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Rolling bearing performance degradation assessment has been receiving much attention for which itscrucial
role to realize CBM(condition-based maintenance).This paper proposed a novel bearing performance degradation method
based on TESPAR(Time Encoded Signal Processing and Recognition)and GMM(Gauss Mixture Model). TESPAR is
used to extracted features which constitute A-matrix. GMM is utilized to approximate the density distribution of singular
values decomposed by A-matrix. TENLLP(Time-Encoded Negative Log Likelihood Probability) serves as a fault severity
which can display the similarity of the singular values between normal samples and fault samples as quantificational.
Results of its application to bearing fatigue test show that this performance degradation assessment can detect the
incipient rolling bearing fault and be sensitive to the change of fault.