Helicopter Health and Usage Monitoring System in China

Wang, Shibin; Tong, Chaowei; Tao, Zhiyu; Yan, Ruqiang; Chen, Xuefeng

doi:10.1109/mim.2022.9756384

Cited by 4 publications

(2 citation statements)

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“…From figure 4, when measurement coefficient was ∈ [3,5], the diagnostic accuracy of the model was always greater than 99%. As the measurement coefficient continued to decrease, diagnostic accuracy showed a downward trend.…”

Section: Configuration Of Measurement Coefficient Of Loss Functionmentioning

confidence: 95%

“…In fact, because of the complexity of industrial operation processes combined with the interference of working conditions, the vibration signals produced by machinery are usually nonstationary, nonlinear, and mingled with intense background noise [3]. In addition, the complex structures of advanced devices bring about the common issue of multiple mixed faults, which increases the difficulty in diagnostic tasks [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A feature reconstruction and SAE model based diagnosis method for multiple mixed faults

Yang,

Chen,

Zhan

et al. 2024

Meas. Sci. Technol.

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Owing to the merits of automatic feature extraction and depth structure, intelligent fault diagnosis based on deep neural networks has become a great concern. However, the non-fault state monitoring data volume of actual industrial machinery is rich, whereas the fault state data volume is insufficient and weak. Furthermore, achieving multiple mixed-fault diagnoses using skewed data distributions is extremely difficult. A feature reconstruction and sparse auto-encoder model-based diagnosis method for multiple mixed faults is proposed in this study to bridge these gaps. Such a feature reconstruction algorithm is designed and employed to address the following issues: 1) expensive computing resulting from the long sequential features of vibration monitoring data and 2) the extraction problem caused by the submersion of scarce data features. Furthermore, an adaptive loss function was formulated, and a deep auto-encoder network was constructed to identify the health status and determine the fault level. Diagnoses of artificial and real faults verify the availability and superiority of the proposed scheme, demonstrating the adaptability and robustness of these hyperparameters.

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Section: Configuration Of Measurement Coefficient Of Loss Functionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%