A Study of Non-stationary Signal Processing for Machinery Condition Monitoring
نویسنده
چکیده
The University Repository is a digital collection of the research output of the University, available on Open Access. Copyright and Moral Rights for the items on this site are retained by the individual author and/or other copyright owners. Users may access full items free of charge; copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational or notforprofit purposes without prior permission or charge, provided:
منابع مشابه
Application of Signal Processing Tools for Fault Diagnosis in Induction Motors-A Review-Part II
The use of efficient signal processing tools (SPTs) to extract proper indices for the fault detection in induction motors (IMs) is the essential part of any fault recognition procedure. The 2nd part of this two-part paper is, in turn, divided into two parts. Part two covers the signal processing techniques which can be applied to non-stationary conditions. In this paper, all utilized SPTs for n...
متن کاملOrder Bi-spectrum For Bearing Fault Monitoring and Diagnosis Under Run-up Condition
Varying speed machinery condition detection and fault diagnosis are more difficult due to non-stationary machine dynamics and vibration. Therefore, most conventional signal processing methods based on time invariant carried out in constant time interval are frequently unable to provide meaningful results. This paper deals with the detection of bearing faults in gearbox under non-stationary run-...
متن کاملA Time-Frequency approach for EEG signal segmentation
The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful ...
متن کاملAdaptive Segmentation with Optimal Window Length Scheme using Fractal Dimension and Wavelet Transform
In many signal processing applications, such as EEG analysis, the non-stationary signal is often required to be segmented into small epochs. This is accomplished by drawing the boundaries of signal at time instances where its statistical characteristics, such as amplitude and/or frequency, change. In the proposed method, the original signal is initially decomposed into signals with different fr...
متن کاملReal-time damage detection of bridges using adaptive time-frequency analysis and ANN
Although traditional signal-based structural health monitoring algorithms have been successfully employed for small structures, their application for large and complex bridges has been challenging due to non-stationary signal characteristics with a high level of noise. In this paper, a promising damage detection algorithm is proposed by incorporation of adaptive signal processing and Artificial...
متن کامل