TACHOLESS ORDER TRACKING FILTERING FOR RUN-UP OR COAST DOWN VIBRATION SIGNAL OF ROTATING MACHINERY BASED ON ZERO-PHASE DISTORTION DIGITAL FILTERING

2004 ◽  
Vol 40 (03) ◽  
pp. 50
Author(s):  
Yu Guo
Keyword(s):  
Digital Filtering ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Phase Distortion ◽  
Order Tracking ◽  
Run Up ◽  
Zero Phase

Order Tracking Filtering Based on Instantaneous Frequency Estimation and Zero-Phase Distortion Digital Filtering

2003 ◽  
Author(s):  
Shuren Qin ◽  
Yu Guo
Keyword(s):  
Instantaneous Frequency ◽  
Frequency Estimation ◽  
Digital Filtering ◽  
Vibration Signal ◽  
Phase Distortion ◽  
Instantaneous Frequency Estimation ◽  
Order Tracking ◽  
Run Up ◽  
Tracking Filter ◽  
Zero Phase

A new method for run-up or coast down vibration signal order tracking filtering of rotating machinery based on instantaneous frequency estimation and zero-phase distortion digital filtering is proposed in this paper. By contrast with traditional methods of order tracking filtering, the features of tacholess, no hardware-based tracking filter required and no frequency shift to original sampling data, etc, make it more attractive. The theorems and algorithms of the method are detailed discussed in this paper. The filtering of overlapped data blocks is used to restrain the edge effect, which caused by digital filtering, is also introduced. An actual test example of a motor’s run-up and coast down vibration is introduced to demonstrate the validity of the method.

Order Tracking Filtering for Rotating Machinery Diagnosis Based on Zero Phase Distortion Digital Filtering

2005 ◽  
Vol 295-296 ◽  
pp. 747-752
Author(s):  
S.R. Qin ◽  
Y. Guo
Keyword(s):  
Frequency Estimation ◽  
Digital Filtering ◽  
Rotating Machinery ◽  
Phase Distortion ◽  
New Method ◽  
Instantaneous Frequency Estimation ◽  
Order Tracking ◽  
Run Up ◽  
Tracking Filter ◽  
Zero Phase

A new method for order tracking filtering of rotating machinery based on instantaneous frequency estimation and zero-phase distortion digital filtering is proposed. Compared with the traditional methods for order tracking filtering, the new method has a number of attractive features such as tacholess, no hardware-based tracking filter and no frequency shift to the original sampling data. The theorems and algorithms of the method are discussed. Filtering of overlapped data blocks, used to restrain the edge effect, which is caused by digital filtering, is introduced. An actual test example of run-up and coast down vibration of a motor is presented to demonstrate the validity of the method.

The Order Tracking Study of Variant Speed Process of Aeroengine Vibration Signal

2009 ◽  
Vol 419-420 ◽  
pp. 805-808
Author(s):  
Xiang Yang Jin ◽  
Shi Sheng Zhong
Keyword(s):  
Vibration Signal ◽  
Rotating Machinery ◽  
State Monitoring ◽  
Tracking Method ◽  
Rotating Speed ◽  
Response Characteristics ◽  
Order Tracking ◽  
Rotating Shafts ◽  
Run Up ◽  
Fault Information

Order tracking is an important method to analyze the response characteristics of rotating machinery for fault diagnosis and condition monitoring in rotating machinery. Order tracking method overcomes the shortcomings of traditional resampling and FFT method. A new use based on the order tracking analysis is proposed in this paper, the computed order tracking method is applied to analyse the dynamic vibration signals during aeroengine start-stop process.In the aeroengine test, the fault characteristic of rotating shafts is related to the rotating speed,vibration will be more complicated and diversified with the increasing speed during run-up process, and has characteristics of periodicity and multiple frequency. It is of vital critical to extract characteristic information from exterior measurement vibration signal and get interior fault information. Applying the order tracking in the analysis of aeroengine signals shows that it can extract the fault characteristics and realize the state monitoring.

