Next Article in Journal
An Evaluation Framework for Spectral Filter Array Cameras to Optimize Skin Diagnosis
Next Article in Special Issue
Sensor-Assisted Weighted Average Ensemble Model for Detecting Major Depressive Disorder
Previous Article in Journal
Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training
Previous Article in Special Issue
A Computer Mouse Using Blowing Sensors Intended for People with Disabilities
Open AccessArticle

Fault Diagnosis of a Rotor and Ball-Bearing System Using DWT Integrated with SVM, GRNN, and Visual Dot Patterns

1
Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
2
Graduate Institute of automation Technology, National Taipei University of Technology, Taipei 10608, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(21), 4806; https://doi.org/10.3390/s19214806
Received: 24 September 2019 / Revised: 28 October 2019 / Accepted: 3 November 2019 / Published: 5 November 2019
In this study, a set of methods for the inspection of a working motor in real time was proposed. The aim was to determine if ball-bearing operation is normal or abnormal and to conduct an inspection in real time. The system consists of motor control and measurement systems. The motor control system provides a set fixed speed, and the measurement system uses an accelerometer to measure the vibration, and the collected signal data are sent to a PC for analysis. This paper gives the details of the decomposition of vibration signals, using discrete wavelet transform (DWT) and computation of the features. It includes the classification of the features after analysis. Two major methods are used for the diagnosis of malfunction, the support vector machines (SVM) and general regression neural networks (GRNN). For visualization and to input the signals for visualization, they were input into a convolutional neural network (CNN) for further classification, as well as for the comparison of performance and results. Unique experimental processes were established with a particular hardware combination, and a comparison with commonly used methods was made. The results can be used for the design of a real-time motor that bears a diagnostic and malfunction warning system. This research establishes its own experimental process, according to the hardware combination and comparison of commonly used methods in research; a design for a real-time diagnosis of motor malfunction, as well as an early warning system, can be built thereupon. View Full-Text
Keywords: support vector machines; general regression neural networks; diagnosis of malfunction; convolutional neural network support vector machines; general regression neural networks; diagnosis of malfunction; convolutional neural network
Show Figures

Figure 1

MDPI and ACS Style

Chu, W.-L.; Lin, C.-J.; Kao, K.-C. Fault Diagnosis of a Rotor and Ball-Bearing System Using DWT Integrated with SVM, GRNN, and Visual Dot Patterns. Sensors 2019, 19, 4806. https://doi.org/10.3390/s19214806

AMA Style

Chu W-L, Lin C-J, Kao K-C. Fault Diagnosis of a Rotor and Ball-Bearing System Using DWT Integrated with SVM, GRNN, and Visual Dot Patterns. Sensors. 2019; 19(21):4806. https://doi.org/10.3390/s19214806

Chicago/Turabian Style

Chu, Wen-Lin; Lin, Chih-Jer; Kao, Kai-Chun. 2019. "Fault Diagnosis of a Rotor and Ball-Bearing System Using DWT Integrated with SVM, GRNN, and Visual Dot Patterns" Sensors 19, no. 21: 4806. https://doi.org/10.3390/s19214806

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop