Intelligent bearing fault diagnosis based on Teager energy operator demodulation and multiscale compressed sensing deep autoencoder

Pei, Xinglong; Zheng, Xiaoyang; Wu, Jian‐Ying

doi:10.1016/j.measurement.2021.109452

Cited by 34 publications

(10 citation statements)

References 55 publications

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“…Therefore, it is necessary to test the performance of the proposed fault diagnosis method in high-noise environments. We add Gaussian noise into the raw signal to construct composite signals possessed of different signal-to-noise ratios (SNRs) [35] in this paper, as shown in figure 11. The SNR can be defined as follows: SNR = 10 × log 10…”

Section: Noise Additionmentioning

confidence: 99%

Bearing fault diagnosis method using the joint feature extraction of Transformer and ResNet

Hou

Lian

Chu

2023

Meas. Sci. Technol.

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The failure of rotating machinery can be prevented and eliminated by regular diagnosis of bearings. In the deep learning model of bearing fault diagnosis driven by big data, there often exist problems such as data acquisition difficulties, data distribution imbalance and high noise in samples. This study proposes a novel bearing fault diagnosis method using the joint feature extraction of Transformer and ResNet coupled with transfer learning strategy (TL-TAR) to overcome the abovementioned issues. First, the data is transmitted to the Transformer encoder and ResNet architecture respectively, where the input obtained by the encoder needs to separate features and word embedding through one-dimensional convolutional layer. Next, the feature sequences mined using encoder and ResNet are connected and classified. Moreover, the transfer learning strategy with model fine-tuning is exploited to reduce the train difficulty of the proposed method in new tasks. Experiments on two bearing fault datasets show that the proposed method can effectively combine the characteristics of both architectures, and the prediction accuracy outperform traditional deep learning networks in high noise environments.

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Section: Noise Additionmentioning

confidence: 99%

Bearing fault diagnosis method using the joint feature extraction of Transformer and ResNet

Hou

Lian

Chu

2023

Meas. Sci. Technol.

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“…The TEO is a difference operator that is composed of the instantaneous signal value and the non-linear combination of the differential [32]. The energy of the dynamic signal generated by the signal source can be estimated by TEO.…”

Section: Estimation Of Time-domain Energymentioning

confidence: 99%

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

Zhong

Wang

2023

Meas. Sci. Technol.

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To accurately and sensitively track the stator current of the induction motor (IM) and detect faults, stochastic resonance (SR) and Teager energy operator (TEO) are combined to detect the fault in the residual stator current of sliding mode observer (SMO) under strong noise interference and complex weak fault conditions. Firstly, a new approach law is constructed to establish an SMO for better state tracking. Secondly, SR is used to absorb noise and amplify the detection residual of the SMO, and the output results are estimated by TEO in the time domain to achieve fault detection. Finally, the detection results of IM stator and rotor winding faults and sensor intermittent faults are presented. The experimental results show that the SMO has higher state tracking accuracy and a faster rate of convergence. Moreover, the residual of stator current is processed by SR and TEO, and the effectiveness of fault detection is enhanced.

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“…The energy of the system contains the kinetic energy of the mass block and potential energy of the spring, it can be calculated by:

which means the instantaneous energy of the simple harmonic vibration system is proportional to the squared product of the amplitude and frequency [ 18 ].…”

Section: Background Theorymentioning

confidence: 99%

A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault Signals

Yan

Zhou

et al. 2022

Sensors

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In order to diagnose an incipient fault in rotating machinery under complicated conditions, a fast sparse decomposition based on the Teager energy operator (TEO) is proposed in this paper. In this proposed method, firstly, the TEO is employed to enhance the envelope of the impulses, which is more sensitive to frequency and can eliminate the low-frequency harmonic component and noise; secondly, a smoothing filtering algorithm was adopted to suppress the noise in the TEO envelope; thirdly, the fault signal was reconstructed by multiplication of the filtered TEO envelope and the original fault signal; finally, sparse decomposition was used based on a generalized S-transform (GST) to obtain the sparse representation of the signal. The proposed preprocessing method using the filtered TEO can overcome the interference of high-frequency noise while maintaining the structure of fault impulses, which helps the processed signal perform better on sparse decomposition; sparse decomposition based on GST was used to represent the fault signal more quickly and more accurately. Simulation and application prove that the proposed method has good accuracy and efficiency, especially in conditions of very low SNR, such as impulses with anSNR of −8.75 dB that are submerged by noise of the same amplitude.

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Intelligent bearing fault diagnosis based on Teager energy operator demodulation and multiscale compressed sensing deep autoencoder

Cited by 34 publications

References 55 publications

Bearing fault diagnosis method using the joint feature extraction of Transformer and ResNet

Bearing fault diagnosis method using the joint feature extraction of Transformer and ResNet

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

A Fast Sparse Decomposition Based on the Teager Energy Operator in Extraction of Weak Fault Signals

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