Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. Expert Systems with Applications, 36(3), 7252-7261. Purchase Instant Access
Widodo et al. used a low speed bearing test rig to stimulate different bearing faults . They used acoustic emission and vibration signals to train a support vector machine (SVM). Frosini and Bassi extended the bearing fault conditions to corrosion in bearing. They used stator current and efficiency
The research paper presents a comparative study of artificial neural network (ANN) and support vector machine (SVM) using continuous wavelet transforms and energy entropy approaches for fault diagnosis and classification of rolling element bearings. An experimental test rig is used to acquire the vibration signals of healthy and faulty bearings.
Fault in induction motor can be either mechanical or electrical fault. In the previous study, the detected fault in induction motor are broken rotor bar - and bearing fault ,-. Electrical fault is usually influenced by power quality that supplied by ac grid, such as variations of frequency and unbalanced voltage.
Fault diagnosis of rolling element bearings using basis pursuit. ... Application of discrete wavelet packet analysis for the detection and diagnosis of low speed rolling-element bearing faults, Ph.D. Thesis, Monash university, 1999. Google Scholar. N.G. Nikolaou, I.A. AntoniadisRolling element bearing fault diagnosis using wavelet packets. NDT ...
The use of acoustic emission (AE) to monitor the condition of roller bearings in rotating machinery is growing in popularity. This investigation is centered on the application of spectral kurtosis (SK) as a denoising tool able to enhance the bearing fault features from an AE signal. This methodology was applied to AE signals acquired from an experimental investigation where different size ...
Small Fault Diagnosis of Front-end Speed Controlled . Wind Generator Based on Deep Learning . Hai-Ying DONG, Li-Xia YANG, Hong-Wei LI . School of Automation & Electrical Engineering . ... diagnosis method of neural network (NN) and support vector machine (SVM) method, the small fault diagnosis
On Finding Better Wavelet Basis for Bearing Fault Detection – 18 – vibrations show periodicity in time. This periodicity is closely related to the geometry and rotational speed of the bearing. Thus, the vibration components can be determined with reasonable accuracy.
The condition monitoring and fault diagnosis of a motor bearing is necessary to reduce breakdown loss and guarantee safe operation. A simple and easily implementing algorithm is proposed for fault diagnosis of motor bearing. The core part of the algorithm is a stochastic-resonance-based adaptive
This study presents fault diagnosis of low speed bearing using support vector machine (SVM). The data used in the experiment was acquired using acoustic emission (AE) sensor and accelerometer.
The effectiveness of wavelet-based features for fault diagnosis of gears using support vector machines (SVM) and proximal support vector machines (PSVM) has been revealed by Saravanan et al. . Yang et al. have proposed a method of fault feature extraction for roller bearings based on intrinsic mode function (IMF) envelope spectrum.
This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using an AE sensor with the test bearing operating at a constant loading (5 kN) and with a speed ...
Abstract: A new method of fault diagnosis based on principal components analysis (PCA) and support vector machine is presented on the basis of statistical learning theory and the feature analysis of vibrating signal of rolling bearing. The key to the fault bearings diagnosis is feature extracting and feature classifying. Multidimensional correlated variable is converted into low dimensional ...
This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions.
This "Cited by" count includes citations to the following articles in Scholar. ... Machine health prognostics using survival probability and support vector machine. A Widodo, BS Yang. Expert Systems with Applications 38 (7), ... Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine.
Feature Mapping Techniques for Improving the Performance of Fault Diagnosis of Synchronous Generator ... Support vector machine (SVM) is a popular machine learning ... multi-class SVM for low speed bearing fault diagnosis (Kang et al., 2015). Fu et al. made a comparative study on grid
classifiers - to machinery fault diagnosis. In the present work, a comparative study is presented on effectiveness of ANN and SVMs for bearing fault diagnostics using time-domain as well as frequency spectrum features. The vibration signals obtained from bearing in normal
Fault detection, isolation, and recovery (FDIR) is a subfield of control engineering which concerns itself with monitoring a system, identifying when a fault has occurred, and pinpointing the type of fault and its location. Two approaches can be distinguished: A direct pattern recognition of sensor readings that indicate a fault and an analysis of the discrepancy between the sensor readings ...
proposed method has better performance in the speed of fault diagnosis than the method based on support vector machine (SVM), which supplies a strategy of fault diagnosis for rolling bearing. In this paper, the performance of the proposed method is also compared with other diagnostic methods.
The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects.
1 Manuscript for Mechanical Systems and Signal Processing . Circular Domain Features based Condition Monitoring for Low Speed Slewing Bearing . Wahyu Caesarendra*a,b, Buyung Kosasiha, Anh Kiet Tieu a, Craig A.S. Moodiea aSchool of Mechanical, Materials and Mechatronic Engine ering, University of Wollongong, Wollongong, New South Wales 2522, Australia