Fault Diagnosis of Crack on Gearbox Using Vibration-Based Approaches

Mohammed, S.; Ghazaly, Nouby M.; Abdo, Jamil

doi:10.3390/sym14020417

Cited by 18 publications

(4 citation statements)

References 21 publications

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“…Gear-tooth crack fault detection is mentioned in [26]. The list of gear fault detections using deep neural networks can continue with more examples.…”

Section: Related Workmentioning

confidence: 99%

Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks

Lupea,

Lupea

2023

Sensors

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A study on the gearbox (speed reducer) defect detection models built from the raw vibration signal measured by a triaxial accelerometer and based on convolutional neural networks (CNNs) is presented. Gear faults such as localized pitting, localized wear on helical pinion tooth flanks, and lubricant low level are under observation for three rotating velocities of the actuator and three load levels at the reducer output. A deep learning approach, based on 1D-CNN or 2D-CNN, is employed to extract from the vibration image significant signal features that are used further to identify one of the four states (one normal and three defects) of the system, regardless of the selected load level or the speed. The best-performing 1D-CNN-based detection model, with a testing accuracy of 98.91%, was trained on the signals measured on the Y axis along the reducer input shaft direction. The vibration data acquired from the X and Z axes of the accelerometer proved to be less relevant in discriminating the states of the gearbox, the corresponding 1D-CNN-based models achieving 97.15% and 97% testing accuracy. The 2D-CNN-based model, built using the data from all three accelerometer axes, detects the state of the gearbox with an accuracy of 99.63%.

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“…Gear-tooth crack fault detection is mentioned in [26]. The list of gear fault detections using deep neural networks can continue with more examples.…”

Section: Related Workmentioning

confidence: 99%

Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks

Lupea,

Lupea

2023

Sensors

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“…The performance of helical gear transmissions may be significantly affected over time due to various types of faults. Crack propagation [6] and teeth wear [7] are two prevalent fault types frequently observed in gear transmissions that are helix-angled. These faults demonstrate an increasingly significant impact as they progress.…”

Section: Introductionmentioning

confidence: 99%

Towards Condition Monitoring: Fabrication and Finite Element Analysis of A Helical Gear Transmission Rig for Fault Simulation

2024

jjmie

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Mechanical systems are mainly considered in heavy transmissions, and helix-angled gears are being mostly used in such fields of interest. As these mechanical components operate, they are heavily subjected to potential faults of operation. To avoid experimental faults, simulation-based analysis and test apparatus are now used for diagnosing purposes and hence, improving the dependability while enhancing their performance. For that, this paper presents an integrated approach of simulation analysis and the fabrication of a test rig specifically designed for fault inducing and seeding in helical gear transmissions. Computer-aided design and engineering techniques are both used to conduct vibration-based analysis. Six mode shapes are called out for in the modal simulation workbench where they were discussed accordingly. Bearing-mounted accelerometers were used to capture vibration signals of three operating axis in order to study the potentiality of utilizing them for condition monitoring. The current approach has demonstrated advancements in fault diagnosis methodologies and provided valuable insights in enhancing reliability and dependability of such applications.

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“…Faults in these gears, without timely diagnosis, lead to considerable economic damage and can endanger human lives. Thus, monitoring the condition [1,2] and diagnosing the faults [3][4][5] of these gears are crucial to ensure the long-term service of planetary gearboxes. The mechanisms of gear faults have attracted much attention from many scholars [6,7].…”

Section: Introductionmentioning

confidence: 99%

Improved Vibration Signal Models of Localized Faults of Sun Gears to Predict Modulation

Yang,

Zeng

et al. 2023

Symmetry

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The vibration modulation of the localized faults of sun gears is complicated because of the structure and motion features of planetary gearboxes. It is challenging to completely determine the vibration modulation mechanism. To address this issue, the influences of fluctuations in speed on the factors that affect vibration like the transfer path function, time-varying projection function, and meshing force were studied. Improved vibration amplitude and frequency modulation models were then established by combining the factors affecting vibration considering the speed fluctuations caused by the localized faults of sun gears. Regarding the features of vibration modulation of localized faults of sun gears, we concluded that both fault feature frequency and carrier rotational frequency modulate the harmonics of the meshing frequency, which form two groups of modulation sidebands. In addition, the harmonics of the fault feature frequency over the whole frequency range are modulated by the rotational frequency of the carrier, which is more dominant in the resonance domain. The experimental results of localized faults of sun gears strongly agree with those deduced using the proposed models. This research contributes an effective frequency indicator that can be used to confirm the condition of planetary gearboxes.

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Fault Diagnosis of Crack on Gearbox Using Vibration-Based Approaches

Cited by 18 publications

References 21 publications

Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks

Detecting Helical Gearbox Defects from Raw Vibration Signal Using Convolutional Neural Networks

Towards Condition Monitoring: Fabrication and Finite Element Analysis of A Helical Gear Transmission Rig for Fault Simulation

Improved Vibration Signal Models of Localized Faults of Sun Gears to Predict Modulation

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