Precise order tracking analysis of time-varying vibro-acoustic signature from rotating machines

2013 ◽  
Author(s):  
Jin-Ho Bae ◽  
Jeong-Guon Ih ◽  
Sang-Ryeol Kim
Keyword(s):  
Time Varying ◽  
Rotating Machines ◽  
Order Tracking ◽  
Acoustic Signature

Precise order tracking analysis of time-varying vibro-acoustic signature from rotating machines

2013 ◽  
Vol 133 (5) ◽  
pp. 3437-3437
Author(s):  
Jin-Ho Bae ◽  
Jeong-Guon Ih ◽  
Sang-Ryeol Kim
Keyword(s):  
Time Varying ◽  
Rotating Machines ◽  
Order Tracking ◽  
Acoustic Signature

scholarly journals Asymptotically Convergent Higher-Order Switching Differentiator

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 185
Author(s):  
Jang-Hyun Park ◽  
Tae-Sik Park ◽  
Seong-Hwan Kim
Keyword(s):  
Time Derivative ◽  
Higher Order ◽  
Time Varying ◽  
Tracking Errors ◽  
Order Tracking ◽  
Barbalat’S Lemma ◽  
Derivatives Of

A novel switching-differentiator (SD) that can asymptotically track the time derivative of time-varying signal was previously proposed. This paper extends the previous SD to estimation of higher-order time derivatives. This study shows that higher-order time-derivatives can be estimated by connecting multiple SDs in cascade form. By successive applying the generalized Barbalat’s lemma, all higher-order tracking errors also approach zeros asymptotically. To illustrate the performance of the proposed higher-order switching differentiator, simulations were performed for estimating higher-order time-derivatives of a signal.

scholarly journals Fault diagnosis of rotating machinery under time-varying speed based on order tracking and deep learning

2020 ◽  
Vol 22 (2) ◽  
pp. 366-382 ◽  
Author(s):  
Peng Wang ◽  
Taiyong Wang ◽  
Lan Zhang ◽  
Huihui Qiao
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Time Varying ◽  
Order Tracking

scholarly journals The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions

2017 ◽  
Vol 32 (1) ◽  
pp. 244-256 ◽  
Author(s):  
Angel Sapena-Bano ◽  
Jordi Burriel-Valencia ◽  
Manuel Pineda-Sanchez ◽  
Ruben Puche-Panadero ◽  
Martin Riera-Guasp
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Time Varying ◽  
Harmonic Order ◽  
Analysis Method ◽  
Order Tracking

scholarly journals Tacholess order tracking method for gearbox based on time-varying filter and energy centrobaric correction method

2019 ◽  
Vol 21 (5) ◽  
pp. 1282-1295 ◽  
Author(s):  
Zhipeng Luo ◽  
Shuangxi Jing ◽  
Junfa Leng ◽  
Zhiyang Wang
Keyword(s):  
Correction Method ◽  
Time Varying ◽  
Tracking Method ◽  
Order Tracking

Algorithm for Multiple Time-Frequency Curve Extraction From Time-Frequency Representation of Vibration Signals for Bearing Fault Diagnosis Under Time-Varying Speed Conditions

2017 ◽  
Author(s):  
Huan Huang ◽  
Natalie Baddour ◽  
Ming Liang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Time Varying ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Order Tracking ◽  
Frequency Representation ◽  
Bearing Vibration ◽  
Fault Characteristic

Bearing fault diagnosis under constant operational condition has been widely investigated. Monitoring the bearing vibration signal in the frequency domain is an effective approach to diagnose a bearing fault since each fault type has a specific Fault Characteristic Frequency (FCF). However, in real applications, bearings are often running under time-varying speed conditions which makes the signal non-stationary and the FCF time-varying. Order tracking is a commonly used method to resample the non-stationary signal to a stationary signal. However, the accuracy of order tracking is affected by many factors such as the precision of the measured shaft rotating speed and the interpolation methods used. Therefore, resampling-free methods are of interest for bearing fault diagnosis under time-varying speed conditions. With the development of Time-Frequency Representation (TFR) techniques, such as the Short-Time Fourier Transform (STFT) and wavelet transform, bearing fault characteristics can be shown in the time-frequency domain. However, for bearing fault diagnosis, instantaneous time-frequency characteristics, i.e. Time-Frequency (T-F) curves, have to be extracted from the TFR. In this paper, an algorithm for multiple T-F curve extraction is proposed based on a path-optimization approach to extract T-F curves from the TFR of the bearing vibration signal. The bearing fault can be diagnosed by matching the curves to the Instantaneous Fault Characteristic Frequency (IFCF) and its harmonics. The effectiveness of the proposed algorithm is validated by experimental data collected from a faulty bearing with an outer race fault and a faulty bearing with an inner race fault, respectively.

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