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.

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.

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 Feature Extraction Method for Weak Faults Based on Time-Delayed Feedback Mixed Potential Stochastic Resonance

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jiachen Tang ◽  
Boqiang Shi ◽  
Zhixing Li
Keyword(s):  
Feature Extraction ◽  
Stochastic Resonance ◽  
Ant Colony Algorithm ◽  
Delayed Feedback ◽  
Mixed Potential ◽  
Feature Extraction Method ◽  
Delay Term ◽  
Short Memory ◽  
Weak Fault ◽  
Time Delayed Feedback

To extract weak faults under strong noise, a method for feature extraction of weak faults with time-delayed feedback mixed potential stochastic resonance (TFMSR) is proposed. This method not only overcomes the saturation characteristics of classical bistable stochastic resonance (CBSR), but also verifies a new potential function model. Based on this model, considering the short memory characteristics of the CBSR method, a method is proposed that can add historical information to the negative feedback process of the stochastic resonance (SR). Through the combination of the above two methods, the weak fault extraction under strong background noise is realized. The article analyzes the effects of the delay term, feedback term, and system parameter on the effect of SR and uses the ant colony algorithm (ACA) to optimize the above parameters. Finally, through simulated and engineering experimental results, it is proved that the proposed method has more advantages than the CBSR method in weak fault feature extraction.

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