Bearing Fault Diagnosis in Traction Motor Using the Features Extracted from Filtered Signals

Yetiş, Hasan; Karaköse, Mehmet; Aydın, İlhan; Akın, Erhan

doi:10.1109/idap.2019.8875901

Cited by 5 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed method is used to identify faults in gearbox and bearing system of rotatory machines. In [37] proposes a method for fault detection of traction motor using a filter which estimates the next healthy value from the previous values of the signal. From the difference of the original and estimated signals, various statistical features are extracted and classification is performed using artificial neural network (ANN), K-nearest neighbors (KNN), SVM and random forest.…”

Section: Literature Reviewmentioning

confidence: 99%

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Kafeel¹,

Aziz

Awais

et al. 2021

Sensors

View full text Add to dashboard Cite

Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Kafeel¹,

Aziz

Awais

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Bearing FD using vibration signal is studied in the literature widely through various methods and algorithms [29][30][31][32]. For instance, Reference [33] uses vibration signals to diagnose bearing fault through an intelligent filter (adaptive linear neuron) by extracting features and running classification algorithms. Since the bearing fault arises between 2-4 kHz, in Reference [34], it is detected through a neural network which is trained by the coherence of vibration and stator current signals.…”

Section: Introductionmentioning

confidence: 99%

Integrated Fault Evaluation Through Fusion Algorithm Supported by Kalman Filter

Mousavi¹,

Kara²,

Şeker³

2020

View full text Add to dashboard Cite

An Integrated Fault Evaluation (IFE) process is proposed in this study. It includes Sensor Validation (SV), Fault Detection (FD) and Fault Source Identification (FSI). The proposed algorithm employs data fusion algorithm enhanced by Kalman filter (KF). As the case study, vibration signals representing different aging states of an induction motor are used. The vibration data collected from two identical sensors with different measurement and process noises are achieved. Through the statistical and frequency domain characteristics, IFE is realized. The most prominent contribution of the study is the capability of distinction between the aging of the system and the process problems. For this aim, a rate representing the healthiness, which can discern the impact of the process noise and system aging, is calculated.

show abstract

Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks

Ribeiro

Almeida

Gomes

2020

Neural Comput & Applic

View full text Add to dashboard Cite

Bearing Fault Diagnosis in Traction Motor Using the Features Extracted from Filtered Signals

Cited by 5 publications

References 14 publications

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Integrated Fault Evaluation Through Fusion Algorithm Supported by Kalman Filter

Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks

Contact Info

Product

Resources

About