Improving the Performance of Storage Tank Fault Diagnosis by Removing Unwanted Components and Utilizing Wavelet-Based Features

Tra, Viet; Duong, Bach Phi; Sohaib, Muhammad; Kim, Jong‐Myon

doi:10.3390/e21020145

Cited by 6 publications

(3 citation statements)

References 51 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model's results contribute to the overall understanding and improvement of storage tank design and performance, leading to safer, more reliable, and more efficient storage systems. They can help mechanical mechanism analysis identify potential failure modes and assess the tank structure's critical loads and stress points [39].…”

Section: Introductionmentioning

confidence: 99%

Fluid–Soil–Structure Interactions in Semi-Buried Tanks: Quantitative and Qualitative Analysis of Seismic Behaviors

Pooraskarparast,

Bento,

Baron

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Qualitative and quantitative assessments evaluate the structural vulnerability of liquid storage tanks. Liquid storage tanks are typically constructed and operated in areas with hard soils to minimize confining influences. However, many of these critical structures are in coastal areas with soft soils. The research conducted in this study entails the utilization of the finite element method accurately model the seismic behavior of a semi-buried concrete tank under various conditions, including changing water levels and soil properties. The study examines fluid–structure and soil–structure interactions through dynamic analyses of the rectangular semi-buried tank and comparing its different parameters. It also identifies sensitive areas where there is a probability of liquid leakage in storage tanks. The modeling is compared with the qualitative evaluation in the Japanese vibration capability diagnosis table. The results show that the tensile stress in the wall adjacent to the expansion joint is greater than the corresponding stress in the wall in all cases. In the dynamic analyses of the soil types, the pressure on the surface increases with increasing water height. A comparison of the quantitative and qualitative evaluation results shows the possible leakage of the tank in soft soil in the expansion joint.

show abstract

Section: Introductionmentioning

confidence: 99%

Fluid–Soil–Structure Interactions in Semi-Buried Tanks: Quantitative and Qualitative Analysis of Seismic Behaviors

Pooraskarparast,

Bento,

Baron

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Signal analysis-based methods for fault diagnosis have long been adopted for use on industrial devices [17][18][19][20]. However, their application for detecting leakages and corrosion in devices working under high pressure, such as a pipeline, boiler tube, pressure vessel, or storage tank, is still in its infancy [21][22][23]. The procedure typically involves three elemental steps: fault extraction, feature evaluation, and fault classification.…”

Section: Introductionmentioning

confidence: 99%

Pressure Vessel Diagnosis by Eliminating Undesired Signal Sources and Incorporating GA-Based Fault Feature Evaluation

Tra

Kim

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

This paper proposes a reliable fault detection model for a pressure vessel under low pressure conditions. To improve the diagnostic performance, signals of different vessel health conditions are purified by eliminating noise so that signals of different categories are much more distinguishable. This de-noising technique uses a blind source separation (BSS) technique in which an initial noise-contaminated signal is separated into constituent sources. These individual sources are either device status-characterizing signal sources or interfering sources. Noise is removed and an unimpaired signal is regenerated from the characteristic sources. An abundant pool of heterogenous features are extracted from a refined signal to avoid omitting important fault-related properties. However, some of these fault features may be redundant and irrelevant, and they are likely to cause classification performance degradation. To choose the most discriminative fault-signatures from a plentiful feature set as well as reduce the dimensions of the feature input, this study proposes a new feature selection algorithm that associates a genetic algorithm (GA)-based discriminative feature analysis to a k-nearest neighbors (k-NN) classifier. The efficacy of involved techniques and the overall fault diagnostic model is examined in terms of visual and qualitative evaluations. Experimental results illustrated in this study justify that the proposed fault detection model is promising and outstanding compared to other state-of-the-art counterparts.

show abstract

“…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.…”

mentioning

confidence: 99%

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

Duong

Kim

2019

Applied Sciences

Self Cite

View full text Add to dashboard Cite

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...

show abstract

Improving the Performance of Storage Tank Fault Diagnosis by Removing Unwanted Components and Utilizing Wavelet-Based Features

Cited by 6 publications

References 51 publications

Fluid–Soil–Structure Interactions in Semi-Buried Tanks: Quantitative and Qualitative Analysis of Seismic Behaviors

Fluid–Soil–Structure Interactions in Semi-Buried Tanks: Quantitative and Qualitative Analysis of Seismic Behaviors

Pressure Vessel Diagnosis by Eliminating Undesired Signal Sources and Incorporating GA-Based Fault Feature Evaluation

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

Contact Info

Product

Resources

About