scholarly journals Influence of Noise on Fault Diagnosis of Transformer based on Vibration Signal

2021 ◽  
Vol 2065 (1) ◽  
pp. 012019
Author(s):  
Ming-hao Chen ◽  
Quan Zhou ◽  
Yangxi Ou
Keyword(s):  
Vibration Signal ◽  
Electrical Equipment ◽  
Signal Identification ◽  
Operating Environment ◽  
Noise Sources ◽  
Fan Noise ◽  
Mechanical Faults ◽  
The Impact ◽  
Influence Of Noise ◽  
Universal Application

Abstract Monitoring transformer vibration signals is a universal application method to realize the diagnosis of internal mechanical faults of transformers. However, the actual transformer operating is interfered by the noise of the surrounding electrical equipment, which reduces the accuracy of the vibration signal identification. This paper simulate the typical noise sources in the actual transformer operating environment, including fan noise and surrounding equipment fault noise, and explore the impact of different noise sources on the transformer vibration signal.

The Impact of Low-Speed Fan Design on Noise: An Exploratory Study

2015 ◽  
Author(s):  
Antoine Moreau ◽  
Sébastien Guérin
Keyword(s):  
Pressure Ratio ◽  
Broadband Noise ◽  
Tonal Noise ◽  
Noise Sources ◽  
Fan Noise ◽  
Theoretical Formulation ◽  
Optimum Pressure ◽  
Fuel Burn ◽  
Parametric Studies ◽  
The Impact

The steady evolution since the 1950’s towards higher bypass ratio engines has enhanced the acoustic role of the fan compared to the jet. This paper addresses the following question: does a further decrease in fan pressure ratio and rotor tip speed provide a significant reduction of fan broadband and tonal noise? The paper presents two conceptual parametric studies conducted with a fast analytical aerodynamic and acoustic prediction tool. The tool includes an aerodynamic fan design model which provides the quantities necessary to assess the trade-off between efficiency and noise at given thrust conditions. The fan acoustic model has a theoretical formulation for broadband and tonal noise sources which is not based on empirical correlations; it is applied on conventional and contra-rotating fan concepts. The first study proposes a variation of the design fan pressure ratio and evaluates for each concept its impact on noise at three acoustic off-design points. The results obtained, which are in line with a past NASA study, indicate that the optimum pressure ratio in terms of fan noise is well below the fuel-burn optimum. Significant noise reductions of the broadband and tonal interaction components can be achieved with fans operating in a fully subsonic domain. Alternatively, designing at higher speed and pressure ratio near the fuel-burn optimum may invite to consider the contra-rotating fan as a candidate: it performs there very well in terms of buzz-saw and broadband noise compared to the conventional fan. The second study addresses the variation of design rotor tip speed at constant fan pressure ratio. Although reduced tip speed may suppress buzz-saw noise, the increased loading related to it implies large blade solidities and wakes which causes a significant increase in broadband noise. Thus, there is an optimum loading that will depend on the severity of fan inflow distortion and on the onset of buzz-saw noise. Here again these conclusions confirm some experimental work performed by NASA on two different fans, and by Rolls-Royce on a third one.

The Impact of Low-Speed Fan Design on Noise: An Exploratory Study

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Antoine Moreau ◽  
Sébastien Guérin
Keyword(s):  
Pressure Ratio ◽  
Broadband Noise ◽  
Tonal Noise ◽  
Noise Sources ◽  
Fan Noise ◽  
Theoretical Formulation ◽  
Optimum Pressure ◽  
Fuel Burn ◽  
Parametric Studies ◽  
The Impact

The steady evolution since the 1950s toward higher bypass ratio engines has enhanced the acoustic role of the fan compared to the jet. This paper addresses the following question: Does a further decrease in fan pressure ratio (FPR) and rotor tip speed provide a significant reduction of fan broadband and tonal noise? The paper presents two conceptual parametric studies conducted with a fast analytical aerodynamic and acoustic prediction tool. The tool includes an aerodynamic fan design model which provides the quantities necessary to assess the tradeoff between efficiency and noise at given thrust conditions. The fan acoustic model has a theoretical formulation for broadband and tonal noise sources which is not based on empirical correlations; it is applied on conventional and contrarotating fan concepts. The first study proposes a variation of the design FPR and evaluates for each concept its impact on noise at three acoustic off-design points. The results obtained, which are in line with a past NASA study, indicate that the optimum pressure ratio in terms of fan noise is well below the fuel-burn optimum. Significant noise reductions of the broadband and tonal interaction components can be achieved with fans operating in a fully subsonic domain. Alternatively, designing at higher speed and pressure ratio near the fuel-burn optimum may invite to consider the contrarotating fan as a candidate: it performs very well in terms of buzz-saw and broadband noise compared to the conventional fan. The second study addresses the variation of design rotor tip speed at constant FPR. Although reduced tip speed may suppress buzz-saw noise, the increased loading related to it implies large blade solidities and wakes which causes a significant increase in broadband noise. Thus, there is an optimum loading that will depend on the severity of fan inflow distortion and on the onset of buzz-saw noise. Here again these conclusions confirm some experimental work performed by NASA on two different fans, and by Rolls-Royce on a third one.

