Leakage Detection of a Spherical Water Storage Tank in a Chemical Industry Using Acoustic Emissions

et al. 2020

Applied Sciences

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Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for multiclass health state classification of spherical tanks in this paper. The proposed HBoF is composed of (a) the acoustic emission (AE) features and (b) the time and frequency based statistical features. A wrapper-based feature chooser algorithm, Boruta, is utilized to extract the most intrinsic feature set from HBoF. The selective feature matrix is passed to the multi-class k-nearest neighbor (k-NN) algorithm to differentiate among normal condition (NC) and two faulty conditions (FC1 and FC2). Experimental results demonstrate that the proposed methodology generates an average 99.7% accuracy for all working conditions. Moreover, it outperforms the existing state-of-art works by achieving at least 19.4%.

Section: Data Acquisition Set-upmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and K-Nearest Neighbor

Hasan

et al. 2020

Applied Sciences

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“…A pencil lead test was performed to produce a guided wave through the tank. A peripheral component interconnect bus (PCI-2)-based data acquisition (DAQ) device [22], connected with wideband differential AE sensors (WDI-AST) [23], was used for recording the AE signals [24]. The data acquisition system and channel (sensor) arrangement during the experiment is illustrated in Figures 3 and 4.…”

Section: Experimental Testbed and Dataset Acquisitionmentioning

confidence: 99%

Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm

Hasan

2019

Energies

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Fault detection in metallic structures requires a detailed and discriminative feature pool creation mechanism to develop an effective condition monitoring system. Traditional fault detection methods incorporate handcrafted features either from the time, frequency or time-frequency domains. To explore the salient information provided by the acoustic emission (AE) signals, a hybrid of feature pool creation and an optimal features subset selection mechanism is proposed for crack detection in a spherical tank. The optimal hybrid feature pool creation process is composed of two major parts:(1) extraction of statistical features from time and frequency domains, as well as extraction of traditional features associated with the AE signals; and (2) genetic algorithm (GA)-based optimal features subset selection. The optimal features subset is then provided to the k-nearest neighbor (k-NN) classifier to distinguish between normal (NC) and crack conditions (CC). Experimental results show that the proposed approach yields an average 99.8% accuracy for heath state classification. To validate the effectiveness of the proposed approach, it is compared to conventional non-linear dimensionality reduction techniques, as well as those without feature selection schemes. Experimental results show that the proposed approach outperforms conventional non-linear dimensionality reduction techniques, achieving at least 2.55% higher classification accuracy.Energies 2019, 12, 991 2 of 14 acoustic emission signals. Detection of cracks in their early stages enables necessary measures to be undertaken in a timely fashion, thereby reducing accident occurrence. Acoustic emission (AE) signals are a promising nondestructive technology, capable of providing the information required for crack classification in the incipient stages. Compared to other nondestructive methods, AE is an economical and efficient alternative for recording the data associated with the health state of an object [6]. Additionally, the low energy signals found in AE signals can provide underlying information for substantial data-driven fault identification approaches [7,8]. Due to these benefits, AE signals are used to record data and develop a data-driven crack classification model for spherical tanks.Traditional data-driven fault identification methodologies rely on two important procedures: handcrafted feature extraction utilizing domain expertise; and detection of the health types using the extracted features. Signal-based health state diagnosis approaches rely primarily on the spectral analysis of the signals [9]. The choice of a signal analysis technique to extract discriminant information from the signals also has an impact in the performance of the fault classification process [10]. Therefore, in this study, the AE signals are analyzed in different domains to explore the detailed intrinsic information contained in the signals. The advantage of analyzing the signals in different domains is the acquisition of multi-domain knowledge of the signals, which would otherwise not...

“…Data, which indicate the health states of the tube, can be acquired by acoustic emissions, the electrical resistance, and vibration and ultrasonic signals [7][8][9], and the leakage detection mechanism is repurposed as a classifier to perform detection. Among these methods, acoustic emission (AE) is widely used to collect data to monitor a degrading system [9][10][11]. When a leak occurs in a tube, turbulence flow created by escaping fluid creates pressure waves throughout the escaping fluid, within the fluid itself, and within the container structure.…”

mentioning

confidence: 99%

Deep Learning Object-Impulse Detection for Enhancing Leakage Detection of a Boiler Tube Using Acoustic Emission Signal

Duong

2019

Applied Sciences

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Advances in technology have enhanced the ability to detect leakages in boiler tube components in thermal power plants. As a specific issue, the interaction between the coal fuel stream and the boiler tube membrane generates random and high-amplitude impulses, which negatively affect the measured acoustic emission (AE) signal from leakages. It is essential to detect and practically handle these kinds of impulses. Based on the object detection concept, this paper proposes an impulse detection methodology that employs deep learning flexible boundary regression (DLFBR). First, the shape extraction (SE) preprocessing technique is implemented to yield the shape signal, which contains intrinsic information about the impulse from the raw AE signal. Then, DLFBR extracts and generates both the feature map and the confidence mask from the shape signal to regress a boundary box, which specifies the position of the impulse. For illustration purposes, the proposed algorithm is applied to an experimental leakage detection dataset recorded from a subcritical boiler unit with a tube membrane. Experimental results show that the proposed method is effective for detecting impulses of leakage in a boiler tube testbed, providing 99.8% average classification accuracy. Appl. Sci. 2019, 9, 4368 2 of 15 is able to identify a fault at its earliest degradation phase by exploiting complex and non-stationary patterns of variables, enabling operators to take proper actions in advance. In a thermal power plant, measures such as forecasting of tube explosions, timely leak detection, and localization of leak positions are necessary to schedule repair times and minimize financial losses. However, the structure of a boiler is so complicated that the state of the component-tube interaction associated with the turbulence of the two-phase flow (water and steam) is hard to model. Hence, boiler tube maintenance can rely on data-driven fault detection in the tubes. Data, which indicate the health states of the tube, can be acquired by acoustic emissions, the electrical resistance, and vibration and ultrasonic signals [7][8][9], and the leakage detection mechanism is repurposed as a classifier to perform detection. Among these methods, acoustic emission (AE) is widely used to collect data to monitor a degrading system [9][10][11]. When a leak occurs in a tube, turbulence flow created by escaping fluid creates pressure waves throughout the escaping fluid, within the fluid itself, and within the container structure. These waves are related to structure-borne acoustic waves. To detect leaks, the energy associated with turbulence waves is transformed into electrical signals using different types of transducers, which are connected to a computer. AE sensors, which have high sensitivity, can record emission events caused by slight variations in the structure of a tube component [12]. An advantage of using AE sensors for assessment is that this allows the entire machine structure to be monitored simultaneously with a simple in situ set up. This analysis techn...