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Prologue: Predictive Maintenance in Dynamic Systems Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Early Fault Detection in Reciprocating Compressor Valves by Means of Vibration and pV Diagram Analysis

Author : Kurt Pichler

Published in: Predictive Maintenance in Dynamic Systems

Publisher: Springer International Publishing

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Abstract

Leaking valves are the most common reason for unexpected shutdowns of reciprocating compressors. Therefore, a leaking valve has to be detected early and reliable, even for arbitrary operating conditions. In this chapter, a data-driven approach for compressor valve monitoring is proposed. As compressors are equipped with different sensing systems and usually retrofitting new sensors is not desired or even impossible, two independent methods are developed. In the first approach, accelerometers are mounted at the valve covers to perform vibration analysis. In the case of a broken valve, certain time–frequency patterns occur, different from the patterns in the case of varying operating condition. It is thus possible to extract specific features from the time–frequency representation to distinguish between healthy and broken valves. In the second approach, pV diagrams of compression cycles are analysed. Gas flowing through a leak affects the pressure in the compression cylinder and thus the pV diagram. The pV diagram is also affected by varying operating conditions such as load, suction, and discharge pressure. Appropriate features to distinguish these cases are extracted both from the logarithmic pV diagram and the environmental pressure conditions.

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previous chapter Online Time Series Changes Detection Based on Neuro-Fuzzy Approach
next chapter A New Hilbert-Huang Transform Technique for Fault Detection in Rolling Element Bearings
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Metadata
Title
Early Fault Detection in Reciprocating Compressor Valves by Means of Vibration and pV Diagram Analysis
Author
Kurt Pichler
Copyright Year
2019
Book
Predictive Maintenance in Dynamic Systems

Print ISBN: 978-3-030-05644-5

Electronic ISBN: 978-3-030-05645-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-05645-2_6