Underwater bolted flange looseness detection using percussion-induced sound and Feature-reduced Multi-ROCKET model

Abstract

Recently, in the field of structural health monitoring, the detection of bolted connection looseness through percussion-based method and machine learning technology has received much attention due to the advantages of removing the requirement of sensor installation and potential for automation. However, there are few such research which are performed in the underwater environment. The paper proposes a new method, Feature-reduced Multiple Random Convolution Kernel Transform (FM-ROCKET), to identify the looseness level of the underwater bolted connections based on the percussion-induced sound (audio signal). By integrating deep learning (DL) and shallow learning, the FM-ROCKET model uses the 1D convolutional layer (a DL method) to extract features from the percussion-induced audio signal and adopts the rigid classifier (linear classifier, a shallow learning method) to classify the features. Five different preload levels of the bolted flange are considered. A hammer is utilized to tap the flange surface and the continuous percussion-induced audio signal is collected by a smartphone in an underwater environment. After the audio signal segmentation, single-hit audio signals are fed into the FM-ROCKET model. To verify the effectiveness of the proposed method, three case studies are conducted on two flanges. In case study I, the proposed method slightly outperforms other DL-based methods under different training/test splitting ratios. In case studies II and III, the proposed method is far more effective than other DL-based methods on independent and different test sets. The results demonstrate the superiority of the FM-ROCKET model in the underwater detection of bolted flange looseness. To the best of our knowledge, this article is the first attempt to address the detection of bolted flange looseness in the underwater environment by combining percussion-based method, DL, and shallow learning.

A pipeline corrosion detecting method using percussion and residual neural network

“…Mel spectrograms, as a popular acoustic feature, are widely used in loss identification for knock detection methods because human sensitivity to frequency is taken into account in the extraction process [39,40]. In addition, the combination of traditional CNN and Mel spectrogram can help to classify the damages based on the percussion sound after data processing [41][42][43]. However, the percussion method for corrosion detection of pipeline has not been reported, to the best knowledge of the authors.…”

Addressing data scarcity using audio signal augmentation and deep learning for bolt looseness prediction

“…On the other hand, He et al [38] extracted audio signals from an underwater flange using a hydrophone and applied a k-nearest neighbor algorithm for its looseness recognition. Chen et al [39] obtained audio data at varied looseness levels of submerged bolted joints using a smartphone. They developed a featurereduced multiple random convolution kernel transform to distinguish the bolt looseness.…”

“…Despite the efforts made by researchers to extract data from complex bolted joints, sophisticated equipment such as underwater autonomous vehicles, unmanned aerial vehicles, etc, are essential for data collection in practical applications [38,39]. However, the possibility of collecting sufficient volumes of quality data from such equipment is low due to environmental disturbances [40].…”