An integrated approach of Adaptive Neuro-Fuzzy Inference System and dimension theory for diagnosis of rolling element bearing

Kumbhar, Surajkumar G.; Sudhagar, P. Edwin

doi:10.1016/j.measurement.2020.108266

Cited by 26 publications

(8 citation statements)

References 26 publications

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“…All four described errors are applied on the measured data, and the accuracy of the designed scheme is examined. To see the superiority of designed algorithm better, it is compared with a recurrent fuzzy neural network (RFNN) [36], SVM-NN [37] and neurofuzzy (NFLS) [38]. The accuracy of different methods has been shown in Table 1.…”

Section: Fault Detectionmentioning

confidence: 99%

Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry

Wang

Tavoosi

Mohammadzadeh

et al. 2021

Sensors

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The main contribution of this paper is to develop a new flowmeter fault detection approach based on optimized non-singleton type-3 (NT3) fuzzy logic systems (FLSs). The introduced method is implemented on an experimental gas industry plant. The system is modeled by NT3FLSs, and the faults are detected by comparison of measured end estimated signals. In this scheme, the detecting performance depends on the estimation and modeling performance. The suggested NT3FLS is used because of the existence of a high level of measurement errors and uncertainties in this problem. The designed NT3FLS with uncertain footprint-of-uncertainty (FOU), fuzzy secondary memberships and adaptive non-singleton fuzzification results in a powerful tool for modeling signals immersed in noise and error. The level of non-singleton fuzzification and membership parameters are tuned by maximum correntropy (MC) unscented Kalman filter (KF), and the rule parameters are learned by correntropy KF (CKF) with fuzzy kernel size. The suggested learning algorithms can handle the non-Gaussian noises that are common in industrial applications. The various types of flowmeters are investigated, and the effect of common faults are examined. It is shown that the suggested approach can detect the various faults with good accuracy in comparison with conventional approaches.

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Section: Fault Detectionmentioning

confidence: 99%

Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry

Wang

Tavoosi

Mohammadzadeh

et al. 2021

Sensors

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“…The conventional maintenance approaches are complex, time-consuming and associated with higher costs. It has been overcome by machine learning algorithms such as Decision Tree (DT), 30 Support Vector Machine (SVM), 31,32 k-Nearest Neighbour (KNN), 33 ANN, 34 which are well known due to their adaptability and robustness to distinguish the faults in rotating machinery. KNN is a statistical instance-based classification algorithm, which involves easy implementation and fast training, but it requires large storage space, and its testing is slow.…”

Section: Introductionmentioning

confidence: 99%

An intelligent bearing fault diagnosis based on hybrid signal processing and Henry gas solubility optimization

Mishra

Choudhary

Mohanty

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Bearing is regarded as one of the core elements in rotating machines and its fault diagnosis is essential for better reliability and availability of the rotating machines. This paper puts forward an intelligent vibration signal-based fault diagnosis approach for bearing faults identification at an early stage, irrespective of speed conditions. The proposed methodology comprises of a frequency shift-based hybrid signal processing technique that involves a combination of Hilbert Transform (HT) and Discrete Wavelet Transform (DWT) followed by sliding window-based feature extraction. Thereafter, a newly developed Henry Gas Solubility Optimization (HGSO) is implemented to select the relevant features. At last, the optimal attributes are used to train the Artificial Neural Network (ANN) model for the classification of the different bearing faults. To test the effectiveness of the speed independent model, experimental validation was done with constant and varying speed conditions. The results demonstrate that the proposed methodology has a tremendous potential to eliminate unplanned failures caused by bearing in rotating machinery.

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“…To reduce the complexity of system modeling based on mathematical algorithms, datadriven approaches have been introduced [12,13]. The applications of the fuzzy technique in function approximation (system modeling) have been reported in [14]. Tuning the gain updating factors and membership functions are significant challenges of the classical fuzzy technique for function approximation.…”

Section: Introductionmentioning

confidence: 99%

Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning

Piltan

Kim

2021

Applied Sciences

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In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated with machine learning-based signal approximation to approximate the bearing vibration signal in normal conditions. After that, the combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy technique is integrated with the proposed signal approximation technique to design an intelligent digital twin. Next, the residual signals will be generated using the proposed intelligent digital twin and the original RAW signals. The machine learning approach will be integrated with the proposed intelligent digital twin for the classification of the bearing anomaly and crack sizes. The Case Western Reserve University bearing dataset is used to test the impact of the proposed scheme. Regarding the experimental results, the average accuracy for the bearing fault pattern recognition and crack size identification will be, respectively, 99.5% and 99.6%.

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An integrated approach of Adaptive Neuro-Fuzzy Inference System and dimension theory for diagnosis of rolling element bearing

Cited by 26 publications

References 26 publications

Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry

Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry

An intelligent bearing fault diagnosis based on hybrid signal processing and Henry gas solubility optimization

Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning

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