scholarly journals Selection Strategy of Vibration Feature Target under Centrifugal Pumps Cavitation

2020 ◽  
Vol 10 (22) ◽  
pp. 8190
Author(s):  
Ruijia Cao ◽  
Jianping Yuan
Keyword(s):  
Correlation Method ◽  
Vibration Signal ◽  
Entropy Method ◽  
Selection Strategy ◽  
Analysis Method ◽  
Centrifugal Pumps ◽  
Acceleration Signal ◽  
Operation Conditions ◽  
Multiple Operation ◽  
Feature Target

The cavitation states among centrifugal pumps can be mirrored by corresponding vibration features. To select the vibration feature target scientifically and objectively for monitor the cavitation states in real time, the analysis method of grey slope correlation with weight entropy was proposed in this paper to explore the relevance between cavitation and vibration features. Thus, the net positive suction head (NPSH) and vibration signal from centrifugal pumps under multiple operation conditions were captured. Moreover, the universal feature targets were extracted from the vibration signal. The grey slope correlation method was applied in the analysis of the positive and negative relevance between NPSH and the multiple operation conditions in a different stage. These feature targets are transformed into the same numerical scale by standardization process. In the end, the final comprehensive coefficient can be attached after endowing power by weight entropy method. These methods can be used to determine the feature targets which have intensive relevance with NPSH. The analysis results indicate that the kurtosis factor, variance, absolute mean, and root mean square obtained from the vibration acceleration signal have stable relevance with NPSH. These feature targets can be used for the proper detection and evaluation of cavitation states in centrifugal pumps. Therefore, the analysis method of grey slope correlation with weight entropy can be used to pre-select the feature targets based on the calculated grey incidence. This method is effective in establishing the relevance between NPSH and vibration.

Data-driven fault diagnosis of FW-UAVs with consideration of multiple operation conditions

2021 ◽  
Author(s):  
Shaojun Liang ◽  
Shirong Zhang ◽  
Yuping Huang ◽  
Xing Zheng ◽  
Jian Cheng ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Data Driven ◽  
Operation Conditions ◽  
Multiple Operation

scholarly journals ASSESSMENT OF URBAN SPRAWL USING SHANNON’S ENTROPY AND FRACTAL ANALYSIS: A CASE STUDY OF ATAKUM, ILKADIM AND CANIK (SAMSUN, TURKEY)

2017 ◽  
Vol 25 (3) ◽  
pp. 264-276 ◽  
Author(s):  
Derya OZTURK
Keyword(s):  
Urban Sprawl ◽  
Fractal Analysis ◽  
Urban Form ◽  
Entropy Method ◽  
Shannon’S Entropy ◽  
Analysis Method ◽  
Maximum Likelihood Classification ◽  
Shannon's Entropy ◽  
Box Counting

Urban sprawl is one of the most important problems in urban development due to its negative environmental and societal impacts. Therefore, the spatial pattern of urban growth should be accurately analyzed and well understood for effective urban planning. This paper focuses on urban sprawl analysis in the Atakum, Ilkadim and Canik districts of Samsun, Turkey. In this study, urban sprawl was examined over a period of 24 years using Shannon's entropy and fractal analysis based on remote sensing and Geographic Information System (GIS). The built-up areas in 1989, 2000 and 2013 were extracted from Landsat TM/ETM+/OLI images using the maximum likelihood classification method, and urban form changes in the 1989–2013 period were investigated. The Shannon's entropy method was used to determine the degree of urban sprawl, and a fractal analysis method based on box counting was used to characterize the urban sprawl. The results show that Atakum, Ilkadim and Canik experienced important changes and have considerable sprawl and complex characteristics now. The study also revealed that there is no monotonic relationship between Shannon's entropy and fractal dimension.

Mine Ventilator Fault Diagnosis Based on Harmonic Wavelet Analysis

2011 ◽  
Vol 143-144 ◽  
pp. 613-617
Author(s):  
Shuang Xi Jing ◽  
Yong Chang ◽  
Jun Fa Leng
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Frequency Domain ◽  
Vibration Signal ◽  
Wavelet Function ◽  
Frequency Region ◽  
Analysis Method ◽  
Vibration Signals ◽  
Harmonic Wavelet ◽  
Frequency Components

Harmonic wavelet function, with the strict box-shaped characteristic of spectrum, has strong ability of identifying signal in frequency domain, and can extract weak components form vibration signals in frequency domain. Using harmonic wavelet analysis method, the selected frequency region and other frequency components of vibration signal of mine ventilator were decomposed into independent frequency bands without any over-lapping or leaking. Simulation and diagnosis example show that this method has good fault diagnosis effect, and the ventilator fault is diagnosed successfully.

scholarly journals Active and Reactive Power Joint Balancing for Analyzing Short-Term Voltage Instability Caused by Induction Motor

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3617
Author(s):  
Ding Wang ◽  
Yangwu Shen ◽  
Zhen Hu ◽  
Ting Cui ◽  
Xiaoming Yuan
Keyword(s):  
Induction Motor ◽  
Reactive Power ◽  
Movement Direction ◽  
Analysis Method ◽  
Short Term ◽  
Voltage Instability ◽  
Operation Conditions ◽  
Active And Reactive Power ◽  
Acceleration And Deceleration ◽  
Power Balancing

