Track Condition Monitoring Based on Car-Body Acceleration Using Time-Frequency Analysis

2020 ◽  
pp. 103-112
Author(s):  
Hitoshi Tsunashima
Keyword(s):  
Condition Monitoring ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Body Acceleration ◽  
Time Frequency ◽  
Car Body ◽  
Track Condition

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

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.

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