Fault diagnosis of gear transmissions via optic Fiber Bragg Grating strain sensors

Bachar, Lior; Klein, Renata; Tur, Moshe; Bortman, Jacob

doi:10.1016/j.ymssp.2021.108629

Cited by 19 publications

(8 citation statements)

References 18 publications

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“…When the grating period Λ changes, the effective refractive index n eff , microcavity length L M , and reflection spectrum of the MFPI can be simulated using (3). The spectrum of the fiber Fabry-Perot interferometer contains multiple interference peaks, unlike the single peak observed in the spectrum of the uniform FBG.…”

Section: Transmission Matrix Analysis and Simulation Of Mfpimentioning

confidence: 99%

“…Fiber grating sensing technology offers many obvious advantages, such as high sensitivity, compact size, immunity to electromagnetic interference, ease of building sensing networks, and powerful multiplexing capability [1][2][3][4][5]. It has attracted considerable attention, especially for the measurement of parameters such as temperature [6], refractive index [7], strain [8], and pressure [9], thereby making fiber Bragg grating (FBG) an excellent sensing element with great potential for use in fields such as biochemical sensing [10,11] structural health monitoring [12,13], and gas/oil exploration [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Multiparameter measurement depending on the cavity-length differentiation characteristics of a microfiber Fabry–Perot interferometer

Liu,

Ran,

Liu

et al. 2024

Meas. Sci. Technol.

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To solve the cross-sensitivity problem affecting optical fiber sensors and realize multiparameter measurement, a microfiber Fabry–Perot interferometer (MFPI) is proposed and experimentally demonstrated. Simultaneous measurement of two distinct physical parameter (temperature and strain) is realized by monitoring wavelength and reflectivity of MFPI. In the temperature field range of 22-36 ℃, the maximum temperature sensitivity can reach 12 pm/℃. The maximum strain sensitivity is up to 0.8 pm/με in the strain range of 0-800 με. In the simultaneous measurement experiments, the relative errors of temperature and strain were 4.0 % and 0.8%, respectively. Furthermore, the sensing element used in this method was just a single fiber grating sensor without any coating layer, which demonstrated the significant advantage of the proposed method in reducing the complexity and cost of multiparameter measurement.

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Section: Transmission Matrix Analysis and Simulation Of Mfpimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiparameter measurement depending on the cavity-length differentiation characteristics of a microfiber Fabry–Perot interferometer

Liu,

Ran,

Liu

et al. 2024

Meas. Sci. Technol.

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“…The gear pitting fault can be detected by analyzing the change of strain. The strain monitoring method has shown great promise in the laboratory [19]. Conventional monitoring technology can play a certain role in damage detection for the gear transmission system.…”

Section: Introductionmentioning

confidence: 99%

Structural health dynamic monitoring for gear transmission based on guided waves

Chai

Wang²,

Liu³

et al. 2023

Smart Mater. Struct.

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The rotating machinery has a wide range of applications in many fields. The gear transmission system is one of the key structures of rotating machinery. Once the failure occurs to the gear system, it will destroy the equipment itself and lead the whole industrial production to break. Thus, it is necessary to directly monitor the health condition of the gear system under static and running working conditions. Guided waves method is a technology which has been widely used in the field of structural health monitoring (SHM). In this paper, guided waves technology is innovatively applied to the condition monitoring of gear system, which realizes the gear structure direct monitoring for low speed rotating machinery. The piezoelectric transducers integrated with the SMART Layer were placed near the gear mesh region for the direct health monitoring. The feasibility was verified by the guided waves monitoring experiments in a fixed-axis spur gear transmission system. The tooth wear and gear crack were monitored by guided waves under a single working condition. In addition, the stability verification experiment of guided waves was also performed under various low-speed working conditions. To characterize the propagation of gear faults, considering the characteristics of guided waves, a modified health indicator extraction methodology is proposed based on permutation entropy (PeEn). The results indicated that the guided waves technology has fantastic potential applications in gear transmission system dynamic direct health monitoring.

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“…Morais [16] monitors the the force in the mechanical system, which is more likely to be realized in the monitoring of rotating parts. Bachar [1] summarized the application of optic Fiber Bragg Grating (FBG) strain sensors for gear diagnostics, developed a new diagnosis method based on FBG strain sensor. Lei [13] uses deep learning to train deep neural networks, using mechanical frequency domain signals to achieve adaptive extraction of fault features, and accurately identifies health conditions for different fault types at different multi-stage gear transmission systems fault locations under multiple operating conditions and a large sample number.…”

Section: Introductionmentioning

confidence: 99%

Multi-level health degree analysis of vehicle transmission system based on PSO- Indexed by: BP neural network data fusion

Wu¹,

Cui²,

Shu³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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In order to realize the evaluation of the vehicle transmission system health degree, a prediction model by multi-level data fusion method is established in this paper. The prediction model applies PSO(Particle Swarm Optimization)-BP(Back Propagation) neural network algorithm, calculates the whole machine health degree and each module respective weights from the test data. On this basis, it analyzes the error between the model calculated health degree and theoretical health degree. Then the research verifies the validity and prediction model accuracy. The health degree which is obtained by the single module feature parameters fusion, and the vehicle transmission system health degree is investigated, which is less effective compared to the three-level fusions. After that, by analyzing the vehicle transmission system multi-parameter feature weights, it is found that the mechanical module accounted for the largest damage rate, and the three modules influenced the vehicle transmission system health degree in the order of mechanical module, hydraulic module, and electric control module. The study has played a guiding role in the health management of complex equipment.

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Fault diagnosis of gear transmissions via optic Fiber Bragg Grating strain sensors

Cited by 19 publications

References 18 publications

Multiparameter measurement depending on the cavity-length differentiation characteristics of a microfiber Fabry–Perot interferometer

Multiparameter measurement depending on the cavity-length differentiation characteristics of a microfiber Fabry–Perot interferometer

Structural health dynamic monitoring for gear transmission based on guided waves

Multi-level health degree analysis of vehicle transmission system based on PSO- Indexed by: BP neural network data fusion

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