Industrial gearbox fault diagnosis based on multi-scale convolutional neural networks and thermal imaging

Li, Yongbo; Du, Xiaoqiang; Wang, Xianzhi; Si, Shubin

doi:10.1016/j.isatra.2022.02.048

Cited by 44 publications

(11 citation statements)

References 26 publications

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“…[301] convolutional neural networks extract features of infrared thermal images, a robust method of decomposing images at multiple scales followed on by Gaussian pyramid that not only improves the reliability of extracted features but is also resistant to noise interference, thus, merging multiple scaled convolutional neural networks with IR images, led to an end-to-end industrial defect inspection system. [302] To further facilitate fault diagnosis, the construction of a practicefocused algorithm for serving of foundation for the corresponding Excel sheet, shown pertinent rated currents in a broad range of the tested equipment, for variety of commercial and industrial thermal imagers, while monitoring electrical installations of lower voltage. [303] In other industrial electrical applications perspective, the infrared thermal imaging technique has been also applied for assessing the rotational excitation winding of synchronous generators, [304] study lithium battery degradation to understand the self-heating function of all climate batteries, [305] and predict battery cycle life based on thermal sensing and artificial neural network.…”

Section: Industrial and Power Inspectionsmentioning

confidence: 99%

Luminescence‐Based Infrared Thermal Sensors: Comprehensive Insights

Fahad,

Li,

Zhai

et al. 2023

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Recent chronological breakthroughs in materials innovation, their fabrication, and structural designs for disparate applications have paved transformational ways to subversively digitalize infrared (IR) thermal imaging sensors from traditional to smart. The noninvasive IR thermal imaging sensors are at the cutting edge of developments, exploiting the abilities of nanomaterials to acquire arbitrary, targeted, and tunable responses suitable for integration with host materials and devices, intimately disintegrate variegated signals from the target onto depiction without any discomfort, eliminating motional artifacts and collects precise physiological and physiochemical information in natural contexts. Highlighting several typical examples from recent literature, this review article summarizes an accessible, critical, and authoritative summary of an emerging class of advancement in the modalities of nano and micro‐scale materials and devices, their fabrication designs and applications in infrared thermal sensors. Introduction is begun covering the importance of IR sensors, followed by a survey on sensing capabilities of various nano and micro structural materials, their design architects, and then culminating an overview of their diverse application swaths. The review concludes with a stimulating frontier debate on the opportunities, difficulties, and future approaches in the vibrant sector of infrared thermal imaging sensors.

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Section: Industrial and Power Inspectionsmentioning

confidence: 99%

Luminescence‐Based Infrared Thermal Sensors: Comprehensive Insights

Fahad,

Li,

Zhai

et al. 2023

Small

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“…Compared with traditional CNN, MSCNN has better feature extraction ability [35][36][37]. In the feature extraction stage, after the image information is processed by different convolution kernels, the extracted features are fused in the subsequent classification stage for the final fault classification [38]. This study designed a MSCNN for fault diagnosis of the fluid end.…”

Section: Mcnnmentioning

confidence: 99%

SO-IMCKD processed signal improving MSCNN model’s fault diagnosis accuracy for drilling pump fluid end

Ding

et al. 2023

Meas. Sci. Technol.

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Drilling pump is the “heart” of drilling construction. The key to accurate fault diagnosis is to extract useful fault features from noisy raw signals. In order to improve the accuracy of fault diagnosis of drilling pump fluid end, this paper proposes a fault diagnosis method based on multi-scale convolutional neural network (MSCNN) combined with the snake optimization optimized maximum correlation kurtosis deconvolution (SO-IMCKD). First, the SO algorithm is employed to optimize the filter length and the shift number of IMCKD to process the raw signal, enhancing the fault features from the raw signal. Second, the continuous wavelet transform (CWT) is used to convert the enhanced signals into time-frequency images which are input into an established MSCNN to extract the fault feature more effectively. Finally, by changing the training batchsize of the MSCNN model, the identification effect of the model to the normal state, minor damage, and serious damage of the fluid end is analyzed. The identification of nine states of the fluid end is successfully carried out, with an average diagnostic accuracy of 99.93%. Moreover, the adaptability of the proposed method is verified with the Mechanical Failure Prevention Technology Association (MFPT) dataset. The method has high accuracy and good adaptability, which has desired prospect for drilling pump fault diagnosis and bearing fault diagnosis.

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“…For the defect detection of this type of metal columnar object, many experts and scholars have studied various detection techniques. According to their different basic principles, they can be roughly divided into x-ray detection [7], ultrasonic detection [8], thermal imaging [9], magnetic detection [10], optical detection [11], etc.…”

Section: Introductionmentioning

confidence: 99%

FLCNet: faster and lighter cross-scale feature aggregation network for lead bar surface defect detection

Lv,

Xia,

et al. 2024

Meas. Sci. Technol.

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Aiming at the defect inspection under the characteristics of scale change, high reflection, inclined deformation of defects of lead bars and meeting the needs for faster detection, this paper proposes a faster and lighter cross-scale feature aggregation network (FLCNet). In this study, we focus on the redundancy of channel information, and design a new partial channel group convolution, based on which we design a Faster C3 module and a lightweight cross-scale feature fusion module. In addition, we design a cross-scale slim neck to reduce the redundant feature transfer of the model. Finally, we propose a uniform brightness acquisition method for lead bar sidewall image by using combined light source and construct a lead bar dataset with various complex defect samples. Experiments show that FLCNet effectively improves the detection accuracy of the surface defects of lead bars, the mAP@0.5 value reaches 97.1%, and compared with YOLOv5s, the model’s parameters reduced by 33.9%. At the same time, the detection speed reaches 114.9 FPS, which is faster than other advanced detection models.

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Industrial gearbox fault diagnosis based on multi-scale convolutional neural networks and thermal imaging

Cited by 44 publications

References 26 publications

Luminescence‐Based Infrared Thermal Sensors: Comprehensive Insights

Luminescence‐Based Infrared Thermal Sensors: Comprehensive Insights

SO-IMCKD processed signal improving MSCNN model’s fault diagnosis accuracy for drilling pump fluid end

FLCNet: faster and lighter cross-scale feature aggregation network for lead bar surface defect detection

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