An Improved Re-Scaling Frequency Stochastic Resonance and its Application to Weak Fault Signal Detection

Weak fault detection is crucial to incipient mechanical fault diagnosis. In order to extract weak fault signals, a method named improved Re-scaling Frequency Stochastic Resonance (IRFSR) is proposed in this paper. The method consists of four steps: (i) Frequency Information Exchange (FIE); (ii) Amplitude Coefficient Adjustment; (iii) Re-scaling Frequency Stochastic Resonance (RFSR); and (iv) Frequency Information Recovery. By means of the exchange of frequency information, the high-frequency information of the target signal is accordingly transferred to the low frequency band which can be rescaled to satisfy the small-parameter limits of classical stochastic resonance. Then IRFSR is able to overcome the limitation of RFSR, which is that the sampling frequency of RFSR is at least 50 times greater than the frequency of the target signal. Numerical results are reported to evaluate the effectiveness of IRFSR to detect the target signal with higher frequency at a low sampling frequency as compared with RFSR. And the feasibility of the IRFSR in incipient fault detection is demonstrated using case history data obtained from a sliding bearing test rig.

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Time-delayed feedback stochastic resonance enhanced minimum entropy deconvolution for weak fault detection of rolling element bearings

Chinese Journal of Physics ◽

10.1016/j.cjph.2021.12.002 ◽

2021 ◽

Author(s):

Xialun Yun ◽

Xuesong Mei ◽

Gedong Jiang

Keyword(s):

Fault Detection ◽

Stochastic Resonance ◽

Delayed Feedback ◽

Rolling Element Bearings ◽

Minimum Entropy ◽

Rolling Element ◽

Weak Fault ◽

Time Delayed Feedback

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Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second-Order Stochastic Resonance Method with Barrier Height Optimization

Shock and Vibration ◽

10.1155/2021/5539912 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Huaitao Shi ◽

Yangyang Li ◽

Peng Zhou ◽

Shenghao Tong ◽

Liang Guo ◽

...

Keyword(s):

Feature Extraction ◽

Fault Detection ◽

Barrier Height ◽

Stochastic Resonance ◽

Working Conditions ◽

Potential Well ◽

Rolling Bearings ◽

Input Signals ◽

Weak Fault ◽

Fault Feature Extraction

The stochastic resonance (SR) method is widely applied to fault feature extraction of rotary machines, which is capable of improving the weak fault detection performance by energy transformation through the potential well function. The potential well functions are mostly set fixed to reduce computational complexity, and the SR methods with fixed potential well parameters have better performances in stable working conditions. When the fault frequency changes in variable working conditions, the signal processing effect becomes different with fixed parameters, leading to errors in fault detection. In this paper, an underdamped second-order adaptive general variable-scale stochastic resonance (USAGVSR) method with potential well parameters’ optimization is put forward. For input signals with different fault frequencies, the potential well parameters related to the barrier height are figured out and optimized through the ant colony algorithm. On this basis, further optimization is carried out on undamped factor and step size for better fault detection performance. Cases with diverse fault types and in different working conditions are studied, and the performance of the proposed method is validated through experiments. The results testify that this method has better performances of weak fault feature extraction and can accurately identify different fault types in the input signals. The method proves to be effective in the weak fault extraction and classification and has a good application prospect in rolling bearings’ fault feature recognition.

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Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance

Machines ◽

10.3390/machines9110275 ◽

2021 ◽

Vol 9 (11) ◽

pp. 275

Author(s):

Di Xu ◽

Jianghua Ge ◽

Yaping Wang ◽

Junpeng Shao

Keyword(s):

Stochastic Resonance ◽

High Frequency ◽

Low Frequency ◽

Rolling Bearing ◽

Frequency Noise ◽

Weak Signal ◽

Resonance System ◽

Weak Fault ◽

High Pass ◽

Fault Characteristic

In engineering practice, the bearing fault signal is composed of a series of complex multi-component signals containing multiple fault characteristics information. In the early stage of fault sprouting and evolution, the fault features are easily disturbed by noise and irrelevant signals, eliminating the fault signals in the strong background noise. To overcome the influence of noise on the signal, this study proposes multi-frequency weak signal decomposition and reconstruction of rolling bearing based on adaptive cascaded stochastic resonance. First, the original signal is passed through the Hilbert transform to obtain the envelope signal. The envelope signal is high-pass filtered to eliminate the interference of low-frequency components on the response of the stochastic resonance system. Secondly, cascaded stochastic resonance system parameters are adaptively optimized by the quantum particle swarm algorithm (QPSO). The high-pass filtered signal input to the adaptive cascaded stochastic resonance system (ACSRS) can further enhance the weak fault characteristics, allowing the gradual transfer of high-frequency noise energy to the low-frequency fault characteristic components. Finally, the signal is decomposed using the variational mode decomposition (VMD) method to jointly determine the location of the fault characteristic frequencies in the intrinsic mode functions (IMF) component by the energy loss coefficient and correlation coefficient to achieve the reconstruction of multi-frequency weak signals. Through simulation and experimental validation, the effectiveness and superiority of the method for multi-frequency weak signal detection in bearings are verified. The results show that the method not only achieves the adaptive optimization of the stochastic resonance system parameters gradually removing the high-frequency noise in the signal and improving the energy of the low-frequency signal but also reduces the number of decomposition layers of the VMD, enhances the fault characteristic information in the weak signal, and effectively identifies the early weak fault characteristics of rolling bearings.

