly desirable approach. However, in real industrial environments, the collected bearing signals often contain a random impact component resulting from changes in working conditions and load mutations. When a machine malfunctions, it can readily induce and generate new faults, resulting in composite faults. To address this challenge, this paper proposes a novel multidimensional blind deconvolution method named fast nonlinear cross-sparse filtering (FNCr-SF). The FNCr-SF aims to separate weak compound faults under random impact interference. Various preprocessing techniques, including Z-score normalization and nonlinear sigmoid activation function, are employed to amplify the faint characteristics of compound faults and minimize the influence of random interference. Furthermore, the FNCr-SF method enables adaptive decomposition of fault components without the need for prior knowledge or pre-processing. This approach effectively reduces random interference and accurately detects compound faults in bearings. Experimental and simulation signals validate the effectiveness of the FNCr-SF method in compound fault detection, demonstrating its high accuracy and robustness.

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Actively Imaginative Data Augmentation for Machinery Diagnosis Under Large-Speed-Fluctuation Conditions

Cited by 14 publications

References 25 publications

A Bearing Fault Diagnosis Method Based on a Residual Network and a Gated Recurrent Unit under Time-Varying Working Conditions

A Bearing Fault Diagnosis Method Based on a Residual Network and a Gated Recurrent Unit under Time-Varying Working Conditions

Rolling bearing fault diagnosis under time-varying speeds based on time-characteristic order spectrum and multi-scale domain adaptation network

Fast nonlinear cross-sparse filtering for rolling bearings compound fault diagnosis

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