Pitch angle sliding variance test method based on Mahony filter for zero-velocity detection

Yang, Yan‐Dong; Zhao, Deli; Liu, Wancheng; Cao, Huiliang; Wang, Chenguang; Tang, Jun; Shen, Chong; Li, Jun

doi:10.1016/j.measurement.2022.112145

Cited by 3 publications

(1 citation statement)

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“…In recent years, microelectromechanical systems (MEMS) sensors [ 1 ] have played a key role in many application areas, such as inertial navigation systems [ 2 , 3 ], automotive safety systems [ 4 ], aerospace systems [ 5 ], and smart electronics. Although MEMS sensors are designed and manufactured under strict quality control and testing [ 6 , 7 ], failures are still unavoidable due to uncertainties in the operating environment, device aging, and manufacturing defects, and incorrect sensor measurements can lead to serious consequences and even endanger personal safety. First, the complex and diverse operating principles and internal structures of MEMS sensors [ 8 ] make fault diagnosis complex and difficult.…”

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confidence: 99%

A Fault Diagnosis Method of Four-Mass Vibration MEMS Gyroscope Based on ResNeXt-50 with Attention Mechanism and Improved EWT Algorithm

Wang²,

et al. 2023

Micromachines

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In this paper, a fault identification algorithm combining a signal processing algorithm and machine learning algorithm is proposed, using a four-mass vibration MEMS gyroscope (FMVMG) for signal acquisition work, constructing a gyroscope fault dataset, and performing the model training task based on this dataset. Combining the improved EWT algorithm with SEResNeXt-50 reduces the impact of white noise in the signal on the identification task and significantly improves the accuracy of fault identification. The EWT algorithm is a wavelet analysis algorithm with adaptive wavelet analysis, which can significantly reduce the impact of boundary effects, and has a good effect on decomposition of signal segments with short length, but a reconstruction method is needed to effectively separate the noise signal and effective signal, and so this paper uses multiscale permutation entropy for calculation. For the reason that the neural network has a better ability to characterize high-dimensional signals, the one-dimensional signal is reconstructed into a two-dimensional image signal and the signal features are extracted. Then, the constructed image signals are fed into the SEResNeXt-50 network, and the characterization ability of the model is further improved in the network with the addition of the Squeeze-and-Excitation module. Finally, the proposed model is applied to the FMVMG fault dataset and compared with other models. In terms of recognition accuracy, the proposed method improves about 30.25% over the BP neural network and about 1.85% over ResNeXt-50, proving the effectiveness of the proposed method.

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