Sparse estimation based monitoring method for damage detection and localization: A case of study. (November 2018)

• Record Type: Journal Article
• Authors: El Mountassir, Mahjoub; Yaacoubi, Slah; Mourot, Gilles; Maquin, Didier
• Is Part Of: Mechanical systems and signal processing. Volume 112(2018)
• Page Start: 61
• Page End: 76
• Publication Date: 2018-11
• Subjects: Structural health monitoring -- Sparse estimation -- Damage detection -- Damage localization -- Environmental and operational conditions; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2018.04.024
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A method to overcome the effect of the environmental and operational changes on the structural health monitoring sensitivity. Detection of small corrosion like damage in a very attenuating medium. A novel approach based on sparse estimation. A method for an efficient real word application. Abstract: This paper suggests a Structural Health Monitoring (SHM) method for damage detection and localization in pipeline. The baseline signals, used in SHM, could change due to the variation of environmental and operational conditions (EOCs). Hence, the damage detection method could give rise to false alarm. In this study, this issue is addressed by estimating the current signal using only few reference signals with similar or very close EOCs. Such an operation can be performed by calculating a sparse estimation of the current signal. The estimation error is used as an indication of the presence of damage. Actually, a signal from the damaged pipe will be characterized by a high estimation error compared to that of a signal from the undamaged pipe. The damage location is obtained by calculating the estimation error on a sliding window over the signal from the damaged pipe. This method was tested on signals collected on a 6 m pipeline segment placed in a workshop under natural temperature variations. Results have shown that the created damage was successfully detected and localized.