An Evaluation of Gearbox Condition Monitoring Using Infrared Thermal Images Applied with Convolutional Neural Networks

Li, Yongbo; Gu, Jiahui; Zhen, Dong; Xu, Min; Ball, Andrew

doi:10.3390/s19092205

Cited by 42 publications

(28 citation statements)

References 40 publications

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“…Thermal imaging can detect many electrical faults, namely: broken bars, shorted coils, insulation faults, fan faults, overvoltages, ventilation faults. Analysis of thermal images can detect the type and location of fault [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Głowacz

2021

Sensors

145

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The paper presents an analysis and classification method to evaluate the working condition of angle grinders by means of infrared (IR) thermography and IR image processing. An innovative method called BCAoMID-F (Binarized Common Areas of Maximum Image Differences—Fusion) is proposed in this paper. This method is used to extract features of thermal images of three angle grinders. The computed features are 1-element or 256-element vectors. Feature vectors are the sum of pixels of matrix V or PCA of matrix V or histogram of matrix V. Three different cases of thermal images were considered: healthy angle grinder, angle grinder with 1 blocked air inlet, angle grinder with 2 blocked air inlets. The classification of feature vectors was carried out using two classifiers: Support Vector Machine and Nearest Neighbor. Total recognition efficiency for 3 classes (TRAG) was in the range of 98.5–100%. The presented technique is efficient for fault diagnosis of electrical devices and electric power tools.

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Section: Theoretical Backgroundmentioning

confidence: 99%

“…The recognition rate achieved nearly 100%. Following faults were analyzed: breakages, tooth pitting, cracks [ 16 ]. The authors of the paper presented thermal condition monitoring of the three-phase induction motors.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Głowacz

2021

Sensors

145

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“…Softmax regression is widely employed for multi-class faults classification [36,37] as it assures better performance and classification results with improved computational accuracy [34]. However, traditional Softmax loss is not capable for effective features classification due to biased sample distribution leading to misclassification.…”

Section: ) Proposed Softmax Classificationmentioning

confidence: 99%

A Novel 2-D Current Signal-Based Residual Learning With Optimized Softmax to Identify Faults in Ball Screw Actuators

et al. 2020

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Ball screw electro-mechanical actuators are commonly found in high precision motion control applications including aerospace systems as well as automated setups for industries. These actuators perform flight / application critical job and ball screw drives are responsible to provide precise linear motion while carrying thrust loading. A failure in ball screw drive may disturb positioning accuracy of overall system. At present, few techniques are available to monitor electro-mechanical actuators for aerospace and industrial systems. This paper provides a deep learning based intelligent technique to monitor condition of ball screw actuators. The proposed scheme utilizes modified residual learning scheme to extract features from two-dimensional transformed motor current signals. The current signal data was collected under different load domains in terms of magnitude and direction reversal. A 2D-Remanant-CNN (2D-Rem-CNN) model was developed for features extraction with proposed optimized softmax for classification of mechanical faults. The proposed technique was validated against different ball screw fault cases. The testing results prove the superiority of 2D-Rem-CNN model against different state of the art techniques. The proposed framework was also tested for system's stability under different load domains.

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“…[14]The paper employed an auto encoding learning method for machine fault diagnosis. Diagnosis of gradual faults in high-speed gear pairs using machine learning [15].…”

Section: Related Workmentioning

confidence: 99%

Fault Monitoring System for the Multistage Gear Box Based on MSTFT and GFLA Algorithm

Ghulanavar*,

Dama

2020

IJITEE

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Gearbox functions as a significant transmission module in the mechanical devices in spite of its failure prone nature and hence there exists a need for the diagnosing the gearbox faults with an optimized solution and new methods should be engaged for improving the effectiveness, accuracy, reliability and few other such parameters. These attempts could meet the growing requirements for the condition monitoring in the detection of gear faults. The feature selection process is a notable process in the machine learning to achieve good performance in the diagnostic process. This framework builds up an innovative structure for fault diagnosis in the gearbox system. Six vibrating signals which are healthy gear signal, fault gear signal, healthy gear signal affected by noise, faulty gear signal affected by noise, distributed fault signal, and local fault signal have been generated with the prescribed dataset and the feature extraction was done by employing the Modified Short-Time Fourier Transform on the basis of Blackman window. This step was followed by selection of the features by using the combination of Genetic algorithm and Frog Leaping algorithm that is termed as GFLA.This novelty approach enhances the initialization process thereby enabling for the calculation of accurate best score calculation. The classification method is performed Support points on the basis of neural network. Finally the performance analysis with respect to the existing methodologies proved the efficiency in detecting the fault of the proposed framework.

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An Evaluation of Gearbox Condition Monitoring Using Infrared Thermal Images Applied with Convolutional Neural Networks

Cited by 42 publications

References 40 publications

Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

A Novel 2-D Current Signal-Based Residual Learning With Optimized Softmax to Identify Faults in Ball Screw Actuators

Fault Monitoring System for the Multistage Gear Box Based on MSTFT and GFLA Algorithm

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