An Intelligent Warning Method for Diagnosing Underwater Structural Damage

Abstract

A number of intelligent warning techniques have been implemented for detecting underwater infrastructure diagnosis to partially replace human-conducted on-site inspections. However, the extensively varying real-world situation (e.g., the adverse environmental conditions, the limited sample space, and the complex defect types) can lead to challenges to the wide adoption of intelligent warning techniques. To overcome these challenges, this paper proposed an intelligent algorithm combing gray level co-occurrence matrix (GLCM) with self-organization map (SOM) for accurate diagnosis of the underwater structural damage. In order to optimize the generative criterion for GLCM construction, a triangle algorithm was proposed based on orthogonal experiments. The constructed GLCM were utilized to evaluate the texture features of the regions of interest (ROI) of micro-injury images of underwater structures and extracted damage image texture characteristic parameters. The digital feature screening (DFS) method was used to obtain the most relevant features as the input for the SOM network. According to the unique topology information of the SOM network, the classification result, recognition efficiency, parameters, such as the network layer number, hidden layer node, and learning step, were optimized. The robustness and adaptability of the proposed approach were tested on underwater structure images through the DFS method. The results showed that the proposed method revealed quite better performances and can diagnose structure damage in underwater realistic situations. Algorithms 2019, 12, 183 2 of 17 by detecting the future damage possibility of a multi-layer reinforced concrete building structure [11]. Kai Xu et al. proposed a new approach to damage detection of a concrete column structure subjected to blast loads using embedded piezoceramic smart aggregates [12]. According to research by Feng et al., almost all types of structural damage can be effectively identified by image processing [13-15]. Zhu Z et al. proposed a new retrieval method for crack defect performance, which located the crack point by the most advanced crack detection technology and used image refinement technology to identify the skeleton structure of the point [16]. Molero et al. applied ultrasound imaging to assess the extent of damage during concrete freeze-thaw cycles and extracted NAAP (damage index) parameters as an evaluation criterion [17]. German et al. presented a new method for automatic detection of the flaking area of reinforced concrete columns and measured its characteristics in image data [18]. Based on the data provided by self-powered wireless sensors, Hasni et al. proposed a steel beam tensional vibration fatigue cracking detection method based on artificial intelligence [19].Although these methods have achieved superior results through the combination of image properties and neural network models for damage identification, it is difficult to realize accurate damage diagnosis for underwater real-world structures [20][21][22][23]. Taking i...

Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis

“…Subsequently, for the first time, a specialized monitoring system was implemented for the detection and diagnosis of failures in a nuclear plant [ 5 , 12 , 13 ]. In recent years, promising techniques have been investigated and applied, such as parity space [ 14 , 15 , 16 ], fuzzy logic, and neural networks [ 17 , 18 , 19 ], among others. In Figure 1 the fault detection techniques are shown and in particular the Data-Drive Based method are underlined in a red color.…”

“…Accordingly, fault detection and classification (FDC) in semiconductor manufacturing is now generally accepted as an integral element of the APC (advanced process control) framework for maximizing production efficiency (yield and machine utilization) while minimizing process variation [ 18 ]. The semiconductor industry has benefited from developing and implementing many FDC techniques that aid in issue identification and root cause investigation.…”