کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
560222 1451869 2015 23 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Application of the largest Lyapunov exponent algorithm for feature extraction in low speed slew bearing condition monitoring
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Application of the largest Lyapunov exponent algorithm for feature extraction in low speed slew bearing condition monitoring
چکیده انگلیسی


• This paper presents a new application of the largest Lyapunov exponent (LLE) algorithm for feature extraction.
• The increase deterioration level of slew bearing is detected.
• The comparison between LLE feature and time-domain features and EMD is reported.
• The most important LLE parameters namely reconstruction delay J is selected based on the FFT.

This paper presents a new application of the largest Lyapunov exponent (LLE) algorithm for feature extraction method in low speed slew bearing condition monitoring. The LLE algorithm is employed to measure the degree of non-linearity of the vibration signal which is not easily monitored by existing methods. The method is able to detect changes in the condition of the bearing and demonstrates better tracking of the progressive deterioration of the bearing during the 139 measurement days than comparable methods such as the time domain feature methods based on root mean square (RMS), skewness and kurtosis extraction from the raw vibration signal and also better than extracting similar features from selected intrinsic mode functions (IMFs) of the empirical mode decomposition (EMD) result. The application of the method is demonstrated with laboratory slew bearing vibration data and industrial bearing data from a coal bridge reclaimer used in a local steel mill.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanical Systems and Signal Processing - Volumes 50–51, January 2015, Pages 116–138
نویسندگان
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