Detecting and Predicting Early Faults of Complex Rotating Machinery Based on Cyclostationary Time Series Model

2006 ◽  
Vol 128 (5) ◽  
pp. 666-671 ◽  
Author(s):  
Z. S. Chen ◽  
Y. M. Yang ◽  
Z. Hu ◽  
G. J. Shen
Keyword(s):  
Signal To Noise Ratio ◽  
Rotating Machinery ◽  
Ar Model ◽  
Model Parameters ◽  
Time Varying ◽  
Signal To Noise ◽  
Vibration Signals ◽  
Novel Method ◽  
Cyclostationary Signals ◽  
Gear Crack

Vibration signals of complex rotating machinery are often cyclostationary, so in this paper one novel method is proposed to detect and predict early faults based on the linear (almost) periodically time-varying autoregressive (LPTV-AR) model. At first the algorithms of identifying model parameters and order are presented using the higher-order cyclic-cumulant, which can suppress additive stationary noises and improve the signal to noise ratio (SNR). Then numerical simulations are done and the results indicate that this model is more effective for cyclostationary signals than the classical AR model. In the end the proposed method is used for detecting incipient gear crack fault in a helicopter gearbox. The results demonstrate that the approach can be used to detect and predict early faults of complex rotating machinery by the kurtosis of the residual signal.

Coding of time-varying electric field amplitude modulations in a wave-type electric fish

1996 ◽  
Vol 75 (6) ◽  
pp. 2280-2293 ◽  
Author(s):  
R. Wessel ◽  
C. Koch ◽  
F. Gabbiani
Keyword(s):  
Mutual Information ◽  
Electric Fish ◽  
Signal To Noise Ratio ◽  
Cutoff Frequency ◽  
Spike Trains ◽  
Time Varying ◽  
Signal To Noise ◽  
Single Spike ◽  
The Mean ◽  
P Type

1. The coding of time-varying electric fields in the weakly electric fish, Eigenmannia, was investigated in a quantitative manner. The activity of single P-type electroreceptor afferents was recorded while the amplitude of an externally applied sinusoidal electric field was stochastically modulated. The amplitude modulation waveform (i.e., the stimulus) was reconstructed from the spike trains by mean square estimation. 2. From the stimulus and the reconstructions we calculated the following: 1) the signal-to-noise ratio and thus an effective temporal bandwidth of the units; 2) the coding fraction, i.e., a measure of the fraction of the time-varying stimulus encoded in single spike trains; and 3) the mutual information provided by the reconstructions about the stimulus. 3. Signal-to-noise ratios as high as 7:1 were observed and the bandwidth ranged from 0 up to 200 Hz, consistent with the limit imposed by the sampling theorem. Reducing the cutoff frequency of the stimulus increased the signal-to-noise ratio at low frequencies, indicating a nonlinearity in the receptors' response. 4. The coding fraction and the rate of mutual information transmission increased in parallel with the standard deviation (i.e., the contrast) of the stimulus as well as the mean firing rate of the units. Significant encoding occurred 20-40 Hz above the spontaneous discharge of a unit. 5. When the temporal cutoff frequency of the stimulus was increased between 80 and 400 Hz, 1) the coding fraction decreased, 2) the rate of mutual information transmission remained constant over the same frequency range, and 3) the reconstructed filter changed. This is in agreement with predictions obtained in a simplified neuronal model. 6. Our results suggest that 1) the information transmitted by single spike trains of primary electrosensory afferents to higherorder neurons in the fish brain depends on the contrast and the cutoff frequency of the stimulus as well as on the mean firing rate of the units; and 2) under optimal conditions, more than half of the information about a Gaussian stimulus that can in principle be encoded is carried in single spike trains of P-type afferents at rates up to 200 bits per second.

