Monitoring Of Pitting Corrosion Research Articles

An electromechanical impedance-based sensor for monitoring the pitting corrosion of steel: Simulation with experimental validation

Quantitative monitoring of steel corrosion plays a crucial role in evaluating structural safety and predicting structural performance. The electromechanical impedance (EMI) technique has already emerged for uniform corrosion monitoring. This study explores its potential for pitting corrosion monitoring. Specifically, two EMI-based sensor designs were proposed, namely the cylinder and the tube structures. Finite element models were developed to analyze the influence of the pit’s location, morphology parameters, as well as the random distribution of multiple pits, on the conductance peak frequency of sensors. The computer numerical controlled processes were employed to create random pits on sensors. Experimental results revealed that different distributions of pits correspond to distinct conductance peak frequencies, even with the same mass loss. Additionally, the tubular sensor exhibited reduced sensitivity to pits distribution compared to the cylindrical sensor. This is beneficial for accurately predicting mass loss induced by random pitting. This paper enhances the capability of EMI-based sensors for quantitatively monitoring pitting corrosion by focusing on structural design aspects.

Application of Sensor Path Weighting RAPID Algorithm on Pitting Corrosion Monitoring of Aluminum Plate.

Aluminum alloy is widely used in aerospace structures. However, it often suffers from a harsh corrosion environment, resulting in different damage such as pitting corrosion, which leads to a reduction in the service life of aerospace structures. In the present study, the pitting corrosion with a radius of 1 mm and a depth of 0.6 mm was manufactured using hydrofluoric (HF) acid on a 2024-T3 aluminum alloy plate (400 mm × 400 mm × 2 mm) to simulate the corrosion state of equipment. A signal acquisition system with a square sensor network of 12 piezoelectric transducers (PZTs) was established. The sensor path weighting reconstruction algorithm for the probabilistic inspection of defects (SPW-RAPID) is proposed based on corrosion damage characteristic parameters including signal correlation coefficient (SDC), root mean squared error (RMSE), and signal energy damage index (E1) to explore the monitoring efficacy of pitting corrosion. The sensor path weight w, which is the product of value coefficient a and impact factor l, is established to modify the corrosion damage characteristic parameters. The results indicate that the SPW-RAPID algorithm can improve the accuracy and clarity of image reconstruction results based on SDC, RMSE and E1, which can locate the pitting corrosion with a radius of 1 mm and a depth of 0.6 mm, and the positioning error is controlled within 0.1 mm. The research work may provide an available way to monitor tiny corrosion damage on an aluminum alloy structure.

A novel method for steel bar all-stage pitting corrosion monitoring using the feature-level fusion of ultrasonic direct waves and coda waves

Corrosion monitoring of steel bars has drawn extensive attention in recent decades. Conventional ultrasonic method, utilizing direct waves to detect damage, is adequate for severe pitting corrosion but suffers from low sensitivity to incipient pitting corrosion. Coda wave technique, a very sensitive method to subtle changes in medium using later arrival wave packets, is innovatively introduced to monitor pitting corrosion of steel bars, especially in the early stages. The decorrelation coefficient (DC) values are calculated to quantify the variations of both direct waves and coda waves. To overcome the limitations of coda waves for severe pitting corrosion and remedy the low sensitivity of direct waves for incipient pitting corrosion, a feature-level data fusion strategy is proposed to integrate the two probing waves to monitor all-stage pitting corrosion of steel bars. The combination of direct waves and coda waves could exploit the complementary merits in various pitting corrosion configurations. The proposed feature-level fusion strategy of ultrasonic coda waves and direct waves intercepted from the same recorded signals opens a new perspective in all-stage pitting corrosion monitoring of steel bars and contributes a novel scheme for whole-process damage evaluation of structures.

Development of Pitting Corrosion Monitoring Device under Sodium Chloride Droplet by Means of Wet-Dry Cyclic Method

Pitting corrosion caused by wet-dry cycles under corrosive media droplet is one of the key concerns for passive film of metallic materials, particularly stainless steels and aluminum alloys, exposed to atmosphere during service. In this context, the formation of corrosion can lead to high investment cost dealing with corrosion mitigation strategy, e.g. materials selection, electrochemical corrosion control, etc. Based on materials selection perspectives, it is very necessary to have proper understanding of localized corrosion behaviors of metallic materials under solution droplet. Therefore, the present study aims to develop a methodology for pitting corrosion monitoring that can be suitably used for extending better understanding on corrosion phenomena occurring under wet-dry cycles of droplet. A special liquid handling apparatus controlled by Arduino software was constructed and used for generating NaCl solution droplet at given dimension on the surface of stainless steel specimens based on Pendant drop principle. This was recognized as wet cycle. During dry cycle, such NaCl solution droplet was naturally dried off in various conditions of relative humidity. Pitting initiation was observed through a high-resolution CCD camera. Droplet morphology and evaporation time were evaluated at the temperature of 27°C and relative humidity of 10% and 60%. The research results revealed that pitting corrosion started at 1st cycle without rust formation. Afterwards, the rust formation was clearly noticed when testing cycles of 15 were exceeded.

Investigation of pitting corrosion monitoring using field signature method

The field signature method (FSM) is a nondestructive testing (NDT) method based on the potential drop (PD) technique and has been applied to online metal pipe corrosion monitoring for nearly three decades. The many advantages and benefits of the method have been reported in a number of studies, but few have reported on its limitations or shortcomings. However, the detection accuracy for pitting corrosion in FSM is very low. In this paper, the reasons for the low pitting corrosion detection accuracy of FSM were analyzed and it was found that different corrosion pits, which have different sizes, depths or positions, generally have differing influences on the potentials of nearby electrode pairs. Therefore, a new method using a subdivided resistor network to assess pitting corrosion is proposed and verified. When compared with the traditional method, the most important parameter, namely the pitting corrosion depth detection accuracy, can be significantly improved.

Quantitative monitoring of pitting corrosion based on 3-D cellular automata and real-time ENA for RC structures

A quantitative 3-D cellular automata (CA) prediction algorithm based on real-time electrochemical noise analysis (ENA) has been established for the pitting corrosion monitoring of reinforced concrete (RC) structures. The energy distribution ratio of wavelet energy spectroscopy (WES) has been used to drive a 3-D CA model to qualitatively predict the development of the corrosion pit. The EN characteristics of Q235 carbon steel in simulated concrete pore solutions (SCPS) have been investigated. Finally, the feasibility of the 3-D CA model (driven by the real-time ENA) has been verified by 3-D Ultra-depth Video Microscope experiments.