This study presents the development of a remote acoustic emission （AE） monitoring system for structural health monitoring that integrates a drone platform with a dry couplant–based contact measurement technique. The system incorporates a high-accuracy noise reduction algorithm combining spectral subtraction and a band-pass filter to reduce the continuous noise generated during drone flight. To ensure stable coupling between the AE sensor and the structure, three types of dry couplants were evaluated. Experiments using artificial AE signals demonstrated that the gel-type couplant provided the highest signal-to-noise ratio and the most stable measurements, owing to its thickness and ability to accommodate drone-induced background noise. The proposed system was further validated through accelerated corrosion tests on steel plates. AE signals generated by rust cracking were successfully detected using both fixed sensors and the drone-mounted sensor. It was confirmed that the arrival times of AE events detected by the drone-mounted sensor were the same as those measured by the sensor directly attached to the test steel-plate. The cumulative number of AE events detected by the former sensor agreed well with that detected by the latter sensor at the level of about 64 ％. These results demonstrate that the developed system enables remote monitoring of corrosion-related AE activity with practical accuracy.

A classification method for localized corrosion was developed using potential monitoring with pipe-type electrode with residual stress, which can be installed in salt manufacturing plants. Corrosion tests and potential monitoring using pipe-type electrode with tensile residual stress exceeding 200 MPa were conducted. SUS316L in 10 ％ NaCl solution exhibited metastable pitting corrosion characterized by RD-type potential noise, corresponding to passive film breakdown and repassivation. SUS316L in 27 ％ MgCl₂ solution exhibited stable pitting corrosion. SUS312L in 10 ％ NaCl solution showed no corrosion. An algorithm was developed to detect the occurrence frequency and amplitude of potential noise for each corrosion type for automatic long-term monitoring. Almost no noise was observed under no corrosion. Potential noise of high frequency （5–8.5 h^－1） with low amplitude （＜1 mV） was characteristic of stable pitting corrosion. Potential noise of frequency （2–6 h^－1） with high amplitude （＞4 mV） was characteristic of metastable pitting corrosion. The optimal sampling window size was determined as 2 h or longer based on the coefficient of variation. Three corrosion morphologies were classified by two parameters, which were frequency and average amplitude of potential noise. This method could be applied to corrosion monitoring in salt manufacturing plants.