Mechanical Fault Detection Based on the Wavelet De-Noising Technique

2004 ◽  
Vol 126 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Jing Lin ◽  
Ming J. Zuo ◽  
Ken R. Fyfe
Keyword(s):  
Prior Information ◽  
Early Stage ◽  
Signal To Noise Ratio ◽  
Mechanical Vibration ◽  
Likelihood Estimation ◽  
Morlet Wavelet ◽  
Signal To Noise ◽  
Vibration Signals ◽  
Orthogonal Wavelets ◽  
Basic Wavelet

For gears and roller bearings, periodic impulses indicate that there are faults in the components. However, it is difficult to detect the impulses at the early stage of fault because they are rather weak and often immersed in heavy noise. Existing wavelet threshold de-noising methods do not work well because they use orthogonal wavelets, which do not match the impulse very well and do not utilize prior information on the impulse. A new method for wavelet threshold de-noising is proposed in this paper; it not only employs the Morlet wavelet as the basic wavelet for matching the impulse, but also uses the maximum likelihood estimation for thresholding by utilizing prior information on the probability density of the impulse. This method has performed excellently when used to de-noise mechanical vibration signals with a low signal-to-noise ratio.

scholarly journals Multistage noise reduction processing for vibration signal of hydropower units

2021 ◽  
Vol 2108 (1) ◽  
pp. 012008
Author(s):  
Yousong Shi ◽  
Jianzhong Zhou
Keyword(s):  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Field Testing ◽  
Signal To Noise ◽  
Vibration Signals ◽  
Multi Stage ◽  
Actual Field ◽  
Stationary Vibration ◽  
Testing Environments

Abstract In actual field testing environments of hydropower units, unit vibration signals are often contaminated with noise. In order to obtain the real vibration signal, a multi-stage vibration signal denoise method SG-SVD-VMD is proposed for the guide bearing nonlinear and non-stationary vibration signals. And the root mean square error (RMSE) and signal to noise ratio (SNR) are used to evaluate the noise reduction ability of eight methods. The results show that the noise-canceling ability of this proposed method has improved to some extent. It can effectively suppress the noise of the hydropower units vibration signals. This method can effectively identify the shaft track and the running state of hydropower units.

scholarly journals Dual-polarized multiplexed meta-holograms utilizing coding metasurface

Nanophotonics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 3605-3613 ◽  
Author(s):  
Chunsheng Guan ◽  
Jian Liu ◽  
Xumin Ding ◽  
Zhuochao Wang ◽  
Kuang Zhang ◽  
...  
Keyword(s):  
Data Storage ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Split Ring ◽  
Practical Applications ◽  
Good Imaging ◽  
Linearly Polarized ◽  
Novel Method ◽  
Noise Ratio ◽  
Total Transmittance

AbstractIn this paper, a novel method is proposed to achieve two distinct information channels by simultaneously manipulating both the transmitted cross- and co-polarized components of a 1-bit coding metasurface under linearly polarized incidence. Compared to previously demonstrated incidence-switchable or position multiplexed holograms, our proposed coding meta-hologram can simultaneously project two independent holographic images without inevitable change of the incidence state and can at the same time also avoid crosstalk between different channels. Moreover, the orientation of the double-layered split ring (SR) apertures is specially designed to be 45° or 135° to achieve identical multiplexed functionality for both x-polarized and y-polarized incidences. The proof-of-concept experimental demonstrations present total transmittance efficiency above 30% for the dual linearly polarized incidences at 15 GHz, and good imaging performances with 53.98%/48.18% imaging efficiency, 1.55%/1.46% RMSE, and 29.9/28.72 peak signal-to-noise ratio for the cross-/co-polarized channels under y-polarized incidence, and 47.27%/45.75% imaging efficiency, 1.55%/1.43% RMSE, and 18.74/25.93 peak signal-to-noise ratio under x-polarized incidence, demonstrating great potential of the proposed multiplexed coding meta-hologram in practical applications such as data storage and information processing.

scholarly journals Research on AR-AKF Model Denoising of the EMG Signal

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Sijia Chen ◽  
Zhizeng Luo ◽  
Tong Hua
Keyword(s):  
State Space Model ◽  
Ar Model ◽  
Model Parameters ◽  
Time Varying ◽  
Adaptive Kalman Filter ◽  
Denoising Method ◽  
Signal Tracking ◽  
Emg Signal ◽  
Statistical Knowledge ◽  
Self Learning

