Fault Diagnosis of Coal Mill Based on Kernel Extreme Learning Machine with Variational Model Feature Extraction

Zhang, Hui; Pan, Cunhua; Wang, Yuanxin; Xu, Min; Fu, Zhou; Yang, Xin; Zhu, Lou; Zhao, Chunjiang; Song, Yangfan; Chen, Hongwei

doi:10.3390/en15155385

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…In the exemplary vibration monitoring system of a single coal mill, researchers rely on the kernel extreme learning machine model based on feature extraction. The model reduces information redundancy, reduces computing costs, and provides a new idea for the fault diagnosis of a coal mill based on feature extraction [6].…”

Section: Directions Of Research Workmentioning

confidence: 99%

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A System for Monitoring and Normative Qualification of Building Structure Vibrations Induced by Nearby Construction Works

Hypki,

Sumorek,

Kawecki

et al. 2023

Applied Sciences

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Mechanical action can damage physical objects, such as building structures. The transfer of energy to the ground through the falling mass, forming a crater, can cause kinematic forces in the soil medium. An effect of these forces, connected with the technology of geotechnical machines, is vibrations of the ground and building structures. When finding the energy impact on the object is difficult or even impossible, vibration monitoring and indirect estimation of the negative energy impact effects is possible. Commercial off-the-shelf solutions exist for vibration monitoring and recording, but they are dedicated to one application type and are relatively expensive. This paper presents a proprietary system for monitoring and recording vibrations of building objects transmitted through the ground. This allows for local registration and remote administration and presentation of data via a computer network. Vibrations can be analysed according to the requirements of developers and clients. The developed system shows that creating an effective approach for collecting data on building vibrations using components from different suppliers is possible. The waveforms collected using the system confirm the effectiveness and flexibility of the presented solution, providing a possibility of using the results to decide whether the vibrations are within the standard limits.

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Section: Directions Of Research Workmentioning

confidence: 99%

“…The models are compared with those obtained during the mill operation. By analysing these vibration characteristics and estimating the coal level in the mill, various operational problems, such as overload and lack of carbon, are identified [6].…”

Section: Introductionmentioning

confidence: 99%

A System for Monitoring and Normative Qualification of Building Structure Vibrations Induced by Nearby Construction Works

Hypki,

Sumorek,

Kawecki

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…It includes many classic and typical machine learning methods, such as artificial neural networks (ANNs), [4][5][6][7] extreme learning machines (ELMs), 8 kernel recursive least squares (KRLS), support vector machines (SVMs) and so on. [9][10][11][12] Montazeri-Gh et al proposed a novel approach based on learning the fault characteristic maps of gas turbine components using an ELM. 13 Fentaye presented a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trendmonitoring system.…”

Section: Introductionmentioning

confidence: 99%

Method of aeroengine fault diagnosis based on a Fingerprint map

Gou

Pan³

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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The electrification and intelligentization trends of aeroengine systems present new challenges for the diagnostics of traditionally centralized control architectures. Scientific and practical fault diagnosis is of great significance for reducing aeroengine failures, improving operation efficiency, and ensuring safety. To realize accurate fault diagnosis, an aeroengine fault diagnosis method based on an improved fingerprint map is proposed in this paper. Based on aeroengine data analysis, ISTOA-SVM diagnostic model that embeds improved fingerprint map data as a priori knowledge is adopted to identify the fault sets. Data based on the statistics of the fingerprint map are processed with a ratio coefficient, and the feature parameters are appended through Floyd-principal component analysis (Floyd-PCA) to increase the identification data dimension. On the other hand, to improve the diagnostic performance of the classifier, the sooty terns optimization algorithm algorithm combined with chaotic maps, a variable helix mechanism and a separating operator is designed, and the ISTOA-SVM is constructed. The fault diagnosis method was validated by using a real-world dataset of aeroengines. Comparison experiments show that the proposed method is superior in performance over the state-of-the-art approaches in diagnostic precision under the constraint of limited data. The ISTOA-SVM addresses the issue that similar features may be confused in traditional fingerprint maps. The results are presented through 5 practical cases. The study demonstrates that the proposed method achieves superior performance and can be applied to aeroengine fault diagnosis.

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“…Despite several studies having established HIs for coal mills, their accuracy and practicality find it hard to meet the requirements. Present studies on coal mill condition monitoring are commonly concerned with the real-time diagnosis of typical faults [16][17][18][19][20][21][22] . Though these studies can analyze and diagnose partial faults of the coal mill, they cannot provide an accurate assessment of the real-time operating status of the equipment, thus are employed only as the reference when faults occur in the field.…”

Section: Introductionmentioning

confidence: 99%

Health indicator construction and application of coal mill based on the dynamic model

Yin

Ding

et al. 2023

Structural Health Monitoring

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As the vital auxiliary machine of the coal-fired power plant, monitoring the real-time operating status of coal mills is critical to the secure and stable operation of the power plant. In this study, a new method of construction of the coal mill health indicator (HI) is proposed, and the operation condition monitoring approaches of the device are designed based on the HI value. Firstly, an improved coal mill dynamic model considering the joint influence of drying force, ventilation force, and grinding force is established, and a synchronous optimization approach of model structures and parameters based on the genetic algorithm is designed. Then the deviation between the model output and the actual value is computed by the designed distance measuring approach, and the typical fault characteristic factors are designed based on the relation between the dynamic model and the actual operating state. And the HI value is calculated by fusing the deviation with the characteristic factors. Finally, the HI value is applied to the process of operation condition evaluation, fault diagnosis, and trend prediction, and has obtained favorable application effects. The results of this research show that the established HI value can reflect the operating status of the coal mill promptly and accurately, and the monitoring method designed based on the values have satisfactory practicality.

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Fault Diagnosis of Coal Mill Based on Kernel Extreme Learning Machine with Variational Model Feature Extraction

Cited by 9 publications

References 26 publications

A System for Monitoring and Normative Qualification of Building Structure Vibrations Induced by Nearby Construction Works

A System for Monitoring and Normative Qualification of Building Structure Vibrations Induced by Nearby Construction Works

Method of aeroengine fault diagnosis based on a Fingerprint map

Health indicator construction and application of coal mill based on the dynamic model

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