Impedance‐based looseness detection of bolted joints using artificial neural network: An experimental study. Issue 10 (22nd July 2022)

• Record Type: Journal Article
• Authors: Meher, Umakanta; Mishra, Sudhanshu Kumar; Sunny, Mohammed Rabius
• Is Part Of: Structural control and health monitoring. Volume 29:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-22
• Subjects: artificial neural network (ANN) -- correlation coefficient (CC) -- electro‐mechanical impedance (EMI) -- lead zirconate titanate (PZT) -- root mean square deviation (RMSD) -- structural health monitoring (SHM); Structural engineering -- Periodicals; Structural control (Engineering) -- Periodicals; Automatic data collection systems -- Periodicals; Detectors -- Periodicals; 624.17
• DOI: 10.1002/stc.3049
• Languages: English
• ISSNs: 1545-2255

Abstract

Summary: A detection technique to quantify the degree of bolt looseness in metallic bolted structure using electro‐mechanical impedance signatures is proposed. A bolted joint connection of two steel plates and a stiffener is taken as the specimen to be monitored. Loosening of the bolted joints is considered as the damage present in the structure. At first, the electro‐mechanical responses at two piezoelectric transducer locations are measured experimentally for the undamaged and damaged state of the structure. Damage scenarios with single as well as multiple degrees of bolt looseness are considered. Damage features based on root mean square deviation (RMSD) and correlation coefficient (CC) of conductance with respect to the healthy state conductance are extracted. A single hidden layer backpropagation artificial neural network has been trained for detection of bolt looseness from the damage features. Acceptability of the proposed multiple damage detection technique has been observed through few test cases.