Fuzzy Clustering of Spatially Relevant Acoustic Data for Defect Detection

Kasahara, Jun Younes Louhi; Fujii, Hiromitsu; Yamashita, Atsushi; Asama, Hajime

doi:10.1109/lra.2018.2820178

Cited by 17 publications

(12 citation statements)

References 13 publications

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“…The method of Louhi Kasahara et al [13] was unsupervised, i.e., did not require supervision of any form by a human user, and used position information in order to reinforce the clustering in audio feature space. The proposed method is a weakly supervised method and therefore requires a human user to provide weak supervision on each considered dataset of hammering samples.…”

Section: Our Novelty and Overview Of Proposed Methodsmentioning

confidence: 99%

“…Fuzzy C-Means is a suited clustering framework to incorporate spatial information along with the main data type [24] and has been successfully used with hammering data in [13]. It is a fuzzy clustering algorithm, meaning that samples belong to several clusters at the same time, with varying degrees expressed through fuzzy membership coefficients.…”

Section: Clustering With Position Informationmentioning

confidence: 99%

“…In [11], Mode Decomposition, Principal Component Analysis, and Independent Component Analysis were used for concrete bridge inspection. In [12,13], clustering methods were used. Those bypassed the issue of training data but involved strong priors in their design to compensate for the absence of training data, for which no mitigation would be possible.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Acoustic Inspection of Concrete Structures Using Active Weak Supervision and Visual Information

Kasahara

Yamashita

Asama

2020

Sensors

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Concrete structures are featured heavily in most modern societies. In recent years, the need to inspect those structures has been a growing concern and the automation of inspection methods is highly demanded. Acoustic methods such as the hammering test are one of the most popular non-destructive testing methods for this task. In this paper, an approach to defect detection in concrete structures with active weak supervision and visual information is proposed. Based on audio and position information, pairs of samples are actively queried to a user on their similarity. Those are used to transform the feature space into a favorable one, in a weakly supervised fashion, for clustering defect and non-defect samples, reinforced by position information. Experiments conducted in both laboratory conditions and in field conditions proved the effectiveness of the proposed method.

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Section: Our Novelty and Overview Of Proposed Methodsmentioning

confidence: 99%

Section: Clustering With Position Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Acoustic Inspection of Concrete Structures Using Active Weak Supervision and Visual Information

Kasahara

Yamashita

Asama

2020

Sensors

Self Cite

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“…However, the application of MFCCs in civil engineering is not common, and the existing researches include the concrete defect detection 75 and the delamination detection of concrete bridge decks. 76,77 Since the sound frequency that can be perceived by human ear is nonlinear (human ear can detect sound with frequencies lower than 1 kHz in linear scale and more than 1 kHz in logarithmic scale), MFCCs take this characters into consideration during the extraction process of the sound signal and constitutes a good representation of dominant features in acoustic information.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Detection of sand deposition in pipeline using percussion, voice recognition, and support vector machine

Cheng

Wang

Huo

et al. 2020

Structural Health Monitoring

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Deposits prevention and removal in pipeline has great importance to ensure pipeline operation. Selecting a suitable removal time based on the composition and mass of the deposits not only reduces cost but also improves efficiency. In this article, we develop a new non-destructive approach using the percussion method and voice recognition with support vector machine to detect the sandy deposits in the steel pipeline. Particularly, as the mass of sandy deposits in the pipeline changes, the impact-induced sound signals will be different. A commonly used voice recognition feature, Mel-Frequency Cepstrum Coefficients, which represent the result of a cosine transform of the real logarithm of the short-term energy spectrum on a Mel-frequency scale, is adopted in this research and Mel-Frequency Cepstrum Coefficients are extracted from the obtained sound signals. A support vector machine model was employed to identify the sandy deposits with different mass values by classifying energy summation and Mel-Frequency Cepstrum Coefficients. In addition, the classification accuracies of energy summation and Mel-Frequency Cepstrum Coefficients are compared. The experimental results demonstrated that Mel-Frequency Cepstrum Coefficients perform better in pipeline deposits detection and have great potential in acoustic recognition for structural health monitoring. In addition, the proposed Mel-Frequency Cepstrum Coefficients–based pipeline deposits monitoring model can estimate the deposits in the pipeline with high accuracy. Moreover, compared with current non-destructive deposits detection approaches, the percussion method is easy to implement. With the rapid development of artificial intelligence and acoustic recognition, the proposed method can realize higher accuracy and higher speed in the detection of pipeline deposits, and has great application potential in the future. In addition, the proposed percussion method can enable robotic-based inspection for large-scale implementation.

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“…Several multi-modal approaches for maintenance inspection of infrastructures have been proposed. For example, Im et al proposed a method for crack direction detection by using visual and audio information obtained by sensors [17], and Kasahara et al proposed an unsupervised learning approach for automation of a hammering test [21]. Since determination of the existence of a defect becomes feasible by using sounds returned after a hammer strike on a structure's surface, they collaboratively used audio and position information.…”

Section: Figmentioning

confidence: 99%

Deterioration level estimation via neural network maximizing category-based ordinally supervised multi-view canonical correlation

Maeda

Takahashi

Ogawa

et al. 2020

Multimed Tools Appl

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A deterioration level estimation method via neural network maximizing category-based ordinally supervised multi-view canonical correlation is presented in this paper. This paper focuses on real world data such as industrial applications and has two contributions. First, a novel neural network handling multi-modal features transforms original features into features effectively representing deterioration levels in transmission towers, which are one of the infrastructures, with consideration of only correlation maximization. It can be realized by setting projection matrices maximizing correlations between multiple features into weights of hidden layers. That is, since the proposed network has only a few hidden layers, it can be trained from a small amount of training data. Second, since there exist diverse characteristics and an ordinal scale in deterioration levels, the proposed method newly derives category-based ordinally supervised multi-view canonical correlation analysis (Co-sMVCCA). Co-sMVCCA enables estimation of effective projection considering both within-class divergence and the ordinal scale between classes. Experimental results showed that the proposed method realizes accurate deterioration level estimation.

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Fuzzy Clustering of Spatially Relevant Acoustic Data for Defect Detection

Cited by 17 publications

References 13 publications

Acoustic Inspection of Concrete Structures Using Active Weak Supervision and Visual Information

Acoustic Inspection of Concrete Structures Using Active Weak Supervision and Visual Information

Detection of sand deposition in pipeline using percussion, voice recognition, and support vector machine

Deterioration level estimation via neural network maximizing category-based ordinally supervised multi-view canonical correlation

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