scholarly journals Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3575 ◽  
Author(s):  
María Jesús Gómez ◽  
Cristina Castejón ◽  
Eduardo Corral ◽  
Juan Carlos García-Prada
Keyword(s):  
Condition Monitoring ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Support Vector ◽  
Good Reliability ◽  
Railway Axle ◽  
Monitoring Technique ◽  
The Status ◽  
Minimum Number ◽  
Diagnosis Model

Railway axles are critical to the safety of railway vehicles. However, railway axle maintenance is currently based on scheduled preventive maintenance using Nondestructive Testing. The use of condition monitoring techniques would provide information about the status of the axle between periodical inspections, and it would be very valuable in the prevention of catastrophic failures. Nevertheless, in the literature, there are not many studies focusing on this area and there is a lack of experimental data. In this work, a reliable real-time condition-monitoring technique for railway axles is proposed. The technique was validated using vibration measurements obtained at the axle boxes of a full bogie installed on a rig, where four different cracked railway axles were tested. The technique is based on vibration analysis by means of the Wavelet Packet Transform (WPT) energy, combined with a Support Vector Machine (SVM) diagnosis model. In all cases, it was observed that the WPT energy of the vibration signals at the first natural frequency of the axle when the wheelset is first installed (the healthy condition) increases when a crack is artificially created. An SVM diagnosis model based on the WPT energy at this frequency demonstrates good reliability, with a false alarm rate of lower than 10% and defect detection for damage occurring in more than 6.5% of the section in more than 90% of the cases. The minimum number of wheelsets required to build a general model to avoid mounting effects, among others things, is also discussed.

scholarly journals Improving the Performance of Storage Tank Fault Diagnosis by Removing Unwanted Components and Utilizing Wavelet-Based Features

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 145 ◽  
Author(s):  
Viet Tra ◽  
Bach-Phi Duong ◽  
Jae-Young Kim ◽  
Muhammad Sohaib ◽  
Jong-Myon Kim
Keyword(s):  
Fault Diagnosis ◽  
Storage Tank ◽  
Wavelet Packet ◽  
Relative Energy ◽  
Classification Performance ◽  
Support Vector ◽  
Diagnostic Model ◽  
Spherical Tank ◽  
Useful Components ◽  
Diagnosis Model

This paper proposes a reliable fault diagnosis model for a spherical storage tank. The proposed method first used a blind source separation (BSS) technique to de-noise the input signals so that the signals acquired from a spherical tank under two types of conditions (i.e., normal and crack conditions) were easily distinguishable. BSS split the signals into different sources that provided information about the noise and useful components of the signals. Therefore, an unimpaired signal could be restored from the useful components. From the de-noised signals, wavelet-based fault features, i.e., the relative energy (REWPN) and entropy (EWPN) of a wavelet packet node, were extracted. Finally, these features were used to train one-against-all multiclass support vector machines (OAA MCSVMs), which classified the instances of normal and faulty states of the tank. The efficiency of the proposed fault diagnosis model was examined by visualizing the de-noised signals obtained from the BSS method and its classification performance. The proposed fault diagnostic model was also compared to existing techniques. Experimental results showed that the proposed method outperformed conventional techniques, yielding average classification accuracies of 97.25% and 98.48% for the two datasets used in this study.

Fault feature extraction and classification based on WPT and SVD: Application to element bearings with artificially created faults under variable conditions

2016 ◽  
Vol 231 (22) ◽  
pp. 4186-4196 ◽  
Author(s):  
Mourad Kedadouche ◽  
Zhaoheng Liu
Keyword(s):  
Support Vector Machine ◽  
Singular Value Decomposition ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Transform ◽  
Singular Value ◽  
Support Vector ◽  
Rolling Bearings ◽  
The Matrix ◽  
Value Decomposition

Achieving a precise fault diagnosis for rolling bearings under variable conditions is a problematic challenge. In order to enhance the classification and achieves a higher precision for diagnosing rolling bearing degradation, a hybrid method is proposed. The method combines wavelet packet transform, singular value decomposition and support vector machine. The first step of the method is the decomposition of the signal using wavelet packet transform and then instantaneous amplitudes and energy are computed for each component. The Second step is to apply the singular value decomposition to the matrix constructed by the instantaneous amplitudes and energy in order to reduce the matrix dimension and obtaining the fault feature unaffected by the operating condition. The features extracted by singular value decomposition are then used as an input to the support vector machine in order to recognize the fault mode of rolling bearings. The method is applied to a bearing with faults created using electro-discharge machining under laboratory conditions. Test results show that the proposed methodology is effective to classify rolling bearing faults with high accuracy.

scholarly journals Determining damages in ceramic plates by using discrete wavelet packet transform and support vector machine

2020 ◽  
Vol 10 (5) ◽  
pp. 4759
Author(s):  
Mehmet Yumurtaci ◽  
Gokhan Gokmen ◽  
Tahir Cetin Akinci
Keyword(s):  
Support Vector Machine ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Experimental Equipment ◽  
Analysis Method ◽  
Discrete Wavelet Packet Transform ◽  
Cracked Plates ◽  
Svm Model

In this study, an analysis was conducted by using discrete wavelet packet transform (DWPT) and support vector machine (SVM) methods to determine undamaged and cracked plates. The pendulum was used to land equal impacts on plates in this experimental study. Sounds, which emerge from plates as a result of the impacts applied to undamaged and cracked plates, are sound signals used in the analysis and DWPT of these sound signals were obtained with 128 decompositions for feature extraction. The first four components, reflecting the characteristics of undamaged and cracked plates within these 128 components, were selected for enhancing the performance of the classifier and energy values were used as feature vectors. In the study, the SVM model was created by selecting appropriate C and γ parameters for the classifier. Undamaged and cracked plates were seen to be successfully identified by an analysis of the training and testing phases. Undamaged and cracked statuses of the plates that are undamaged and have the analysis had identified different cracks. The biggest advantage of this analysis method used is that it is high-precision, is relatively low in cost regarding experimental equipment and requires hardware.

Intellectual Gear Fault Detection Based on Wavelet Time-Frequency Analysis

2013 ◽  
Vol 373-375 ◽  
pp. 762-769 ◽  
Author(s):  
Juan Li Zhou
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Gear System ◽  
Support Vector ◽  
Rotating Speed ◽  
Time Frequency ◽  
Fault Recognition ◽  
Vector Machines ◽  
Gear Fault

In this paper, wavelet packet transform and support vector machines are used to detect gear system faults. Testing signals were obtained by measuring the vibration signals of gear system at different rotating speed for different faults. Vibration feature signals were analyzed using wavelet de-noising. By using wavelet packet transform (WPT), signals were decomposed into different frequency bands. the fault detection is used for calculation of energy percents of every frequency. All these were used for fault recognition using Support vector machine (SVM). SVM and neural network transform results were compared. The research indicates that the de-noised signal is superior to the original one. When dealing with various signals, such as Multi-Faults, the diagnosis identification rates are over 92%. This method can be effectively used not only in engineering diagnosis of different faults of gear system, but also for other machinery fault style classification.

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