Automatic crack classification by exploiting statistical event descriptors for Deep Learning

2019/07/24 by Giulio Siracusano, Francesca Garescì, Siracusano, Giulio +15 · 1 citation
Computer Science · Engineering · Mathematics · #Artificial intelligence #Artificial neural network #Autoencoder #Computer science #Concrete Corrosion and Durability #Data mining #Deep learning #Engineering #Event (particle physics) #FOS: Computer and information sciences #FOS: Electrical engineering #Infrastructure Maintenance and Monitoring #Kurtosis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Signal Processing (eess.SP) #Structural Health Monitoring Techniques #Structural health monitoring #cs.LG #eess.SP #electronic engineering #information engineering #stat.ML

paper · pdf · doi:10.48550/arxiv.1907.10709

published in arXiv (Cornell University) (Cornell University) · 19 pages, 2 tables, 9 figures

openalex publication_date 2019/07/24 · arxiv created 2021/11/26 · arxiv updated 2021/11/29 · openalex created_date 2022/07/28 · openalex updated_date 2026/08/05

Abstract

In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven health monitoring systems is gaining in popularity due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as Deep Learning. The main purpose of this paper is to combine deep neural networks with Bidirectional Long Short Term Memory and advanced statistical analysis involving Instantaneous Frequency and Spectral Kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from acoustic emission events (cracks). We investigated on effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of future on structural health monitoring (SHM) technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance.

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