The Research on Torsional Vibration Signal Processing and Automobile Transmission Shaft

2012 ◽  
pp. 641-647
Author(s):  
Liu Xiaoqun ◽  
Chen Hu
Keyword(s):  
Signal Processing ◽  
Torsional Vibration ◽  
Vibration Signal ◽  
Transmission Shaft

A Portable Digital Torsional Vibration Analysis System and its Signal Processing

2012 ◽  
Vol 490-495 ◽  
pp. 1903-1907
Author(s):  
Qi Lin ◽  
Shui Liang Yu
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Torsional Vibration ◽  
Internal Combustion Engines ◽  
Vibration Signal ◽  
Digital System ◽  
Rotation Rates ◽  
Analysis System ◽  
Spectrum Correction ◽  
Signal Processing Methods

Shaft torsional vibration is critical to rotating machinery as internal combustion engines because it may cause disasters if we ignore its significance. This paper introduced a portable digital system we developed to derive torsional vibration signal by a Hall Effect transducer. By analyzing the signal in frequency domain, we furthered the study on the influence of torsional vibration in each order under various rotation rates to determine the torsional resonant frequency. Moreover, a comparison between several signal processing methods in frequency domain was investigated and an optimum method for the spectrum correction obtained subsequently. Experiments conducted by this portable digital system showed its good performance in shaft torsional vibration measurements, analysis and trouble diagnosis.

Development of arbitrary waveform torsional vibration signal generator

2020 ◽  
Vol 75 (4) ◽  
pp. 425-435
Author(s):  
Qingjie Zhang ◽  
Guangxiang Lu ◽  
Chengyu Zhang ◽  
You Xu
Keyword(s):  
Torsional Vibration ◽  
Vibration Signal ◽  
Signal Generator ◽  
Arbitrary Waveform

Fault Detection of Broken Rotor Bar Using an Improved form of Hilbert–Huang Transform

2018 ◽  
Vol 17 (02) ◽  
pp. 1850012 ◽  
Author(s):  
F. Sabbaghian-Bidgoli ◽  
J. Poshtan
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Hilbert Transform ◽  
Wavelet Packet ◽  
Amplitude Spectrum ◽  
Vibration Signal ◽  
External Noise ◽  
Vibration Signals ◽  
Broken Rotor Bar ◽  
Hilbert Huang Transform

Signal processing is an integral part in signal-based fault diagnosis of rotary machinery. Signal processing converts the raw data into useful features to make the diagnostic operations. These features should be independent from the normal working conditions of the machine and the external noise. The extracted features should be sensitive only to faults in the machine. Therefore, applying more efficient processing techniques in order to achieve more useful features that bring faster and more accurate fault detection procedure has attracted the attention of researchers. This paper attempts to improve Hilbert–Huang transform (HHT) using wavelet packet transform (WPT) as a preprocessor instead of ensemble empirical mode decomposition (EEMD) to decompose the signal into narrow frequency bands and extract instantaneous frequency and compares the efficiency of the proposed method named “wavelet packet-based Hilbert transform (WPHT)” with the HHT in the extraction of broken rotor bar frequency components from vibration signals. These methods are tested on vibration signals of an electro-pump experimental setup. Moreover, this project applies wavelet packet de-noising to remove the noise of vibration signal before applying both methods mentioned and thereby achieves more useful features from vibration signals for the next stages of diagnosis procedure. The comparison of Hilbert transform amplitude spectrum and the values and numbers of detected instantaneous frequencies using HHT and WPHT techniques indicates the superiority of the WPHT technique to detect fault-related frequencies as an improved form of HHT.

Diagnosis for Engine Misfire Fault Based on Torsional Vibration and Neural-Network Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 7240-7246
Author(s):  
Can Yi Du ◽  
Kang Ding ◽  
Zhi Jian Yang ◽  
Cui Li Yang
Keyword(s):  
Neural Network ◽  
Torsional Vibration ◽  
Bp Neural Network ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Correction Method ◽  
Operating Conditions ◽  
Diagnostic Model ◽  
Vibration Signal Analysis

Misfire is a common fault which affects the engine performances. Because the signal-to-noise ratio of torsional vibration signal is high, torsional vibration test and analysis for the engine were performed in a variety of operating conditions, including healthy condition and single-cylinder misfire condition. In order to improve the accuracy of analysis, energy centrobaric correction method was used to correct the amplitude. Taking the corrected amplitude of main order as the fault feature, and then a BP neural-network diagnostic model can be established for misfire diagnosis. The result shows that the method of combining torsional vibration signal analysis and neural-network can diagnose engine misfire fault correctly.

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