scholarly journals Exoplanet mass estimation for a sample of targets for the Ariel mission

2021 ◽  
Author(s):  
J. R. Barnes ◽  
C. A. Haswell
Keyword(s):  
Radial Velocity ◽  
Input Parameter ◽  
Noise Sources ◽  
Time Commitment ◽  
Low Mass ◽  
Time Required ◽  
Fixed Input ◽  
The Impact ◽  
Quadrature Sampling

AbstractAriel’s ambitious goal to survey a quarter of known exoplanets will transform our knowledge of planetary atmospheres. Masses measured directly with the radial velocity technique are essential for well determined planetary bulk properties. Radial velocity masses will provide important checks of masses derived from atmospheric fits or alternatively can be treated as a fixed input parameter to reduce possible degeneracies in atmospheric retrievals. We quantify the impact of stellar activity on planet mass recovery for the Ariel mission sample using Sun-like spot models scaled for active stars combined with other noise sources. Planets with necessarily well-determined ephemerides will be selected for characterisation with Ariel. With this prior requirement, we simulate the derived planet mass precision as a function of the number of observations for a prospective sample of Ariel targets. We find that quadrature sampling can significantly reduce the time commitment required for follow-up RVs, and is most effective when the planetary RV signature is larger than the RV noise. For a typical radial velocity instrument operating on a 4 m class telescope and achieving 1 m s−1 precision, between ~17% and ~ 37% of the time commitment is spent on the 7% of planets with mass Mp < 10 M⊕. In many low activity cases, the time required is limited by asteroseismic and photon noise. For low mass or faint systems, we can recover masses with the same precision up to ~3 times more quickly with an instrumental precision of ~10 cm s−1.

scholarly journals Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning

2021 ◽  
Vol 11 (10) ◽  
pp. 4602
Author(s):  
Farzin Piltan ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Variable Structure ◽  
Vibration Signal ◽  
Crack Size ◽  
Approximation Technique ◽  
Digital Twin ◽  
Average Accuracy ◽  
Machine Learning Approach ◽  
The Impact ◽  
Signal Approximation

In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated with machine learning-based signal approximation to approximate the bearing vibration signal in normal conditions. After that, the combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy technique is integrated with the proposed signal approximation technique to design an intelligent digital twin. Next, the residual signals will be generated using the proposed intelligent digital twin and the original RAW signals. The machine learning approach will be integrated with the proposed intelligent digital twin for the classification of the bearing anomaly and crack sizes. The Case Western Reserve University bearing dataset is used to test the impact of the proposed scheme. Regarding the experimental results, the average accuracy for the bearing fault pattern recognition and crack size identification will be, respectively, 99.5% and 99.6%.

Research on Shearer Rocker Gearbox Fault Location Based on Wavelet Envelope Demodulation

2014 ◽  
Vol 528 ◽  
pp. 210-216
Author(s):  
Zeng Qiang Wang ◽  
Hong Wei Ma ◽  
Mei Hua Tao ◽  
Xu Hui Zhang ◽  
Qing Hua Mao
Keyword(s):  
Fourier Transform ◽  
Frequency Spectrum ◽  
Spectrum Analysis ◽  
Fault Location ◽  
Vibration Signal ◽  
Morlet Wavelet ◽  
Frequency Spectrum Analysis ◽  
Mechanical Faults ◽  
Faults Diagnosis ◽  
Feature Frequency

To solve the problem of faults location for shearer rocker gearbox, the multiple sites vibration signal of faulty rocker gearbox are collected, as well as the Morlet wavelet envelope demodulation is applied to demodulate vibration signal and Fourier transform is used to carry out frequency spectrum analysis of vibration signal. Experimental results show that this method can effectively extract the faults feature frequency from complex vibration signal. The faults location result is consistent with actual faults part. This mean realizes to locate faults accurately. It provides an effective method for mechanical faults diagnosis of shearer.

