Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions. (January 2016)

• Record Type: Journal Article
• Authors: Ruiz-Cárcel, C.; Jaramillo, V.H.; Mba, D.; Ottewill, J.R.; Cao, Y.
• Is Part Of: Mechanical systems and signal processing. Volume 66/67(2016)
• Page Start: 699
• Page End: 714
• Publication Date: 2016-01
• Subjects: Condition monitoring -- Non-stationary operation -- Compressor -- Vibration -- Canonical Variate Analysis; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.05.018
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: The detection and diagnosis of faults in industrial processes is a very active field of research due to the reduction in maintenance costs achieved by the implementation of process monitoring algorithms such as Principal Component Analysis, Partial Least Squares or more recently Canonical Variate Analysis (CVA). Typically the condition of rotating machinery is monitored separately using vibration analysis or other specific techniques. Conventional vibration-based condition monitoring techniques are based on the tracking of key features observed in the measured signal. Typically steady-state loading conditions are required to ensure consistency between measurements. In this paper, a technique based on merging process and vibration data is proposed with the objective of improving the detection of mechanical faults in industrial systems working under variable operating conditions. The capabilities of CVA for detection and diagnosis of faults were tested using experimental data acquired from a compressor test rig where different process faults were introduced. Results suggest that the combination of process and vibration data can effectively improve the detectability of mechanical faults in systems working under variable operating conditions. Highlights: Data was acquired from a compressor test rig where faults were seeded. Vibration signature of mechanical faults was simulated. Process and vibration measurements were merged. The fault detection trough Canonical Variate Analysis was improved.