Condition Monitoring and Diagnostics in Heavy-Duty Wheels: A First Experimental Approach

2009 ◽  
Author(s):  
Marco Malago` ◽  
Emiliano Mucchi ◽  
Giorgio Dalpiaz
Keyword(s):  
Condition Monitoring ◽  
Frequency Analysis ◽  
Experimental Approach ◽  
Heavy Duty ◽  
Time Frequency ◽  
Acoustic Data ◽  
Analysis Techniques ◽  
Set Up ◽  
Monitoring And Diagnostics ◽  
Systematic Control

This paper assesses and compares the effectiveness of different analysis techniques for fault detection and diagnostics in heavy-duty wheels by using vibro-acoustic data. Firstly, different defect types have been artificially created on the wheels, trying to replicate anomalies that could really happen within the manufacturing process. Hence, different sensors and test conditions have been tested in order to determine the set up that at the best highlights the anomalies of the wheels; moreover the Time Synchronous Average (TSA) has been computed to reduce measurement noise. Kurtosis statistical coefficient has been used to detect defect presence (condition monitoring step), whereas frequency analysis, time-frequency analysis and signal trend have been performed for identifying the type of defect (diagnosis step). Finally, the effectiveness and the limitations of the above-mentioned techniques and diagnostics procedures are compared and discussed in order to define a systematic control at the end of the production line.

scholarly journals Frequency and Time-Frequency Analysis of Cutting Force and Vibration Signals for Tool Condition Monitoring

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 6400-6410 ◽  
Author(s):  
Juan C. Jauregui ◽  
Juvenal R. Resendiz ◽  
Suresh Thenozhi ◽  
Tibor Szalay ◽  
Adam Jacso ◽  
...  
Keyword(s):  
Cutting Force ◽  
Condition Monitoring ◽  
Frequency Analysis ◽  
Tool Condition Monitoring ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Tool Condition

Overview of Time-Frequency Analysis Techniques in Vibration Signals of Rotating Machinery

2014 ◽  
Vol 684 ◽  
pp. 124-130
Author(s):  
Hong Li ◽  
Qing He ◽  
Zhao Zhang
Keyword(s):  
Frequency Analysis ◽  
Local Characteristic ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Analysis Techniques ◽  
Advantages And Disadvantages ◽  
Analysis Methods ◽  
Short Time

There is very rich fault information in vibration signals of rotating machineries. The real vibration signals are nonlinear, non-stationary and time-varying signals mixed with many other factors. It is very useful for fault diagnosis to extract fault features by using time-frequency analysis techniques. Recent researches of time-frequency analysis methods including Short Time Fourier Transform, Wavelet Transform, Wigner-Ville Distribution, Hilbert-Huang Transform, Local Mean Decomposition, and Local Characteristic-scale Decomposition are introduced. The theories, properties, physical significance and applications, advantages and disadvantages of these methods are analyzed and compared. It is pointed that algorithms improvement and combined applications of time-frequency analysis methods should be researched in the future.

scholarly journals application based comparison of statistical versus deep learning approaches to reciprocating compressor valve condition monitoring

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jason Kolodziej ◽  
Jacob Chesnes
Keyword(s):  
Deep Learning ◽  
Condition Monitoring ◽  
Frequency Analysis ◽  
Reciprocating Compressor ◽  
Learning Approaches ◽  
Early Assessment ◽  
Valve Seat ◽  
Repeated Impact ◽  
Time Frequency Analysis ◽  
Time Frequency

This paper presents a vibration-based condition monitoring approach for early assessment of valve wear in an industrial reciprocating compressor. Valve seat  wear is a common fault mode that is caused by repeated impact and accelerated by chatter. Seeded faults consistent with valve seat wear are installed on the head-side discharge valves of a Dresser-Rand ESH-1 industrial reciprocating compressor. Due to the cyclostationary nature of these units a time-frequency analysis is employed where targeted crank angle positions can isolate externally mounted, non-invasive, vibration measurements. A region-of-interest (ROI) is then extracted from the time-frequency analysis and used to train a suitably sized convolutional neural network (CNN). The proposed deep learning method is then compared against a similarly trained discriminant classifier using the same ROIs where features are extracted using texture and shape image statistics. Both methods achieve > 90% success with the CNN classification strategy nearing a perfect result.

scholarly journals Signal-based diagnostics of the gasoline injection engine

2021 ◽  
Vol 9 (2) ◽  
pp. 083-091
Author(s):  
Marius Singureanu ◽  
Ion Copae
Keyword(s):  
Frequency Analysis ◽  
Experimental Basis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Techniques

The paper addresses one of the methods of diagnosing the gasoline injection engine available on the vehicle, namely signal-based diagnostics. The most used algorithms for signal-based diagnostics are highlighted. The possibilities offered on an experimental basis are presented and the tests performed in this respect are presented. Time-frequency analysis techniques are applied to detect defects caused in the case of the Audi A6 car engine.

Detection and Parameter Extraction of Low Probability of Intercept Frequency Hopping Signals using the Spectrogram and the Reassigned Spectrogram

2020 ◽  
pp. 11-20
Author(s):  
Daniel L. Stevens
Keyword(s):  
Frequency Analysis ◽  
Parameter Extraction ◽  
Frequency Hopping ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Techniques ◽  
Time Frequency Localization ◽  
Low Probability ◽  
Low Probability Of Intercept ◽  
Radar Signals

Low probability of intercept radar signals, which are often problematic to detect and characterize, have as their goal ‘to see and not be seen’. Digital intercept receivers are currently moving away from Fourier-based analysis and towards classical time-frequency analysis techniques for the purpose of analyzing these low probability of intercept radar signals. Although these classical time-frequency analysis techniques are an improvement over existing Fourier-based techniques, they still suffer from a lack of readability –which can be caused by poor time-frequency localization (such as the spectrogram), which may in turn lead to inaccurate detection and parameter extraction. In this study, the reassignment method, because of its ability to improve time-frequency localization, is proposed as an improved signal analysis technique to address the poor time-frequency localization deficiency of the spectrogram. This paper presents the novel approach of characterizing low probability of intercept frequency hopping radar signals through utilization and direct comparison of the spectrogram versus the reassigned spectrogram.

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