Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

Gao, Tong; Wei, Sheng; Zhou, Mingliang; Fang, Bin; Luo, Futing; Li, Jiajun

doi:10.3390/s20195633

Cited by 15 publications

(7 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MEMS pressure sensors have prominent advantages in weight and power consumption due to their characteristics [ 4 ]. Basov [ 5 ] proposed a mathematical model of a high-sensitivity pressure sensor with a novel electrical circuit utilizing a piezosensitive transistor differential amplifier with a negative feedback loop.…”

Section: Introductionmentioning

confidence: 99%

A Novel Defect Inspection System Using Convolutional Neural Network for MEMS Pressure Sensors

Deng

Zhang

et al. 2022

J. Imaging

View full text Add to dashboard Cite

Defect inspection using imaging-processing techniques, which detects and classifies manufacturing defects, plays a significant role in the quality control of microelectromechanical systems (MEMS) sensors in the semiconductor industry. However, high-precision classification and location are still challenging because the defect images that can be obtained are small and the scale of the different defects on the picture of the defect is different. Therefore, a simple, flexible, and efficient convolutional neural network (CNN) called accurate-detection CNN (ADCNN) to inspect MEMS pressure-sensor-chip packaging is proposed in this paper. The ADCNN is based on the faster region-based CNN, which improved the performance of the network by adding random-data augmentation and defect classifiers. Specifically, the ADCNN achieved a mean average precision of 92.39% and the defect classifier achieved a mean accuracy of 97.2%.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Defect Inspection System Using Convolutional Neural Network for MEMS Pressure Sensors

Deng

Zhang

et al. 2022

J. Imaging

View full text Add to dashboard Cite

show abstract

“…Generally, fault diagnosis techniques can be categorized into two types, signal analysis, and data-driven methods. For signal analysis methods, vibration signals are first dealt with signal processing methods such as time-domain analysis [7], frequency domain analysis [8] and time-frequency domain analysis [9,10]. Then, based on the expert knowledge, features extracted from different domains are used to detect bearings health changes and assess health states.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN

Tong

et al. 2021

Sensors

View full text Add to dashboard Cite

Bearings are the key and important components of rotating machinery. Effective bearing fault diagnosis can ensure operation safety and reduce maintenance costs. This paper aims to develop a novel bearing fault diagnosis method via an improved multi-scale convolutional neural network (IMSCNN). In traditional convolutional neural network (CNN), a fixed convolutional kernel is often employed in the convolutional layer. Thus, informative features can not be fully extracted for fault diagnosis. In the proposed IMSCNN, a 1D dimensional convolutional layer is used to mitigate the effect of noise contained in vibration signals. Then, four dilated convolutional kernels with different dilation rates are integrated to extract multi-scale features through the inception structure. Experimental results from the popular CWRU and PU datasets show the superiority of the proposed method by comparison with other related methods.

show abstract

“…Many scholars have proposed a lot of data-driven fault prediction methods [20][21][22][23][24]. Gao et al [25] proposed a new method of fault diagnosis using a bidirectional long short-term memory-(BLSTM-) based Hilbert-Huang transform (HHT) and CNN. Liang et al [26] proposed a fault diagnosis method based on Wavelet Transform (WT), Generative Adversarial Network (GAN), and CNN.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short‐Term Memory

You

Chen

Liu

et al. 2021

Shock and Vibration

View full text Add to dashboard Cite

The traditional bearing fault diagnosis methods have complex operation processes and poor generalization ability, while the diagnosis accuracy of the existing intelligent diagnosis methods needs to be further improved. Therefore, a novel fault diagnosis approach named CNN-BLSTM for bearing is presented based on convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) in this paper. This method directly takes the collected one-dimensional raw vibration signal as input and adaptively extracts the feature information through CNN. Then, the BLSTM is used to fuse the extracted features to acquire the failure information sufficiently and prevent the model from overfitting. Finally, two different experimental datasets are used to verify the effectiveness of the method. The experimental results show that the proposed CNN-BLSTM model can accurately diagnose the fault category of bearings. It has the advantages of rapidity, stability, antinoise, and strong generalization.

show abstract

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert–Huang Transform and Deep Learning

Cited by 15 publications

References 68 publications

A Novel Defect Inspection System Using Convolutional Neural Network for MEMS Pressure Sensors

A Novel Defect Inspection System Using Convolutional Neural Network for MEMS Pressure Sensors

Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN

Intelligent Fault Diagnosis of Bearing Based on Convolutional Neural Network and Bidirectional Long Short‐Term Memory

Contact Info

Product

Resources

About