Contribution of oblique multiresolution analysis and generalized likelihood ratio to partially cooperative recognition.

Summary The monitoring of rotating machines of the edf nuclear power plants requires the identification of the shape of the transients of the vibration signals. The assignment of a shape is done relative to a reference set and is independent of the parameters of scale, amplitude, baseline and arrival time. The purpose of this thesis is to automate this pattern recognition procedure. Two approaches are proposed to solve this problem. A direct approach based on the generalized likelihood ratio. Its advantage is that it allows not only to recognize the shape of the transient, but also to estimate its unknown parameters. Its major drawback is that it requires a simple mathematical model of the shape. The second approach is based on a hierarchical decision tree. It uses two multiscale detectors as well as algorithms for parameter jump detection and line detection in a spectrum. These algorithms take into account the fact that the processed vibratory signals are sampled with a random step. The first multiscale detector is based on the extremum coding of the wavelet decomposition. It uses a redundant oblique multiresolution analysis, which is an extension of the one introduced by s. Mallat and is defined in this thesis. The second detector uses the generalized maximum likelihood technique to decompose the observed signal on four reference wavelets. The performance of these detectors is evaluated using horn curves. The hierarchical approach for pattern recognition is validated on a set of synthetic signals and evaluated on a set of real signals.