Blade Incipient Crack Determination for Centrifugal Compressor Based on Pressure Pulsation Signal Feature Extraction

2016 ◽  
Author(s):  
Hongkun Li ◽  
Changbo He ◽  
Daren Jiang ◽  
Xuejun Wang
Keyword(s):  
Feature Extraction ◽  
Centrifugal Compressor ◽  
Pressure Pulsation ◽  
Feature Extraction Method ◽  
Crack Classification ◽  
Compressor Impeller ◽  
Simulation Signal ◽  
Blade Crack ◽  
Envelope Spectrum ◽  
Incipient Crack

Centrifugal compressor is a piece of key equipment for factories. Among the components of a centrifugal compressor, impeller is a pivotal part as it is used to transform kinetic energy to pressure energy. The blades are exposed to centrifugal forces, gas pressure, and the friction force which usually lead to cracks. Therefore, early crack feature extraction and pattern recognition are important to prevent it from failure. Although time series analysis for monitored signals can be used on feature extraction, it is not enough. So the incipient weak feature extraction method should be investigated. In this research, pressure pulsation sensors arranged close to crack area are used to monitor the blade crack signal and extract the feature information. As the different kinds of interference of flow, the pressure pulsation signals for a centrifugal compressor are full of nonlinear characteristics. Therefore, how to obtain the weak information from monitored signals effectively should be investigated. A method on blade crack classification is present by continuous wavelet transform (CWT) and envelope spectrum in this research. Simulation signal analysis and experimental investigation on blade crack classification are carried out to verify the effectiveness of this method. The results show that it is an effective tool for blade incipient crack classification for a centrifugal compressor.

Blade Incipient Crack Determination for Centrifugal Compressor Based on CWT-Stochastic Resonance Method

2017 ◽  
Author(s):  
Hongkun Li ◽  
Changbo He ◽  
Qiang Zhou ◽  
Fuan Lu
Keyword(s):  
Feature Extraction ◽  
Stochastic Resonance ◽  
Centrifugal Compressor ◽  
Pressure Pulsation ◽  
Resonance Method ◽  
Aerodynamic Load ◽  
Load Effect ◽  
Feature Extraction Method ◽  
Blade Crack ◽  
Incipient Crack

Centrifugal compressor is a piece of key equipment for factories. Among the components of centrifugal compressor, impeller is a pivotal part as it is used to transform kinetic energy to pressure energy. But it usually leads to blade crack or failure as irregular aerodynamic load effect on the blade. Therefore, early crack feature extraction and pattern recognition is important to prevent it from failure. Although time series analysis for monitored signal can be used on feature extraction, incipient weak feature extraction method should be investigated. In this research, pressure pulsation sensors arranged in close vicinity to crack area are used to monitor the blade crack and feature extraction. As there are different kinds of flow interference, the pressure pulsation signal for centrifugal compressor is full of nonlinear characteristics. Therefore, how to obtain the weak information from monitored signal is investigated. Although FFT and envelope analysis have been widely used for rotating equipment, they are not suitable for the determination of incipient crack of a blade as the signal modulation and noise interference. In this research, stochastic resonance is used for the pressure pulsation signal. The results show that it is an effective tool to blade incipient crack classification on centrifugal compressor.

scholarly journals Pressure Pulsation Signal Analysis for Centrifugal Compressor Blade Crack Determination

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Hongkun Li ◽  
Xuefeng Zhang ◽  
Xiaowen Zhang ◽  
Shuhua Yang ◽  
Fujian Xu
Keyword(s):  
Centrifugal Compressor ◽  
Early Warning ◽  
Signal Analysis ◽  
Pressure Pulsation ◽  
Process Analysis ◽  
Dynamic Strain ◽  
Blade Vibration ◽  
Mode Decomposition ◽  
Blade Failure ◽  
Blade Crack

Blade is a key piece of component for centrifugal compressor. But blade crack could usually occur as blade suffers from the effect of centrifugal forces, gas pressure, friction force, and so on. It could lead to blade failure and centrifugal compressor closing down. Therefore, it is important for blade crack early warning. It is difficult to determine blade crack as the information is weak. In this research, a pressure pulsation (PP) sensor installed in vicinity to the crack area is used to determine blade crack according to blade vibration transfer process analysis. As it cannot show the blade crack information clearly, signal analysis and empirical mode decomposition (EMD) are investigated for feature extraction and early warning. Firstly, signal filter is carried on PP signal around blade passing frequency (BPF) based on working process analysis. Then, envelope analysis is carried on to filter the BPF. In the end, EMD is carried on to determine the characteristic frequency (CF) for blade crack. Dynamic strain sensor is installed on the blade to determine the crack CF. Simulation and experimental investigation are carried on to verify the effectiveness of this method. The results show that this method can be helpful for blade crack classification for centrifugal compressors.

scholarly journals Multistage Fault Feature Extraction of Consistent Optimization for Rolling Bearings Based on Correlated Kurtosis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Long Zhang ◽  
Binghuan Cai ◽  
Guoliang Xiong ◽  
Jianmin Zhou ◽  
Wenbin Tu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Diagnostic Methods ◽  
Rolling Bearings ◽  
Feature Extraction Method ◽  
Transmission Path ◽  
The Individual ◽  
Envelope Spectrum ◽  
Fault Feature Extraction

