Intelligent worm gearbox fault diagnosis under various working conditions using vibration, sound and thermal features

Karabacak, Yunus Emre; Özmen, Nurhan Gürsel; Gümüşel, Levent

doi:10.1016/j.apacoust.2021.108463

Cited by 59 publications

(21 citation statements)

References 42 publications

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“…The gearboxes play an irreplaceable role in the mechanical power system, which usually works in harsh and complex environments [ 1 , 2 ]. The failure of gearboxes may cause unexpected accidents and economic losses.…”

Section: Introductionmentioning

confidence: 99%

Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions

Mao

Zhang

Qiao

et al. 2022

Entropy

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The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods.

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Section: Introductionmentioning

confidence: 99%

Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions

Mao

Zhang

Qiao

et al. 2022

Entropy

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“…The analysis of thermal images is presented in the literature [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Worm gear condition monitoring using thermal images was presented in reference [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

“…It can be used for complex structural components such as crankshafts. Worm gearbox fault diagnosis using vibrations, sounds, and thermal images was presented in the work [ 4 ]. The following states of the worm gearbox were analyzed: Pitting, tooth breakage, healthy, and wear.…”

Section: Introductionmentioning

confidence: 99%

Thermographic Fault Diagnosis of Shaft of BLDC Motor

Głowacz

2022

Sensors

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A technique of thermographic fault diagnosis of the shaft of a BLDC (Brushless Direct Current Electric) motor is presented in this article. The technique works for the shivering of the thermal imaging camera in the range of 0–1.5 [m/s2]. An electric shaver was used as the source of the BLDC motor. The following states of the BLDC motor were analyzed: Healthy BLDC motor (HB), BLDC motor with one faulty shaft (1FSB), BLDC motor with two faulty shafts (2FSB), and BLDC motor with three faulty shafts (3FSB). A new method of feature extraction named PNID (power of normalized image difference) was presented. Deep neural networks were used for the analysis of thermal images of the faulty shaft of the BLDC motor: GoogLeNet, ResNet50, and EfficientNet-b0. The results of the proposed technique were very good. PNID, GoogLeNet, ResNet50, and EfficientNet-b0 have an efficiency of recognition equal to 100% for four classes.

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“…As a result, some feature selection methods are utilized to choose more informative features to defects. For example, Karabacak et al 27 proposed an intelligent feature selection and classifier method for worm gear fault diagnosis. They considered wear, pitting, and tooth breakage as gear faults.…”

Section: Introductionmentioning

confidence: 99%

Incipient Fault Detection of Helical Gearbox Based on Variational Mode Decomposition and Time Synchronous Averaging

Niaki

Alavi

Ohadi

2022

Structural Health Monitoring

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As the fault detection methods diagnose defects in the earlier stage, the subsequent costs will be reduced. Feature extraction from the vibration signal is the foremost step for incipient fault detection of gearboxes. However, the current statistical features in the time and frequency domains cannot diagnose the early or low intensity faults. In this research, for the first time, besides these features, some other features are extracted by combining variational mode decomposition and time synchronous average (VMD-TSA) to overcome the problem. The combinations have occurred in two ways. First, the Intrinsic Mode Functions (IMFs) of the TSA signal are calculated by VMD, and the Amplitude Energy (AE) and Permutation Entropy (PE) of the first four IMFs are computed. Secondly, the IMFs of vibration signals are calculated, and the TSA features are extracted from the most informative IMF. Moreover, 16 features in the time domain, 13 features in the frequency domain, and 9 features by TSA are extracted from the vibration signals. These features are extracted from healthy and four faulty conditions: crack, spalling, chipping, and wear in three different severities. After feature extraction, the Relief-F algorithm selects the informative features, and selected features are utilized for fault detection by a Feed-Forward Neural network (FNN) classifier. In this study, the ability of the VMD-TSA method is compared with others like Empirical Mode Decomposition-TSA (EMD-TSA) and Ensemble Empirical Mode Decomposition-TSA (EEMD-TSA), which shows that the proposed method is more powerful than others in early fault detection. Besides, the classification accuracy of these methods is compared with some other feature selection methods like Laplacian Score (LS), Principal Component Analysis (PCA), and Minimum Redundancy-Maximum Relevance (MRMR). Also, the performance of the FNN classifier is compared with the Support Vector Machine (SVM). As shown in this study, the VMD-TSA features improve the early fault detection in a positive manner. For instance, the classification accuracy of all features without VMD-TSA in crack fault detection is 93.98%. However, by adding VMD-TSA features, the accuracy grows up to 99.48% in the same conditions.

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Intelligent worm gearbox fault diagnosis under various working conditions using vibration, sound and thermal features

Cited by 59 publications

References 42 publications

Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions

Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions

Thermographic Fault Diagnosis of Shaft of BLDC Motor

Incipient Fault Detection of Helical Gearbox Based on Variational Mode Decomposition and Time Synchronous Averaging

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