Corrosion of metal structures is often prevented using cathodic protection systems, that employ sacrificial anodes that corrode more preferentially relative to the metal to be protected. In-situ monitoring of these sacrificial anodes during early stages of their useful life could offer several insights into deterioration of the material surrounding the infrastructure as well as serve as early warning indicator for preventive maintenance of critical infrastructure. In this article, we present an Electro-Mechanical Impedance (EMI) measurement-based technique to quantify extent of corrosion of a zinc sacrificial anode without manual intervention. The detection apparatus consists of a lead zirconate titanate (PZT) transducer affixed onto a circular zinc disc, with waterproofing epoxy protecting the transducer element when the assembly is submerged in liquid electrolyte (salt solution) for accelerated corrosion by means of impressed current. We develop an analytical model for discerning the extent of corrosion by monitoring shift in resonance frequency for in-plane radial expansion mode of the disc. The model presented here accurately captures the nonlinearity introduced by partial delamination of the corrosion product (zinc oxide) from the disc, and shows excellent agreement with experimental results. Our work establishes the efficacy of the proposed technique for monitoring the state of health of sacrificial anodes in their early stage of deterioration and could thus be widely adopted for structural health monitoring applications within the internet of things. INDEX TERMS Cathodic protection system, corrosion monitoring, electro-mechanical impedance (EMI), sacrificial anode, smart infrastructure, structural health monitoring.
Conventional damage localisation algorithms used in ultrasonic guided wave-based structural health monitoring (GW-SHM) rely on physics-defined features of GW signals. In addition to requiring domain knowledge of the interaction of various GW modes with various types of damages, they also suffer from errors due to variations in environmental and operating conditions (EOCs) in practical use cases. While several machine learning tools have been reported for EOC compensation, they need to be custom-designed for each combination of damage and structure due to their dependence on physics-defined feature extraction. In this work, we propose a CNN-based automated feature extraction framework coupled with Gaussian mixture model (GMM) based temperature compensation and damage classification and localisation method. Features learnt by the CNNs are used for damage classification and localisation of damage by modelling the probability distribution of the features using GMMs. The Kullback-Leibler (KL) divergence of these GMMs with respect to corresponding baseline GMMs are used as signal difference coefficients (SDCs) to compute damage indices (DIs) along various GW sensor paths, and thus for damage localisation. The efficacy of the proposed method is demonstrated using FE generated GW-data for an aluminium plate with a network of six lead zirconate titanate (PZT) sensors, for three different types of damages (rivet hole, added mass, notch) at various temperatures (from 0°C to 100°C), with added white noise and pink noise to incorporate errors due to EOCs. We also present experimental validation of the method through characterisation of notch damage in an aluminium panel under varying and non-uniform temperature profiles, using a portable custom-designed field programmable gate array (FPGA) based signal transduction and data acquisition system. We demonstrate that the method outperforms conventional temperature compensation method using GMM with physics-defined features for damage localisation in GW-SHM systems prone to EOC variations.
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