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Stochastic resonance based on frequency information exchange

Acta Physica Sinica ◽

10.7498/aps.65.220501 ◽

2016 ◽

Vol 65 (22) ◽

pp. 220501

Author(s):

Liu Jin-Jun ◽

Leng Yong-Gang ◽

Lai Zhi-Hui ◽

Tan Dan

Keyword(s):

Stochastic Resonance ◽

Information Exchange ◽

Frequency Information

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Lipreading with Auditory Low-frequency Information Contextual Constraints

International Journal of Audiology ◽

10.3109/14992029609047994 ◽

1996 ◽

Vol 25 (2) ◽

pp. 127-132

Author(s):

Jerker Rönnberg ◽

Stefan Samuelsson ◽

Björn Lyxell ◽

Stig Arlinger

Keyword(s):

Low Frequency ◽

Frequency Information

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Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR

Applied Sciences ◽

10.3390/app11115028 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5028

Author(s):

Miaomiao Sun ◽

Zhenchun Li ◽

Yanli Liu ◽

Jiao Wang ◽

Yufei Su

Keyword(s):

Reflection Coefficient ◽

Compressed Sensing ◽

Objective Function ◽

Low Frequency ◽

Original Data ◽

Seismic Exploration ◽

High Signal ◽

Inversion Process ◽

Frequency Information ◽

Low Snr

Low-frequency information can reflect the basic trend of a formation, enhance the accuracy of velocity analysis and improve the imaging accuracy of deep structures in seismic exploration. However, the low-frequency information obtained by the conventional seismic acquisition method is seriously polluted by noise, which will be further lost in processing. Compressed sensing (CS) theory is used to exploit the sparsity of the reflection coefficient in the frequency domain to expand the low-frequency components reasonably, thus improving the data quality. However, the conventional CS method is greatly affected by noise, and the effective expansion of low-frequency information can only be realized in the case of a high signal-to-noise ratio (SNR). In this paper, well information is introduced into the objective function to constrain the inversion process of the estimated reflection coefficient, and then, the low-frequency component of the original data is expanded by extracting the low-frequency information of the reflection coefficient. It has been proved by model tests and actual data processing results that the objective function of estimating the reflection coefficient constrained by well logging data based on CS theory can improve the anti-noise interference ability of the inversion process and expand the low-frequency information well in the case of a low SNR.

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Low-Frequency Signal Sampling Method Implemented in a PLC Controller Dedicated to Applications in the Monitoring of Selected Electrical Devices

Electronics ◽

10.3390/electronics10040442 ◽

2021 ◽

Vol 10 (4) ◽

pp. 442

Author(s):

Marcin Jaraczewski ◽

Ryszard Mielnik ◽

Tomasz Gębarowski ◽

Maciej Sułowicz

Keyword(s):

Power System ◽

Reactive Power ◽

Low Frequency ◽

Sampling Frequency ◽

Electrical Signal ◽

Distortion Factor ◽

Electrical Devices ◽

Frequency Sampling ◽

Sampled Signal ◽

Band Limited

High requirements for power systems, and hence for electrical devices used in industrial processes, make it necessary to ensure adequate power quality. The main parameters of the power system include the rms-values of the current, voltage, and active and reactive power consumed by the loads. In previous articles, the authors investigated the use of low-frequency sampling to measure these parameters of the power system, showing that the method can be easily implemented in simple microcontrollers and PLCs. This article discusses the methods of measuring electrical quantities by devices with low computational efficiency and low sampling frequency up to 1 kHz. It is not obvious that the signal of 50–500 Hz can be processed using the sampling frequency of fs = 47.619 Hz because it defies the Nyquist–Shannon sampling theorem. This theorem states that a reconstruction of a sampled signal is only guaranteed possible for a bandlimit fmax < fs, where fmax is the maximum frequency of a sampled signal. Therefore, theoretically, neither 50 nor 500 Hz can be identified by such a low-frequency sampling. Although, it turns out that if we have a longer period of a stable multi-harmonic signal, which is band-limited (from the bottom and top), it allows us to map this band to the lower frequencies, thus it is possible to use the lower sampling ratio and still get enough precise information of its harmonics and rms value. The use of aliasing for measurement purposes is not often used because it is considered a harmful phenomenon. In our work, it has been used for measurement purposes with good results. The main advantage of this new method is that it achieves a balance between PLC processing power (which is moderate or low) and accuracy in calculating the most important electrical signal indicators such as power, RMS value and sinusoidal-signal distortion factor (e.g., THD). It can be achieved despite an aliasing effect that causes different frequencies to become indistinguishable. The result of the research is a proposal of error reduction in the low-frequency measurement method implemented on compact PLCs. Laboratory tests carried out on a Mitsubishi FX5 compact PLC controller confirmed the correctness of the proposed method of reducing the measurement error.

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