Fault diagnosis of rolling bearings is not a trivial task because fault-induced periodic transient impulses are always submerged in environmental noise as well as large accidental impulses and attenuated by transmission path. In most hybrid diagnostic methods available for rolling bearings, the problems lie in twofolds. First, most optimization indices used in the individual signal processing stage do not take the periodical characteristic of fault transient impulses into consideration. Second, the individual stages make use of different optimization indices resulting in inconsistent optimization directions and possibly an unsatisfied diagnosis. To solve these problems, a multistage fault feature extraction method of consistent optimization for rolling bearings based on correlated kurtosis (CK) is proposed where maximum correlated kurtosis deconvolution (MCKD) is employed to attenuate the influence of transmission path followed by tunable Q factor wavelet transform (TQWT) to further enhance fault features by decomposing the preprocessed signals into multiple subbands under different Q values. The major contribution of the proposed approach is to consistently use CK as an optimization index of both MCKD and TQWT. The subband signal with the maximum CK which is an index being able to measure the periodical transient impulses in vibration signal yields an envelope spectrum, from which fault diagnosis is implemented. Simulated and experimental signals verified the effectiveness and advantages of the proposed method.

scholarly journals Sparse Envelope Spectra for Feature Extraction of Bearing Faults Based on NMF

2019 ◽  
Vol 9 (4) ◽  
pp. 755 ◽  
Author(s):  
Lin Liang ◽  
Lei Shan ◽  
Fei Liu ◽  
Ben Niu ◽  
Guanghua Xu
Keyword(s):  
Feature Extraction ◽  
Frequency Band ◽  
Rolling Bearing ◽  
Weight Vector ◽  
Inner Product ◽  
Bearing Faults ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Envelope Spectrum ◽  
Transient Oscillations

Periodic impulses and the oscillation response signal are the vital feature indicators of rolling bearing faults. However, finding the suitable feature frequency band is usually difficult due to the interferences of other components and multiple resonance regions. According to the characteristics of non-negative matrix factorization (NMF) on a spectrogram, the feature extraction method from a sparse envelope spectrum for rolling bearing faults is proposed in this paper. On the basis of the time–frequency distribution (TFD) of the periodic transient oscillations, the basic matrix can be interpreted as the spectral bases, and the time weight matrix corresponding to spectral bases can be extracted by NMF. Because the bases and the weights have a one-to-one correspondence, the frequency band filtering with the basic component and the time domain envelope of the weight vector are calculated respectively. Then, the sparse envelope spectrum can be derived by the inner product of the above results. The effectiveness of the proposed method is verified by simulations and experiments. Compared with band-pass filtering and spectral kurtosis methods, and considering the time weights and corresponding the spectral bases for the periodic transient oscillations, the weak fault-rated feature can be enhanced in the sparse spectrum, while other components and noise are weakened. Therefore, the proposed method can reduce the requirement of selecting frequency band filtering.

scholarly journals Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1555
Author(s):  
Hui Li ◽  
Fan Li ◽  
Rong Jia ◽  
Fang Zhai ◽  
Liang Bai ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wind Turbine ◽  
Rolling Bearing ◽  
Classification Model ◽  
Feature Extraction Method ◽  
Vibration Data ◽  
Mode Decomposition ◽  
Simulation Signal ◽  
Fault Feature Extraction ◽  
Geometric Mode

Symplectic geometric mode decomposition (SGMD) is a newly proposed signal processing method. Because of its superiority, it has gained more and more attention in the field of fault diagnosis. However, the similar component reorganization problem involved in this method has not been clearly stated. Aiming at this problem, this paper proposes the SGMD-CS method based on the SGMD method and the cosine similarity (CS) and has been compared and verified on the simulation signal and the actual rolling bearing signal. In addition, in order to realize the intelligent diagnosis of the wind turbine bearing fault, the symplectic geometric entropy (SymEn) is extracted as the fault feature and input it into the AdaBoost classification model. In summary, this paper proposes a new wind turbine fault feature extraction method based on the SGMD-CS and AdaBoost framework, and the validity of the method is verified by the rolling bearing vibration data of the Electrical Engineering Laboratory of Case Western Reserve University.

scholarly journals Rolling Bearing Feature Extraction Method Based on Improved Intrinsic Time-Scale Decomposition and Mathematical Morphological Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2719
Author(s):  
Jianpeng Ma ◽  
Guodong Chen ◽  
Chengwei Li ◽  
Liwei Zhan ◽  
Guang-Zhu Zhang
Keyword(s):  
Feature Extraction ◽  
Time Scale ◽  
Rolling Bearing ◽  
Feature Extraction Method ◽  
Intrinsic Time ◽  
Extraction Scheme ◽  
Time Scale Decomposition ◽  
Adaptive Feature Extraction ◽  
Envelope Spectrum ◽  
Fault Feature Extraction

To overcome the difficulty of extracting the feature frequency of early bearing faults, this paper proposes an adaptive feature extraction scheme. First, the improved intrinsic time-scale decomposition, proposed in this paper, is used as a noise reduction method. Then, we use the adaptive composite quantum morphology analysis method, also proposed in this paper, to perform an adaptive demodulation analysis on the signal, and finally, extract the fault characteristics in the envelope spectrum. The experimental results show that the scheme performs well in the early fault feature extraction of rolling bearings.

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