Rolling bearing faults diagnosis based on empirical mode decomposition: Optimized threshold de-noising method

Abdelkader, Rabah; Derouiche, Z.; Kaddour, Abdelhafid; Zergoug, M.

doi:10.1109/icmic.2016.7804296

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…For illustration purposes, this paper compared the proposed method with two other impulse detection methods found in the literature. The results of the proposed DLFBR algorithm and the other two methods, i.e., the soft threshold [21,22] and constant fault alarm rate (CFAR) [24] methods, are considered by measuring the prediction performance of these methodologies based on the metric of the average precision, which is based on the intersection over union. To compute the precision, the overlap area between the output box from the algorithm and the ground truth box was first found.…”

Section: Effectiveness Of the Dlfbr Impulse Detection Algorithmmentioning

confidence: 99%

“…To filter the fluid leakage signal, this kind of high S/N impulse waveform needs to be detected and removed from the recorded signal. Previously, the impulse detection problem has usually been solved by setting a threshold (e.g., a soft threshold [21,22], wavelet threshold [23], constant false alarm rate for object detection [24]) and a cut-off range where the amplitude of the signal exceeds the threshold. However, this approach is easily affected by random peaks from noise, and the cut-off range does not cover the whole length of the impulse.…”

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confidence: 99%

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

Duong

Kim

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...

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Section: Effectiveness Of the Dlfbr Impulse Detection Algorithmmentioning

confidence: 99%

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

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“…The EMD method decomposes a signal into several intrinsic mode functions (IMF) and a residual, where the IMF should meet the following two conditions [1][2][3][4][5]:…”

Section: The Emd Algorithmmentioning

confidence: 99%

“…Appropriate processing of the vibration signal is necessary to eliminate signals from other sources in the machine and to improve the quality of the useful signal. Empirical Mode Decomposition (EMD) is a signal processing tool used to analyze non-stationary, nonlinear signals [1][2][3][4][5]. It was first presented by Huang et al [3].…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing fault diagnosis based on improved complete ensemble empirical mode of decomposition with adaptive noise combined with minimum entropy deconvolution

Abdelkader¹,

Abdelhafid²

2018

J. vibroeng.

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The vibration signals provide useful information about the state of rolling bearing and the diagnosis of the faults requires an accurate analysis of these signals. Several methods have been developed for diagnosing rolling bearing faults by vibration signal analysis. In this paper, we present an improvement of the technique Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), this technique is combined with the Minimum Entropy Deconvolution (MED) and the correlation coefficient to diagnose defects. First, the vibration signal was decomposed by the improved CEEMDAN decomposition into several oscillatory modes called Intrinsic Mode Function (IMF). After calculation of the correlation coefficients between the original signal and their IMFs, the modes with higher coefficients are selected as the relevant modes. Secondly, the MED technique is applied to the selected modes in order to improve the sensitivity of the scalar and frequency indicators of faults detection. Finally, kurtosis and envelope analysis are used to detect and locate the defect position. The simulation is carried out using the Case Western University data base and the results obtained show that the proposed method provides very good results for the early detection and diagnosis of defects and can efficiently extract the defective characteristics of the rolling bearing.

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“…Fault diagnosis is important to system to correct timely and work smoothly. Up to now, fault diagnosis has been applied extensively to all kinds of profession, such as mechanics, [1][2][3][4] chemistry, 5 nucleus, 6 and electric. 7 A number of approaches to optimize the algorithm of fault diagnosis are proposed, such as the average current Park's vector approach, 8 a fuzzy approach, 9,10 and optimized threshold de-noising method.…”

Section: Introductionmentioning

confidence: 99%

Engine fault diagnosis based on sensor data fusion considering information quality and evidence theory

Zhang

Deng

2018

Advances in Mechanical Engineering

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Fault diagnosis is a problem processing variable information obtained from different sources in nature. Evidence theory, efficient to deal with information viewed as evidence, is widely used in fault diagnosis. However, a shortcoming of the existing fault diagnosis methods only gets probability distribution rather than the basic probability assignment. A novel method of generating basic probability assignment that takes information quality into account is proposed. The probability distribution is determined by the preliminary matrix and sampling matrix that are constructed by sensor data. And the quality of probability distribution is taken as the discount factor and the rest of belief is assigned to the universal set. Hence, the basic probability assignment is obtained. Then, basic probability assignment can be combined with Dempster and Shafer evidence theory to determine the status of the engine. An application of engine fault is shown to illustrate the practicability of the proposed method. Then by comparing the result of the method which takes information quality into account (the proposed method) and does not do it, the former is better than the latter. Finally, the reliability analysis shows that the proposed method has strong reliability because performance accuracy is 100% when the error rate is less than 10%.

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Rolling bearing faults diagnosis based on empirical mode decomposition: Optimized threshold de-noising method

Cited by 11 publications

References 8 publications

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

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

Rolling bearing fault diagnosis based on improved complete ensemble empirical mode of decomposition with adaptive noise combined with minimum entropy deconvolution

Engine fault diagnosis based on sensor data fusion considering information quality and evidence theory

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