scholarly journals Quantum Divide and Compute: Exploring the Effect of Different Noise Sources

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Thomas Ayral ◽  
François-Marie Le Régent ◽  
Zain Saleem ◽  
Yuri Alexeev ◽  
Martin Suchara
Keyword(s):  
Success Probability ◽  
Quantum Circuit ◽  
Noise Sources ◽  
Output Distribution ◽  
Tensor Network ◽  
Learning Machine ◽  
Society Annual ◽  
The Impact ◽  
Noise Models ◽  
Detailed Derivation

AbstractOur recent work (Ayral et al. in Proceedings of IEEE computer society annual symposium on VLSI, ISVLSI, pp 138–140, 2020. 10.1109/ISVLSI49217.2020.00034) showed the first implementation of the Quantum Divide and Compute (QDC) method, which allows to break quantum circuits into smaller fragments with fewer qubits and shallower depth. This accommodates the limited number of qubits and short coherence times of quantum processors. This article investigates the impact of different noise sources—readout error, gate error and decoherence—on the success probability of the QDC procedure. We perform detailed noise modeling on the Atos Quantum Learning Machine, allowing us to understand tradeoffs and formulate recommendations about which hardware noise sources should be preferentially optimized. We also describe in detail the noise models we used to reproduce experimental runs on IBM’s Johannesburg processor. This article also includes a detailed derivation of the equations used in the QDC procedure to compute the output distribution of the original quantum circuit from the output distribution of its fragments. Finally, we analyze the computational complexity of the QDC method for the circuit under study via tensor-network considerations, and elaborate on the relation the QDC method with tensor-network simulation methods.

The Impact of Reference Environment Selection on the Exergy Efficiencies of Aerospace Engines

1999 ◽  
Author(s):  
Jason Etele ◽  
Marc A. Rosen
Keyword(s):  
Sea Level ◽  
Exergy Analysis ◽  
Operating Environment ◽  
A Value ◽  
Turbojet Engine ◽  
Reference Environment ◽  
The Impact

Abstract An exergy analysis is applied to a turbojet engine over a range of flight altitudes ranging from sea level to 15,000 m (∼50,000 ft), to examine the effects of using different reference-environment models. The results of this analysis using a variable reference environment (equal to the operating environment at all times) are compared to the results obtained using two constant reference environments (sea level and 15,000 m). The actual rational efficiency of the turbojet decreases with increasing altitude, ranging from a value of 16.9% at sea level to 15.3% at 15,000 m. In the most extreme cases considered, the rational efficiency value calculated using a constant reference environment varies by approximately 2% from the variable reference environment value.

scholarly journals A Fiber Vibration Signal Identification Method Based on the Combination of EWT-FE and LSTM

2021 ◽  
Vol 2112 (1) ◽  
pp. 012020
Author(s):  
Xin Zhang ◽  
Qingmo Ja ◽  
SaiSai Ruan ◽  
Qin Hu
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Real Life ◽  
Vibration Signal ◽  
Machine Learning Algorithms ◽  
Signal Identification ◽  
Feature Vectors ◽  
Identification Method ◽  
Effective Manner ◽  
Empirical Wavelet Transform

Abstract As the optical fiber perimeter security system is widely used in real life, how to identify the types of intrusion events in a timely and effective manner is becoming a major research hotspot. At present, in this field, various signal feature extraction algorithms are usually used to extract intrusion signal features to form feature vectors, and then machine learning algorithms are used to classify the feature vectors to achieve the role of identifying the types of intrusion events. As a common signal feature extraction algorithm, the EMD algorithm has been widely used in the feature extraction of various vibration signals, but it will have the problem of modal aliasing and affect the feature extraction effect of the signal. Therefore, EWT, VMD and other algorithms have been successively used proposed to improve modal aliasing. On the basis of fully comparing the existing algorithms, this paper proposes a fiber vibration signal identification method that decomposes the signal through the empirical wavelet transform (EWT) algorithm and then extracts the fuzzy entropy (FE) of each component, and uses LSTM for classification. The final experiment shows that the method can identify four kinds of fiber intrusion signals in time and effectively, with an average recognition accuracy rate of 97.87%, especially for flap and knock recognition rate of 100%.

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