A deep sequence multi-distribution adversarial model for bearing abnormal condition detection

Ou, Xuelian; Wen, Guangrui; Huang, Xin; Su, Yu; Chen, Xuefeng; Lin, Hailong

doi:10.1016/j.measurement.2021.109529

Cited by 18 publications

(3 citation statements)

References 29 publications

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“…These statistical methods have the advantages of simplicity, intuitiveness, and ease of implementation [7]. However, their accuracy and effectiveness are limited by complex mechanical structures and environmental conditions [8].…”

Section: Introductionmentioning

confidence: 99%

Blade fouling fault detection based on shaft orbit generative adversarial network

Huang,

Ma,

Shao

et al. 2024

Meas. Sci. Technol.

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The precision and stability of anomaly detection methods are vital for the secure and efficient operation of machinery. In this paper, finite element model is firstly used to analyze the shaft orbit of cantilever rotor from the perspective of fault mechanism. Then shaft orbit generative adversarial network is proposed and applied to detect the blade fouling fault. Variational autoencoder is used as the foundational network architecture for extracting high-dimensional latent features from shaft orbit images. Concurrently, the seventh-order moment of shaft orbit images is extracted and embedded into a bypass within the generator, thereby enhancing the accuracy of fault detection. Two sets of real-world blade fouling fault data are collected and meticulously analyzed. The results demonstrate that the proposed method exhibits higher accuracy and more robust generalization capability in anomaly detection. Additionally, the utilization of gradient information for the localization and visual analysis of anomalies dynamically tracks the spatial evolution of the rotor shaft orbit throughout its entire lifecycle. The data generation capability and interpretability of the proposed model can effectively support the digital twin modeling and health management of rotating machinery.

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Section: Introductionmentioning

confidence: 99%

Blade fouling fault detection based on shaft orbit generative adversarial network

Huang,

Ma,

Shao

et al. 2024

Meas. Sci. Technol.

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“…Vos et al [34] developed a gear anomaly detection algorithm combining deep learning and LSTM. Ou et al [35] provided a bearing state anomaly detection method based on the LSTM network. In the training process of this method, only health data was used.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Detection for Running State of Linear Motor Feeding System Based on Deep Neural Networks

et al. 2022

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Because the linear motor feeding system always runs in complex working conditions for a long time, its performance and state transition have great randomness. Therefore, abnormal detection is particularly significant for predictive maintenance to promptly discover the running state degradation trend. Aiming at the problem that the abnormal samples of linear motor feed system are few and the samples have time-series features, a method of abnormal operation state detection of a linear motor feed system based on normal sample training was proposed, named GANomaly-LSTM. The method constructs an encoding-decoding-reconstructed encoding network model. Firstly, the time-series features of vibration, current and composite data samples are extracted by the long short-term memory (LSTM) network; Secondly, the three-layer fully connected layer is employed to extract potential feature vectors; Finally, anomaly detection of the system is completed by comparing the potential feature vectors of the two encodings. An experimental platform of the X-Y two-axis linkage linear motor feeding system is built to verify the rationality of the proposed method. Compared with other classical methods such as GANomaly and GAN-AE, the average AUROC index of this method is improved by 17.5% and 9.3%, the average accuracy is enhanced by 11.6% and 15.5%, and the detection time is shortened by 223 ms and 284 ms, respectively. GANomaly-LSTM has successfully proved its superiority for abnormal detection for running state of linear motor feeding systems.

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“…The temperature change of a bearing directly affects its lifespan and stability; thus, bearing condition monitoring technology is of great significance to the fault prediction and normal operation of engines [ 1 , 2 , 3 , 4 , 5 , 6 ]. Bearings are an important rotating component of engines.…”

Section: Introductionmentioning

confidence: 99%

Wireless LC Conformal Temperature Sensor Based on Ag Film (9912-K FL) for Bearing Temperature Measurement

Feng

Hong

et al. 2022

Nanomaterials

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As the key component of aero-engines and industrial gas turbines, a bearing’s working temperature at high speed is close to 300 ℃. The measurement of an engine bearing’s temperature is of great significance to ensure flight safety. In this study, we present a wireless LC conformal temperature sensor for bearing temperatures, which integrates silver on the bearing surface in situ through a screen-printing process. This process makes Ag film (9912-K FL) firmly adhere to the bearing surface and realizes wireless measurements for bearing temperatures in situ. A high-temperature holding experiment of the prepared sensor was conducted, and the results showed that the sensor can work stably for 10 h at 300 ℃. We tested the designed wireless LC conformal temperature sensor at 20–270 ℃. The results showed that the proposed temperature sensor attained as good accuracy and stability in the temperature range 20–270 ℃. The sensitivity of the temperature measurements was 20.81 KHz/℃ when the bearing rotateds, the maximum repeatability was 0.039%, the maximum uncertainty was 0.081%, and the relative error was stable within 0.08%.

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A deep sequence multi-distribution adversarial model for bearing abnormal condition detection

Cited by 18 publications

References 29 publications

Blade fouling fault detection based on shaft orbit generative adversarial network

Blade fouling fault detection based on shaft orbit generative adversarial network

Abnormal Detection for Running State of Linear Motor Feeding System Based on Deep Neural Networks

Wireless LC Conformal Temperature Sensor Based on Ag Film (9912-K FL) for Bearing Temperature Measurement

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