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Most of the existing time series methods of feature extraction involve complex algorithm and the extracted features are affected by sample size and noise. In this paper, a simple time series method for bearing fault feature extraction using singular spectrum analysis (SSA) of the vibration signal is proposed. The method is easy to implement and fault feature is noise immune. SSA is used for the decomposition...
Most of the time domain methods for bearing condition monitoring are machine and load dependent or involves complex mathematical calculations or need of training the algorithm. To overcome the above issues, symbolic dynamics based method is proposed. The time series vibration data is converted into symbolic series from which dictionary of the signal is constructed. Common Signal Index (CSI) a parameter...
A novel method to diagnose the bearing fault is presented. The proposed method is based on the analysis of the bearing vibration signals using Singular Spectrum Analysis (SSA). SSA is a non-parametric technique of time series analysis that decomposes the acquired bearing vibration signals into an additive set of time series to extract information correlated with the condition of the bearing. Information...
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