Optimized magnetic sensors to measure speed and position in adverse environments

Przysowa, Radosław; Rokicki, Edward; Majewski, Paweł

doi:10.1049/cp.2014.0552

Cited by 11 publications

(6 citation statements)

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“…Przysowa et al developed a new inductive sensor for blade health monitoring systems for military turbofans. Also the resistance to high temperature and contamination were verified [40,60]. Du et al developed multiplexed inductive sensors for detection of blade tip clearances [17].…”

Section: Hot Gas Component Monitoringmentioning

confidence: 99%

A Review of NDT/Structural Health Monitoring Techniques for Hot Gas Components in Gas Turbines

Mevissen

Meo

2019

Sensors

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The need for non-destructive testing/structural health monitoring (SHM) is becoming increasingly important for gas turbine manufacturers. Incipient cracks have to be detected before catastrophic events occur. With respect to condition-based maintenance, the complex and expensive parts should be used as long as their performance or integrity is not compromised. In this study, the main failure modes of turbines are reported. In particular, we focus on the turbine blades, turbine vanes and the transition ducts of the combustion chambers. The existing monitoring techniques for these components, with their own particular advantages and disadvantages, are summarised in this review. In addition to the vibrational approach, tip timing technology is the most used technique for blade monitoring. Several sensor types are appropriate for the extreme conditions in a gas turbine, but besides tip timing, other technologies are also very promising for future NDT/SHM applications. For static parts, like turbine vanes and the transition ducts of the combustion chambers, different monitoring possibilities are identified and discussed.

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Section: Hot Gas Component Monitoringmentioning

confidence: 99%

A Review of NDT/Structural Health Monitoring Techniques for Hot Gas Components in Gas Turbines

Mevissen

Meo

2019

Sensors

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“…At the same time, the common methods for monitoring the cage speed are not perfect. For example, the electromagnetic induction detection [9] method requires a high electromagnetic field strength; therefore, the bearing must be magnetized before detection can be performed. This may lead to a buildup of metal debris dislodged by friction during bearing operation, which accelerates bearing failure and reduces bearing life.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Zhang

2021

Symmetry

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The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.

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“…But, the sensor has to be placed very close to the target. 2 Thus, it is not suitable to study the cage slip subjected to the limitation of space installation. Optical fiber sensor has many advantages, such as compact dimension, fast respond speed, high temperature, and wide sensor range.…”

Section: Introductionmentioning

confidence: 99%

Study on the cage slip of rolling bearing using a non-contact method

Zhan

et al. 2020

Structural Health Monitoring

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In this article, a novel, non-contact method is proposed to detect cage slip of rolling bearing. The proposed method can obtain the speed signal of cage and inner raceway by the introduction of weak magnetic field detection technology simultaneously. Meanwhile, to enhance the identified accuracy of cage slip, an adaptive filtering method is developed to suppress noise in weak magnetic field sensor output signal. The method adopts complementary complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the weak magnetic field sensor output signal into a set of intrinsic mode functions adaptively. The intrinsic mode functions contain noisy component of detection signal (noisy intrinsic mode functions) as well as corresponding signal component of the roller and inner raceway (signal intrinsic mode functions). The noisy- and signal intrinsic mode functions are identified by the development of reconstruction criterion. The criterion is determined based on upper boundary of rotational frequency of roller under pure rolling conditions. Then, the noisy intrinsic mode functions are forced to zero, and signal intrinsic mode functions are reconstructed to extract the speed information of roller and inner raceway. An experiment system is developed to verify the effectiveness of proposed method. The experiment result shows that the cage slip decreases with the increment in load under the given speed conditions and increases with the increment in speed under the given load conditions, as well as the cage slip is more serious under given no-load conditions. It shows that the weak magnetic field detection technology can be used to detect the cage slip of rolling bearing.

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Optimized magnetic sensors to measure speed and position in adverse environments

Cited by 11 publications

References 0 publications

A Review of NDT/Structural Health Monitoring Techniques for Hot Gas Components in Gas Turbines

A Review of NDT/Structural Health Monitoring Techniques for Hot Gas Components in Gas Turbines

Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Study on the cage slip of rolling bearing using a non-contact method

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