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ch are comparable with 1 component full rank covariance GMM model. The performance of 2 components GMM with diagonal covariance matrices are indicated in gure 5 () with the solid (blue) line. The NLL values are seen to be sensitive to the operating conditions. The second approach implements GMM with full rank covariance matrices. Full rank covariance matrices render it possible to model the interdependencies between different datum points in signal segment. Even single full rank covariance component performs quite well in this simple analytical example. The dashed line in gure 5 () indicates the NLL values as obtained with 2 component GMM with full rank covariance matrices. The third and nal approach investigates the use of conditional NLL values. To compute any single conditional negative log likelihood value ln[(si;ci)=(ci)]it is required to estimate two GMM density distributions, one which represents the joint density distribution (si;ci)for the signal segments si;fori=1::rNand the operating condition variable() ci;fori=1::rN, and second density distribution (ci)which represents the operating conditions. Only single operating condition variable is investigated, so that the vector cireduces to variable ci. The pinion angular velocity is used as operating condition variable , since it is known to be indicative of the amplitude and phase modulation. Both density distributions are estimated with 2 component GMM models with full rank covariance matrices. Figure 5 () indicates the computed NLL for the operating conditions. It is seen that the NLL values for the operating conditions which correspond to the rst 0.4s generally have large values. This shows that the rst 0.4s of operating conditions are not well representative of the typical operating conditions. large spike is observed at the end of the 0.4s. This represents the transition from one operating condition to the other. The large spike indicates that this transitional operating condition is very unco