Automotive Science and Engineering
Automotive Science and Engineering
ASE
Engineering & Technology
http://www.iust.ac.ir/ijae
18
agent2
2717-2023
10.22068/ase
en
jalali
1394
12
1
gregorian
2016
3
1
6
1
online
1
fulltext
fa
Studying Influence of Preheating Conditions on Design Parameters of Continuous Paint Cure Ovens
موتور احتراق داخلی
Internal Combustion Engines (ICE, ...)
پژوهشي
Research
<p class="keyword" style="margin: 0in 0in 10pt; text-align: justify; text-indent: 0.5in; text-justify: inter-ideograph;"><span style="line-height: 115%; font-size: 10pt; font-style: normal; mso-bidi-font-size: 12.0pt; mso-ascii-font-family: " times="" new="" roman";="" mso-hansi-font-family:="" "times="" mso-bidi-language:="" fa;"=""><font color="#000000"><font face="Times New Roman">This paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (LSSVM). Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction. The fault diagnosis method consists of three steps, firstly the six different base wavelets are considered. Out of these six wavelets, the base wavelet is selected based on wavelet selection criterion to extract statistical features from wavelet coefficients of raw vibration signals. Based on wavelet selection criterion, Daubechies wavelet and Meyer are selected as the best base wavelet among the other wavelets considered from the Maximum Relative Energy and Maximum Energy to Shannon Entropy criteria respectively. Finally, the gearbox faults are classified using these statistical features as input to LSSVM technique. The optimal decomposition level of wavelet is selected based on the Maximum Energy to Shannon Entropy ratio criteria. In addition to this, Energy and Shannon Entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. Some kernel functions and multi kernel function as a new method are used with three strategies for multi classification of gearboxes. The results of fault classification demonstrate that the LSSVM identified the fault categories of gearbox more accurately with multi kernel and OAOT strategy.</font></font></p>
radiation oven, dynamic optimization, radiation heat transfer, paint cure window
2075
2081
http://www.iust.ac.ir/ijae/browse.php?a_code=A-10-63-114&slc_lang=fa&sid=1
Z.
Baniamerian
`180031947532846002300`

180031947532846002300
Yes
Assistant Professor, Department of Mechanical Engineering,