Electrical and mechanical data fusion for hydraulic valve leakage diagnosis. (1st April 2023)

• Record Type: Journal Article
• Authors: Conti, Fabio; Madeo, Federica; Boiano, Antonio; Tarabini, Marco
• Is Part Of: Measurement science & technology. Volume 34:Number 4(2023)
• Publication Date: 2023-04-01
• Subjects: data fusion -- vibration measurement -- current measurement -- predictive maintenance -- industry 4.0 -- fault detection; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/acb376
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: This paper describes a method for the identification of valves' failure identification, with the final aim of creating a predictive maintenance architecture. After revising the scientific literature, we selected the electric current, the acoustic emission and the vibration signals as the most promising monitoring techniques. The processes of feature extraction and data fusion have been optimized to detect early symptoms of a failure. Performances of five different machine learning algorithms have been compared. Results, obtained in a specific case study, evidenced that a data fusion process based on vibration and current data, paired with a random forest model allowed a prediction accuracy and a Jaccard index close to 99%.