Fault detection in reciprocating compressor valves under varying load conditions. (March 2016)

• Record Type: Journal Article
• Authors: Pichler, Kurt; Lughofer, Edwin; Pichler, Markus; Buchegger, Thomas; Klement, Erich Peter; Huschenbett, Matthias
• Is Part Of: Mechanical systems and signal processing. Volume 70/71(2016)
• Page Start: 104
• Page End: 119
• Publication Date: 2016-03
• Subjects: Fault detection -- Compressor valves -- Varying load conditions -- Time–frequency analysis -- Feature extraction -- Feature engineering; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.09.005
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: This paper presents a novel approach for detecting cracked or broken reciprocating compressor valves under varying load conditions. The main idea is that the time frequency representation of vibration measurement data will show typical patterns depending on the fault state. The problem is to detect these patterns reliably. For the detection task, we make a detour via the two dimensional autocorrelation. The autocorrelation emphasizes the patterns and reduces noise effects. This makes it easier to define appropriate features. After feature extraction, classification is done using logistic regression and support vector machines. The method׳s performance is validated by analyzing real world measurement data. The results will show a very high detection accuracy while keeping the false alarm rates at a very low level for different compressor loads, thus achieving a load-independent method. The proposed approach is, to our best knowledge, the first automated method for reciprocating compressor valve fault detection that can handle varying load conditions. Abstract : Highlights: Load changes and valve faults show different patterns in the spectrogram. Autocorrelation of the spectrogram difference emphasizes these patterns. Distinctive features can be extracted from autocorrelation representation. Classification works well for arbitrary load levels of the compressor.