Fault Diagnosis for Steam-flow Exciting Vibration of Ultra Supercritical
1000 MW Steam Turbine

Li, Chen; Hu, Niansu

doi:10.2174/1874155x01509011067

Cited by 5 publications

(4 citation statements)

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“…In recent years, with the rapid development of fault monitoring and diagnosis technology, more and more comprehensive intelligent diagnostic technology can detect potential dangers before catastrophic equipment failures, improve equipment reliability and safety, at the same time, it can reduce the operation and maintenance cost of complex engineering system. Therefore, the health monitoring and fault diagnosis of gear machinery is of great significance [11][12].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Gears Based on Deep Learning Feature Extraction and Particle Swarm Support Vector Machine State Recognition

Al-Masri¹,

Mokayed²

2021

JISIoT

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Gear faults have always been a problem encountered in mechanical processing. For gear fault diagnosis, using mathematical-statistical feature extraction methods, deep learning neural networks (DLNN), particle swarm algorithm (PSA), and support vector machines (SVM), etc. According to the feature extraction of deep learning and particle swarm SVM state recognition, the intelligent diagnosis model is established, and the reliability of the model is verified by experiments. The model uses the combination of spectral features extracted by deep learning adaptively and the time domain features extracted by mathematical statistics methods to form a joint feature vector and then uses particle swarm SVM to diagnose the joint feature vector. After research, this paper draws a classification fitness curve combining the fault spectrum features extracted by DLNN and traditional time-domain statistical features. The classification result obtained by using this method is 95.3%. The reliability of the model is verified, and satisfactory diagnosis results are obtained. In addition, the application results also verify the effectiveness of adaptively extracting spectral features based on deep learning.

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Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Gears Based on Deep Learning Feature Extraction and Particle Swarm Support Vector Machine State Recognition

Al-Masri¹,

Mokayed²

2021

JISIoT

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“…There are a variety of signals that can be used for health monitoring, such as vibration, sound, temperature, crack and pressure. In the actual monitoring, it can be found that the vibration signal is most widely used (Li, 2001). The vibration is common for the mechanical equipment, and the mechanical damage is often caused by vibration (Li, 1999).…”

Section: Introductionmentioning

confidence: 99%

Vibration sensor for the health monitoring of the large rotating machinery: review and outlook

Xing²,

et al. 2018

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Purpose At present, one of the key equipment in pillar industries is a large rotating machinery. Conducting regular health monitoring is important for ensuring safe operation of the large rotating machinery. Because vibrations sensors play an important role in the workings of the rotating machinery, measuring its vibration signal is an important task in health monitoring. This paper aims to present these. Design/methodology/approach In this work, the contact vibration sensor and the non-contact vibration sensor have been discussed. These sensors consist of two types: the electric vibration sensor and the optical fiber vibration sensor. Their applications in the large rotating machinery for the purpose of health monitoring are summarized, and their advantages and disadvantages are also presented. Findings Compared with the electric vibration sensor, the optical fiber vibration sensor of large rotating machinery has unique advantages in health monitoring, such as provision of immunity against electromagnetic interference, requirement of less insulation and provision of long-distance signal transmission. Originality/value Both contact vibration sensor and non-contact vibration sensor have been discussed. Among them, the electric vibration sensor and the optical fiber vibration sensor are compared. Future research direction of the vibration sensors is presented.

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“…As early as the 1980s, the United States discovered that as much as 150 million dollars were spent in power sector on repairing the erosion damaged turbine cascades. 4 Research by Liu 5 showed that the severe tearing of nozzle trailing edge by SPE would reduce the stage efficiency by 15–20%, resulting in significant economic losses. Therefore, how to effectively reduce or eliminate SPE of cascade has been a main issue concerned by power companies and experts.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation on erosion resistance of iron boride coatings for steam turbines at high temperatures

Cai

Mao

Wang

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part J:

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In this paper, high temperature erosion tests are performed on four typical anti-erosion coatings for high-parameter steam turbine blades using pneumatic accelerated erosion method to simulate the actual erosion environment in power units. With the employment of scanning electron microscope, energy-dispersive X-ray spectroscopy, glancing-angle X-ray diffraction, and Vickers indentation testing techniques, detailed microscopic analysis of samples is conducted to reveal the underlying causes affecting the anti-erosion performance of coatings. Results show that, under the solid particle erosion at high temperature, boride coatings with more compact structure and higher hardness have stronger anti-erosion ability, whose erosion rate is only 30–50% that of HVOF Cr3C2 coatings under the same erosion condition. Boride coatings composed of duplex phase (FeB and Fe2B) have lower anti-erosion damage capability than that of boride coatings composed of single Fe2B phase. Under high-speed large incidence angle impingement, the surface layer of FeB cracks easily and rapidly propagates to the subsurface layer Fe2B phase under the sustained impingement from particles, resulting in the flaking off and failure of coatings. Single Fe2B phase boride coatings have smaller hardness gradient in the depth direction and higher fracture toughness than boride coatings composed of duplex phase, so that exhibit more stable excellent erosion resistance under different erosion parameters. Therefore, single Fe2B phase boride coatings should be used as the preferred wear resistant coating for turbine blades solid particle erosion problem.

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Fault Diagnosis for Steam-flow Exciting Vibration of Ultra Supercritical 1000 MW Steam Turbine

Cited by 5 publications

References 0 publications

Intelligent Fault Diagnosis of Gears Based on Deep Learning Feature Extraction and Particle Swarm Support Vector Machine State Recognition

Intelligent Fault Diagnosis of Gears Based on Deep Learning Feature Extraction and Particle Swarm Support Vector Machine State Recognition

Vibration sensor for the health monitoring of the large rotating machinery: review and outlook

Experimental investigation on erosion resistance of iron boride coatings for steam turbines at high temperatures

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