A Scheme with Acoustic Emission Hit Removal for the Remaining Useful Life Prediction of Concrete Structures

Nguyen, Tuan-Khai; Ahmad, Zahoor; Kim, Jong‐Myon

doi:10.3390/s21227761

Cited by 10 publications

(8 citation statements)

References 28 publications

(35 reference statements)

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“…The calculation of the HI-constructing factor(s) (also known as features) is often performed either in the time, frequency, or time–frequency domain. The time domain approaches [ 27 , 28 , 37 , 38 ] generally offer a fast and simple solution that can be widely applicable to systems and fault types. They often include statistical computation and impulse analysis.…”

Section: The General Health Indicator Construction Framework and Eval...mentioning

confidence: 99%

“…In addition to the fitness analysis of the construction result, the HI was also evaluated by its performance concerning RUL prognosis. A long short-term memory recurrent neural network (LSTM-RNN) [ 28 ] was chosen for this purpose. Because each run-to-fail signal is a sequence of values at the time steps, it can be deemed as a univariate time series.…”

Section: Experimental Setup and Evaluationmentioning

confidence: 99%

“…As nondestructive methods for SHM have become dominant in recent years, different methods have been investigated for concrete SHM such as ultrasonic [ 5 , 17 , 18 ], vibration [ 19 , 20 ], image processing [ 3 , 21 , 22 ], and acoustic emission (AE) [ 4 , 12 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. Even though ultrasonic testing can detect internal defects and their sizes, it is susceptible to complicated part geometry and certain materials, such as austenitic steel, which can mask defects by causing attenuation.…”

Section: Introductionmentioning

confidence: 99%

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A Deep-Learning-Based Health Indicator Constructor Using Kullback–Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures

Nguyen

Ahmad

Kim

2022

Sensors

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This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback–Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) approaches for monitoring the health of concrete structures. Through the construction of a HI, the deterioration process can be processed and portrayed so that it can be forwarded to a prediction module for RUL prognosis. The degradation progression and failure can be identified by predicting the RUL based on the situation of the current specimen; as a result, maintenance can be planned to reduce safety risks, reduce financial costs, and prolong the specimen’s useful lifetime. The portrayal of deterioration through HI construction from raw acoustic emission (AE) data is performed using a deep neural network (DNN), whose parameters are obtained by pretraining and fine tuning using a stack autoencoder (SAE). Kullback–Leibler divergence, which is calculated between a reference normal-conditioned signal and a current unknown signal, was used to represent the deterioration process of concrete structures, which has not been investigated for the concrete beams so far. The DNN-based constructor then learns to generate HI from raw data with KLD values as the training label. The HI construction result was evaluated with run-to-fail test data of concrete specimens with two measurements: fitness analysis of the construction result and RUL prognosis. The results confirm the reliability of KLD in portraying the deterioration process, showing a large improvement in comparison to other methods. In addition, this method requires no adept knowledge of the nature of the AE or the system fault, which is more favorable than model-based approaches where this level of expertise is compulsory. Furthermore, AE offers in-service monitoring, allowing the RUL prognosis task to be performed without disrupting the specimen’s work.

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Section: The General Health Indicator Construction Framework and Eval...mentioning

confidence: 99%

Section: Experimental Setup and Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Deep-Learning-Based Health Indicator Constructor Using Kullback–Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures

Nguyen

Ahmad

Kim

2022

Sensors

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“…In the past decade, researchers have focused on feature extraction and feature recognition models for leak detection in pipelines [10,11]. AE has been used for condition monitoring in many methods [12].…”

Section: Introductionmentioning

confidence: 99%

A Method for Pipeline Leak Detection Based on Acoustic Imaging and Deep Learning

Ahmad

Kim

et al. 2022

Sensors

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This paper proposes a reliable technique for pipeline leak detection using acoustic emission signals. The acoustic emission signal of a pipeline contains leak-related information. However, the noise in the signal often obscures the leak-related information, making traditional acoustic emission features, such as count and peaks, less effective. To obtain leak-related features, first, acoustic images were obtained from the time series acoustic emission signals using continuous wavelet transform. The acoustic images (AE images) were the wavelet scalograms that represent the time–frequency scales of the acoustic emission signal in the form of an image. The acoustic images carried enough information about the leak, as the leak-related information had a high-energy representation in the scalogram compared to the noise. To extract leak-related discriminant features from the acoustic images, they were provided as input into the convolutional autoencoder and convolutional neural network. The convolutional autoencoder extracts global features, while the convolutional neural network extracts local features. The local features represent changes in the energy at a finer level, whereas the global features are the overall characteristics of the acoustic signal in the acoustic image. The global and local features were merged into a single feature vector. To identify the pipeline leak state, the feature vector was fed into a shallow artificial neural network. The proposed method was validated by utilizing a data set obtained from the industrial pipeline testbed. The proposed algorithm yielded a high classification accuracy in detecting leaks under different leak sizes and fluid pressures.

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“…Nguyen et al [100] predicted the failure of a concrete structure using the AE and RNN signals. The raw signals are first preprocessed by an SVM (support vector machine) to extract only the signals relevant to the construction of the health indicator of the concrete product.…”

mentioning

confidence: 99%

Machine-Learning-Based Methods for Acoustic Emission Testing: A Review

Ciaburro

Iannace

2022

Applied Sciences

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Acoustic emission is a nondestructive control technique as it does not involve any input of energy into the materials. It is based on the acquisition of ultrasonic signals spontaneously emitted by a material under stress due to irreversible phenomena such as damage, microcracking, degradation, and corrosion. It is a dynamic and passive-receptive technique that analyzes the ultrasonic pulses emitted by a crack when it is generated. This technique allows for an early diagnosis of incipient structural damage by capturing the precursor signals of the fracture. Recently, the scientific community is making extensive use of methodologies based on machine learning: the use of machine learning makes a machine capable of receiving a series of data, modifying the algorithms as they receive information on what they are processing. In this way, the machine can learn without being explicitly programmed, and this implies a huge use of data and an efficient algorithm to adapt. This review described the methodologies for the implementation of the acoustic emission (AE) technique in the evaluation of the conditions and in the monitoring of materials and structures. The latest research products were also analyzed in the development of new methodologies based on machine learning for the detection and localization of damage for the characterization of the fracture and the prediction of the failure mode. The work carried out highlighted the strong use of these methods, which confirms the extreme usefulness of these techniques in identifying structural damage in scenarios heavily contaminated by residual noise.

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A Scheme with Acoustic Emission Hit Removal for the Remaining Useful Life Prediction of Concrete Structures

Cited by 10 publications

References 28 publications

A Deep-Learning-Based Health Indicator Constructor Using Kullback–Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures

A Deep-Learning-Based Health Indicator Constructor Using Kullback–Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures

A Method for Pipeline Leak Detection Based on Acoustic Imaging and Deep Learning

Machine-Learning-Based Methods for Acoustic Emission Testing: A Review

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