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

Abstract

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...

Valve Internal Leakage Rate Quantification Based on Factor Analysis and Wavelet-BP Neural Network Using Acoustic Emission

“…In order to evaluate the performance of the quantitative model based on neural network for valve leakage rate, mean absolute error (MAE), root mean square error (RMSE) and Pearson's correlation coefficient (R) are introduced as evaluation indexes, as shown in Equations (7)- (9).…”

“…Several kinds of techniques, including acoustic emission (AE) [4], vibration monitoring [5,6] and cavity dynamic pressure [7], have been used for the detection of valve internal leakage. Among these techniques, AE has witnessed a wide application in the detection of valve internal leakage both qualitatively and quantitatively, because it possesses the advantage of nonintrusive, high sensitivity and strong anti-interference ability [8][9][10]. As the accurate prediction of internal leakage Appl.…”

Performance Identification of a Steam Boiler Burner via Acoustic Analysis

“…In addition, the acoustic emission method, which, in recent years, needs less data [23], is used for failure detection in the tubes of steam generators, boilers and heat exchangers [24]. Moreover, bidirectional long short-term memory recurrent neural networks [25] and the deep learning flexible boundary regression method can be used with acoustic emission signals to enhance leak detection in boiler tubes [26]. Acoustic array global interpolation algorithms have been developed for leak detection during boiler operations of coal-fired power plants [27].…”

Data Augmentation Method by Applying Color Perturbation of Inverse PSNR and Geometric Transformations for Object Recognition Based on Deep Learning

“…It is applied in various domains, such as optimization, control and troubleshooting, and manufacturing. Specifically, deep learning is used in applications such as visual inspection processes, fault diagnoses of induction motors and boiler tubes, crack detection, and monitoring of tool condition [2][3][4][5][6][7][8].…”