Reinforced concrete poles are very popular in transmission lines due to their economic efficiency. However, these poles have structural safety issues in their service terms that are caused by cracks, corrosion, deterioration, and short-circuiting of internal reinforcing steel wires. Therefore, they must be periodically inspected to evaluate their structural safety. There are many methods of performing external inspection after installation at an actual site. However, on-site nondestructive safety inspection of steel reinforcement wires inside poles is very difficult. In this study, we developed an application that classifies the magnetic field signals of multiple channels, as measured from the actual poles. Initially, the signal data were gathered by inserting sensors into the poles, and these data were then used to learn the patterns of safe and damaged features. These features were then processed with the isometric feature mapping (ISOMAP) dimensionality reduction algorithm. Subsequently, the resulting reduced data were processed with a random forest classification algorithm. The proposed method could elucidate whether the internal wires of the poles were broken or not according to actual sensor data. This method can be applied for evaluating the structural integrity of concrete poles in combination with portable devices for signal measurement (under development).

An experimental verification of a damage detection process using novel optimization techniques such as modified real coded genetic algorithms and swarm-based algorithms is presented. Here, the objective function is defined as the sum of differences of the modal frequencies between intact and stiffness damaged state, which has to be minimized to identify the damage location and its severity in the process of model updating. In addition to the structural or damage variables such as the mass or stiffness of the numerical model, the profiles of modal frequency shifts are also damage-sensitive features. The iterative process that uses the proposed population-based optimization algorithms successfully identifies the local mass change of a test structure by updating the damage variables to fit the modal data of test structures such as a cantilevered beam and multibay truss frame. Copyright © 2011 John Wiley & Sons, Ltd.

Anomaly sound detection (ASD) is a field of research dedicated to the early identification of mechanical failures and the facilitation of preventative maintenance to avert potential hazards and optimize performance. The capability of ASD to detect defects in mechanical equipment within this domain largely depends on the ability to uncover faults that belong to unknown categories. However, the limited labeled anomaly data available in real-world settings remains a challenge in ASD. To overcome this problem, in this study, we introduce a novel method for ASD, namely LSTM-AE with MFCC and dynamic feature maximization transformation (DFMT). The proposed method can improve normal and anomaly sound distinction in the limited anomaly data. Additionally, the unsupervised learning LSTM-AE technique enables the model to effectively learn complex sound patterns and accurately identify anomalies and deviations from normal features. In the proposed method, MFCC and DFMT are combined to generate and integrate feature maps, removing noise while retaining robustness without losing crucial features. Experimental results reveal that the proposed method achieved an accuracy of 99.2%.