scholarly journals A combined generalized Warblet transform and second order synchroextracting transform for analyzing nonstationary signals of rotating machinery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kai Wei ◽  
Xuwen Jing ◽  
Bingqiang Li ◽  
Chao Kang ◽  
Zhenhuan Dou ◽  
...  
Keyword(s):  
Vibration Signal ◽  
Rotating Machinery ◽  
Second Order ◽  
Variable Speed ◽  
Short Time Fourier Transform ◽  
Stationary Characteristic ◽  
Nonstationary Signals ◽  
Time Frequency ◽  
Effective Technology ◽  
Short Time

AbstractIn recent years, considerable attention has been paid in time–frequency analysis (TFA) methods, which is an effective technology in processing the vibration signal of rotating machinery. However, TFA techniques are not sufficient to handle signals having a strong non-stationary characteristic. To overcome this drawback, taking short-time Fourier transform as a link, a TFA methods that using the generalized Warblet transform (GWT) in combination with the second order synchroextracting transform (SSET) is proposed in this study. Firstly, based on the GWT and SSET theories, this paper proposes a method combining the two TFA methods to improve the TFA concentration, named GWT–SSET. Secondly, the method is verified numerically with single-component and multi-component signals, respectively. Quantized indicators, Rényi entropy and mean relative error (MRE) are used to analyze the concentration of TFA and accuracy of instantly frequency (IF) estimation, respectively. Finally, the proposed method is applied to analyze nonstationary signals in variable speed. The numerical and experimental results illustrate the effectiveness of the GWT–SSET method.

scholarly journals A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0246905
Author(s):  
Chunming Wu ◽  
Zhou Zeng
Keyword(s):  
Fault Diagnosis ◽  
Noise Pollution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Generative Adversarial Network ◽  
High Background ◽  
Bearing Fault Diagnosis ◽  
Adversarial Network ◽  
Practical Engineering

Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating machinery. However, in practical engineering applications, the working conditions of rotating machinery are various, and it is difficult to extract the effective features of early fault due to the vibration signal accompanied by high background noise pollution, and there are only a small number of fault samples for fault diagnosis, which leads to the significant decline of diagnostic performance. In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis. Among them, during the process of training the ACGAN-SDAE, the generator and discriminator are alternately optimized through the adversarial learning mechanism, which makes the model have significant diagnostic accuracy and generalization ability. The experimental results show that our proposed ACGAN-SDAE can maintain a high diagnosis accuracy under small fault samples, and have the best adaptation performance across different load domains and better anti-noise performance.

scholarly journals Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zong Yuan ◽  
Taotao Zhou ◽  
Jie Liu ◽  
Changhe Zhang ◽  
Yong Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Ranking And Selection ◽  
Fault Features ◽  
Feature Importance ◽  
Importance Ranking ◽  
Common Signal ◽  
Diagnosis Approach

The key to fault diagnosis of rotating machinery is to extract fault features effectively and select the appropriate classification algorithm. As a common signal decomposition method, the effect of wavelet packet decomposition (WPD) largely depends on the applicability of the wavelet basis function (WBF). In this paper, a novel fault diagnosis approach for rotating machinery based on feature importance ranking and selection is proposed. Firstly, a two-step principle is proposed to select the most suitable WBF for the vibration signal, based on which an optimized WPD (OWPD) method is proposed to decompose the vibration signal and extract the fault information in the frequency domain. Secondly, FE is utilized to extract fault features of the decomposed subsignals of OWPD. Thirdly, the categorical boosting (CatBoost) algorithm is introduced to rank the fault features by a certain strategy, and the optimal feature set is further utilized to identify and diagnose the fault types. A hybrid dataset of bearing and rotor faults and an actual dataset of the one-stage reduction gearbox are utilized for experimental verification. Experimental results indicate that the proposed approach can achieve higher fault diagnosis accuracy using fewer features under complex working conditions.

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