Vibration-based gear continuous generating grinding fault classification and interpretation with deep convolutional neural network

Liu, Chenyu; Meerten, Yannick; Declercq, Katrien; Gryllias, Konstantinos

doi:10.1016/j.jmapro.2022.04.068

Cited by 14 publications

(5 citation statements)

References 11 publications

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“…MLP has a good nonlinear learning ability, but the number of parameters that need to be adjusted in the process of model training is very large. The noise reduction methods based on CNN can effectively reduce the parameters of model training, further extract the deep-seated features of the image and reduce the occurrence of overfitting [ 71 , 72 , 73 ]. Among them, the more representative noise reduction methods based on CNN include encoder–decoder networks [ 74 , 75 ], nonlinear reaction–diffusion model [ 76 ], denoising convolutional neural networks (DNCNN) [ 77 , 78 ], etc.…”

Section: The Flow Of Fault Diagnosis Methods For Rotating Machinery U...mentioning

confidence: 99%

A Review of Fault Diagnosis Methods for Rotating Machinery Using Infrared Thermography

Wang¹,

Zhan²,

Bai³

et al. 2022

Micromachines

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At present, rotating machinery is widely used in all walks of life and has become the key equipment in many production processes. It is of great significance to strengthen the condition monitoring of rotating machinery, timely diagnose and eliminate faults to ensure the safe and efficient operation of rotating machinery and improve the economic benefits of enterprises. When the state of a rotating machine deteriorates, the thermal energy that is much more than its normal operation will be generated due to the increase in the friction between the components or other factors. Therefore, using the infrared thermal camera to collect the infrared thermal images of rotating machinery and judge the health status of rotating machinery by observing the temperature distribution in the thermal images is often more rapid and effective than other technologies. Nevertheless, after decades of development, the research achievements of infrared thermography (IRT) and its application in various industrial fields are numerous and complex, and there is a lack of systematic sorting and summary of the achievements in this field. Accordingly, this paper summarizes the development and application of IRT as a non-contact and non-invasive tool for equipment condition monitoring and fault diagnosis, and introduces the basic theory of IRT, image processing technology and fault diagnosis methods of rotating machinery in detail. Finally, the review is summarized and some future potential topics are proposed, which will make the subject easier for beginners and non-experts to understand.

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Section: The Flow Of Fault Diagnosis Methods For Rotating Machinery U...mentioning

confidence: 99%

A Review of Fault Diagnosis Methods for Rotating Machinery Using Infrared Thermography

Wang¹,

Zhan²,

Bai³

et al. 2022

Micromachines

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show abstract

“…Additionally, it is of increasing interest to perform harmonic analysis to control noise and vibration from machine tools [10,11] and how they relate to the gear geometry [12,13].…”

Section: Introductionmentioning

confidence: 99%

Characterisation and evaluation of the harmonic content of involute gear surface deviations

Reavie,

Frazer,

Przyklenk

et al. 2024

Eng. Res. Express

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Gears are fundamental precision components that are crucial to many industries, including the wind energy sector.They are traditionally analysed using profile and helix line measurements but this does not consider the majority of the contacting surface with intricate surface variations that dictate functional performance.Practical measurement and evaluation of involute gear surfaces remain underexplored.This paper uses the Fourier and wavelet transforms to evaluate measured gear surface deviation harmonic content for a single trace, a trace set, and surface analysis and describes the utility of each.The methods are first demonstrated with synthetic data and then with measured data, then the measured data is simulated in a quasi-static FE tooth contact analysis, which yields insights not solely gleaned from harmonic analysis.The uncertainty of harmonic content analysis is to be established in future work, but the uncertainty of the simulated functional performance may be a more useful metric for measurement.

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“…GradCAM was further applied to explain bearing fault classifications based on preprocessed acoustic emission data [35], vibration data based classifications of a neuro-fuzzy network [36] and in [37] GradCAM was applied in an anomaly detection use-case for time series of vibration data of a rotating system. Grad-CAM was also applied to explain vibration data-based fault detection of linear motion guides [38], bearings [39] and grinding machines [40]. Further, frequency activation maps were calculated to explain a time domain-based bearing fault detection model in frequency domain [41].…”

Section: Introductionmentioning

confidence: 99%

Explainable AI Algorithms for Vibration Data-based Fault Detection: Use Case-adadpted Methods and Critical Evaluation

Mey¹,

Neufeld²

2022

Preprint

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Analyzing vibration data using deep neural network algorithms is an effective way to detect damages in rotating machinery at an early stage. However, the black-box approach of these methods often does not provide a satisfactory solution because the cause of classifications is not comprehensible to humans. Therefore, this work investigates the application of explainable AI (XAI) algorithms to convolutional neural networks for vibration-based condition monitoring. For this, various XAI algorithms are applied to classifications based on the Fourier transform as well as the order analysis of the vibration signal. The results are visualized as a function of the revolutions per minute (RPM), in the shape of frequency-RPM maps and order-RPM maps. This allows to assess the saliency given to features which depend on the rotation speed and those with constant frequency. To compare the explanatory power of the XAI methods, investigations are first carried out with a synthetic data set with known class-specific characteristics. Then a real-world data set for vibration-based imbalance classification on an electric motor, which runs at a broad range of rotation speeds, is used. A special focus is put

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Vibration-based gear continuous generating grinding fault classification and interpretation with deep convolutional neural network

Cited by 14 publications

References 11 publications

A Review of Fault Diagnosis Methods for Rotating Machinery Using Infrared Thermography

A Review of Fault Diagnosis Methods for Rotating Machinery Using Infrared Thermography

Characterisation and evaluation of the harmonic content of involute gear surface deviations

Explainable AI Algorithms for Vibration Data-based Fault Detection: Use Case-adadpted Methods and Critical Evaluation

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