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.

Vibration Measurement and Fault Diagnosis System for Experimental Teaching

2013 ◽  
Vol 401-403 ◽  
pp. 1226-1229
Author(s):  
Xiao Yan Yang ◽  
You Gang Xiao ◽  
Jian Feng Huang
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Virtual Instrument ◽  
Experimental System ◽  
Vibration Measurement ◽  
Diagnosis System ◽  
Time Frequency ◽  
Experimental Teaching ◽  
Equipment State ◽  
Signal Processing Methods

Based on LabVIEW, vibration measurement and diagnosis system of equipment was developed for experimental teaching. On the platform, vibration signals from running equipment can be sampled, displayed, stored, and analyzed in time domain, frequency domain and time-frequency domain. The advanced signal processing technology such as power spectrum analysis, cepstrum analysis, demodulation analysis can also be executed. Using pattern recognition technology, the processed signals can be integrated for intelligent diagnosis of equipment state. The experimental system is helpful for students to learn signal processing methods, and to design virtual instrument.

scholarly journals Application of Vibration Signal Processing Methods to Detect and Diagnose Wheel Flats in Railway Vehicles

2021 ◽  
Vol 11 (5) ◽  
pp. 2151
Author(s):  
JaeSeok Shim ◽  
GeoYoung Kim ◽  
ByungJin Cho ◽  
JeongSeo Koo
Keyword(s):  
Signal Processing ◽  
Cross Correlation ◽  
Vibration Signal ◽  
Analysis Method ◽  
Order Analysis ◽  
Cepstrum Analysis ◽  
Cross Correlation Analysis ◽  
Train Speed ◽  
Wheel Flat ◽  
Signal Processing Methods

This paper studied two useful vibration signal processing methods for detection and diagnosis of wheel flats. First, the cepstrum analysis method combined with order analysis was applied to the vibration signal to detect periodic responses in the spectrum for a rotating body such as a wheel. In the case of railway vehicles, changes in speed occur while driving. Thus, it is difficult to effectively evaluate the flat signal of the wheel because the time cycle of the flat signal changes frequently. Thus, the order analysis was combined with the existing cepstrum analysis method to consider the changes in train speed. The order analysis changes the domain of the vibration signal from time domain to rotating angular domain to consider the train speed change in the cepstrum analysis. Second, the cross correlation analysis method combined with the order analysis was applied to evaluate the flat signal from the vibration signal well containing the severe field noise produced by the vibrations of the rail irregularities and bogie components. Unlike the cepstrum analysis method, it can find out the wheel flat size because the flat signal linearly increases to the wheel flat. Thus, it is more effective when checking the size of the wheel flat. Finally, the data tested in the Korea Railroad Research Institute were used to confirm that the cepstrum analysis and cross correlation analysis methods are appropriate for not only simulation but also test data.

scholarly journals The Use of Multicriteria Inference Method to Identify and Classify Selected Combustion Engine Malfunctions Based on Vehicle Structure Vibrations

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2470
Author(s):  
Krzysztof Prażnowski ◽  
Andrzej Bieniek ◽  
Jarosław Mamala ◽  
Adam Deptuła
Keyword(s):  
Internal Combustion Engine ◽  
Frequency Domain ◽  
Internal Combustion Engines ◽  
Combustion Engine ◽  
Vibration Signal ◽  
Internal Combustion ◽  
Technical Condition ◽  
Engine Block ◽  
Injection System ◽  
Combustion Engines

Internal combustion engines are among the most commonly used propulsion units for drive systems in various industries such as land transportation, maritime transportation, and power generation. Their operation involves a continuous change of technical condition as a result of not only the combustion process but also their operation under conditions of variable load or ambient impact. It is therefore important to monitor the technical condition of internal combustion engines to ensure high performance and reliability over their lifetime. The article presents the test results obtained from incorrect operation of an internal combustion engine as a result of forced failures of the ignition and injection system. On this basis, a multicriteria comparison of the signal analysis of engine block vibrations was made, after the transformation of the signal from the time domain to the frequency domain, by using the induction technique obtained from the operation of decision tree algorithms. For this purpose, the amplitude spectrum in the frequency domain, scaled to absolute values of discretization for which teaching and testing data tables were created for successive harmonics, was determined for the engine block vibration signal being tested. On the basis of the developed algorithm using decision trees, a multicriteria data table was analyzed for which a compatibility path for the analyzed engine block vibration signal is created. In this way, it is confirmed with a specified degree of effectiveness, depending on the point of operation of the engine resulting from its crankshaft speed, that there is a possibility of detecting a preset ignition or injection system malfunction in the technical condition of the internal combustion engine with a probability up to about 72%.

Identification of tooth fault in a gearbox based on cyclostationarity and empirical mode decomposition

2017 ◽  
Vol 17 (3) ◽  
pp. 494-513 ◽  
Author(s):  
Jong-Sik Kim ◽  
Sang-Kwon Lee
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Health Monitoring ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Simulated Signal ◽  
Mode Decomposition ◽  
Signal Processing Methods ◽  
The Impact

In the previous work, the cyclostationarity process, which is one of signal processing methods, has been used in health monitoring of the rotating machinery because of the superior detecting property of hidden periodicity. However, it is often difficult to acquire the information about the hidden periodicity due to the fault of the rotating machinery when the impact signal is low. Therefore, a certain preprocessing tool to extract the information about the impact signal due to the fault is required. This article presents the new detection process of tooth faults in a gearbox system based on the empirical mode decomposition algorithm which adaptively decomposes the signal into a set of intrinsic mode functions and the cyclostationarity process which identifies the hidden periodicity clearly in bi-frequency domain. The proposed method was demonstrated with a simulated signal and was applied to the detection of four types of conditions of tooth fault successfully.

Application of Mathematical Morphological Filter in Vibration Signal Processing

2011 ◽  
Vol 71-78 ◽  
pp. 4564-4567
Author(s):  
Ai Jun Hu ◽  
Jing Jing Sun ◽  
Wan Li Ma
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Time Domain ◽  
Nonlinear Filtering ◽  
Vibration Signal ◽  
Morphological Operations ◽  
Morphological Filter ◽  
Original Signal ◽  
Low Pass ◽  
The Time Domain

The morphological filter as a nonlinear filtering method has been widely used for image (or signal) processing. Unlike the traditional digital filters, mathematical morphological operations are shape-based computing. Feature extraction of signals is entirely in the time domain without the transforming of the signal from the time domain to frequency domain. The vibration signal contaminated with noise is processed using morphological filter and Butterworth filter respectively. To compare the outputs of the two filters, we find that morphological filter shows better performance. It is effective in suppressing noise while maintaining the original signal both in the time and frequency domain. In addition, an outstanding advantage of morphological filter is its ability to keep the phase of the original signal. Its computing speed is faster. In the end, its low-pass characteristic is verified by processing vibration signal.

Sign in / Sign up

Export Citation Format

Share Document