Short-term voltage instability has a sensational effect once it occurs with massive loss of load, possibly area instability, and voltage collapse. This paper analyzes the short-term voltage instability caused by induction motor from the viewpoint of active and reactive power joint balancing. The analysis method is based on (1) the reactive power balancing between system supply and induction motor demand, and (2) the active power balancing between air-gap power and mechanical power, which is expressed by the region of rotor acceleration and deceleration in the Q-V plane. With the region of rotor acceleration and deceleration in the Q-V plane and the reactive power balancing, the movement direction of the operating point can be visually observed in the Q-V plane, thereby achieving a clear comprehension of physical properties behind the short-term voltage instability phenomenon. Furthermore, the instability mechanisms of two kinds of grid-connected induction motor operation conditions after a large disturbance are discussed to explain the basic theory of the analysis method and to provide examples of its application. Time-domain simulations are presented for a single-load infinite-bus system to validate the analyses.

scholarly journals Hybridization of time synchronous averaging, singular value decomposition, and adaptive neuro fuzzy inference system for multi-fault bearing diagnosis

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098056
Author(s):  
Walid Touzout ◽  
Djamel Benazzouz ◽  
Fawzi Gougam ◽  
Adel Afia ◽  
Chemseddine Rahmoune
Keyword(s):  
Singular Value Decomposition ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Vibration Signal ◽  
Relevant Information ◽  
Singular Value ◽  
Inference System ◽  
Operation Conditions ◽  
Neuro Fuzzy ◽  
Value Decomposition

Bearing diagnosis has attracted considerable research interest; thus, researchers have developed several signal processing techniques using vibration analysis to monitor the rotating machinery’s conditions. In practical engineering, features extraction with most relevant information from experimental vibration signals under variable operation conditions is still regarded as the most critical concern. Therefore, actual works focus on combining Time Domain Features (TDFs) with decomposition techniques to obtain accurate results for defect detection, identification, and classification. In this paper, a new hybrid method is proposed, which is based on Time Synchronous Averaging (TSA), TDFs, and Singular Value Decomposition (SVD) for the feature extraction, then the Adaptive Neuro-Fuzzy Inference System (ANFIS) which gathers the advantages of both neural networks and fuzzy logic is applied for the classification process. First, TSA is used to reduce noises in the vibration signal by extracting the periodic waveforms from the disturbed data; thereafter, TDFs are applied on each synchronous signal to construct a feature matrix; afterwards, SVD is performed on the obtained matrices to remove the instability of statistical values and select the most stable vectors. Finally, ANFIS is implemented to provide a powerful automatic tool for features classification.

Wavelet Cross-Correlation Analysis of the Unsteady Signals of the Flow in the Open Sump

Volume 1 ◽  
2004 ◽  
Author(s):  
Mansa Kante ◽  
Yulin Wu ◽  
Yong Li ◽  
Shuhong Liu ◽  
Daqing Zhou
Keyword(s):  
Cross Correlation ◽  
Correlation Method ◽  
Vibration Signal ◽  
Structural Similarity ◽  
Signal Distribution ◽  
Cross Correlation Analysis ◽  
Correlation Scale ◽  
Pump Sump ◽  
The Relationship ◽  
Signal Analysis Method

The wavelet cross-correlation method was used to analyze the unsteady signals of the flow of the model open pump sump, which include pressure signal, vibration signal and acoustic signal. The continuous wavelet transform was done first to find the signal distribution at various periods and at any time, then the wavelet cross-correlation was used to find the relationship between the signals taken two a two. Through comparing the result of wavelet cross-correlation and the result of classic cross-correlation, one can find the correlation scale of any two unsteady signals (pressure-vibration, pressure-noise, and vibration-noise). The signal on the correlation scale was reconstruct and its characteristics were obtained using classical signal analysis method same as the structural similarity of a arbitrary two signals.

scholarly journals Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD

2020 ◽  
Vol 10 (18) ◽  
pp. 6376 ◽  
Author(s):  
Yihan Wang ◽  
Zhonghui Fan ◽  
Hongmei Liu ◽  
Xin Gao
Keyword(s):  
Fault Diagnosis ◽  
Information Entropy ◽  
Entropy Method ◽  
Characteristic Matrix ◽  
Intrinsic Mode Functions ◽  
Noise Component ◽  
Time Frequency ◽  
Frequency Information ◽  
Operation Conditions ◽  
Mode Decomposition

Planetary gearboxes are more and more widely used in large and complex construction machinery such as those used in aviation, aerospace fields, and so on. However, the movement of the gear is a typical complex motion and is often under variable conditions in real environments, which may make vibration signals of planetary gearboxes nonlinear and nonstationary. It is more difficult and complex to achieve fault diagnosis than to fix the axis gearboxes effectively. A fault diagnosis method for planetary gearboxes based on improved complementary ensemble empirical mode decomposition (ICEEMD)-time-frequency information entropy and variable predictive model-based class discriminate (VPMCD) is proposed in this paper. First, the vibration signal of planetary gearboxes is decomposed into several intrinsic mode functions (IMFs) by using the ICEEMD algorithm, which is used to determine the noise component by using the magnitude of the entropy and to remove the noise components. Then, the time-frequency information entropy of intrinsic modal function under the new decomposition is calculated and regarded as the characteristic matrix. Finally, the fault mode is classified by the VPMCD method. The experimental results demonstrate that the method proposed in this paper can not only solve the fault diagnosis of planetary gearboxes under different operation conditions, but can also be used for fault diagnosis under variable operation conditions. Simultaneously, the proposed method is superior to the wavelet entropy method and variational mode decomposition (VMD)-time-frequency information entropy.

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