Electromyography (EMG) signals can be used for clinical diagnosis and biomedical applications. It is very important to reduce noise and to acquire accurate signals for the usage of the EMG signals in biomedical engineering. Since EMG signal noise has the time-varying and random characteristics, the present study proposes an adaptive Kalman filter (AKF) denoising method based on an autoregressive (AR) model. The AR model is built by applying the EMG signal, and the relevant parameters are integrated to find the state space model required to optimally estimate AKF, eliminate the noise in the EMG signal, and restore the damaged EMG signal. To be specific, AR autoregressive dynamic modeling and repair for distorted signals are affected by noise, and AKF adaptively can filter time-varying noise. The denoising method based on the self-learning mechanism of AKF exhibits certain capabilities to achieve signal tracking and adaptive filtering. It is capable of adaptively regulating the model parameters in the absence of any prior statistical knowledge regarding the signal and noise, which is aimed at achieving a stable denoising effect. By comparatively analyzing the denoising effects exerted by different methods, the EMG signal denoising method based on the AR-AKF model is demonstrated to exhibit obvious advantages.

scholarly journals Deep Feature Autoextraction Method for Intrapulse Data of Radar Emitter Signal

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Shiqiang Wang ◽  
Caiyun Gao ◽  
Chang Luo ◽  
Huiyong Zeng ◽  
Guimei Zheng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Signal To Noise Ratio ◽  
Support Vector ◽  
Large Signal ◽  
Signal To Noise ◽  
Training Scheme ◽  
Deep Feature ◽  
Novel Method ◽  
Sparse Autoencoder

Concerned with the problems that the extracted features are the absence of objectivity for radar emitter signal intrapulse data because of relying on priori knowledge, a novel method is proposed. First, this method gets the sparse autoencoder by adding certain restrain to the autoencoder. Second, by optimizing the sparse autoencoder and confirming the training scheme, intrapulse deep features are autoextracted with encoder layer parameters. The method extracts the eigenvectors of six typical radar emitter signals and uses them as inputs to a support vector machine classifier. The experimental results show that the method has higher accuracy in the case of large signal-to-noise ratio. The simulation verifies that the extracted features are feasible.

Parametric Time-Frequency Map and its Processing for Local Damage Detection in Rotating Machinery

2013 ◽  
Vol 588 ◽  
pp. 214-222 ◽  
Author(s):  
Ryszard Makowski ◽  
Radoslaw Zimroz
Keyword(s):  
Damage Detection ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Time Varying ◽  
Local Damage ◽  
Time Frequency ◽  
Vibration Data ◽  
Novel Approach ◽  
Vibration Signal Analysis

The detection of local damage in rotating machinery (gears, bearings) via vibration signal analysis is one of the most powerful techniques in condition monitoring. However, in some cases, especially in heavy industrial machinery, it is difficult to detect damage because of the poor signal-to-noise ratio of the measured vibration. Therefore it is necessary to use unconventional advanced techniques to enhance the signal. In this paper, a novel approach based on parametric time-frequency analysis and further processing for: i) time-varying spectral content modelling, ii) the identification of informative frequency bands by statistical analysis, iii) local damage detection and iv) cycle identification via cepstral analysis, is presented. The proposed procedure is validated using real vibration data from bearings and gearboxes. It is worth noting that this methodology can be also successfully used in time-varying speed conditions (with limited fluctuation).

Evaluation of the Time Varying Gravity Fields from GRACE Follow-On LRI Instrument

2020 ◽  
Author(s):  
Nadege Pie ◽  
Mark Tamisiea ◽  
Ben Krichman ◽  
Peter Nagel ◽  
Steve Poole ◽  
...  
Keyword(s):  
High Frequency ◽  
Signal To Noise Ratio ◽  
Gravity Models ◽  
Laser Ranging ◽  
Frequency Noise ◽  
Time Varying ◽  
Signal To Noise ◽  
Science Data ◽  
Design Concepts ◽  
Gravity Fields

<p>The Laser Ranging Interferometer (LRI) instrument on-board of the GRACE Follow-On (GRACE-FO) satellites has been collecting science data since a June 2018, a few weeks after launch. Though the LRI instrument, based on design concepts developed for a future LISA mission, was intended mostly as a demonstration instrument, it has far-exceeded its mission requirements and has provided intersatellite ranging observations with improved signal-to-noise ratio compared to the K-Band Ranging (KBR) instrument. The exploitation of the LRI observations has led to a set of monthly gravity field solutions comparable in many ways to the ones obtained from KBR. Though the ranging observations from the LRI have a much lower high-frequency noise content, this has not so far led to an improvement of the time-varying gravity estimates in the spatial domain, while stark differences are visible in the spectral domain between the KBR and LRI fields. We present the series of LRI gravity models in comparison to its KBR counterpart, as well as regional intercomparisons of the gravity solutions against hydrology models.</p>

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