Singularities Detection System Design for Automatic Analysis of Biomedical Signals and Machine Condition Monitoring and Fault Diagnostics

Abstract

Most important information about nature of a signal is often carried by the singularity points, such as the peaks, the discontinuities, etc. Moreover, at the moment when specific event occur, the output signals usually contain jump points that often are singularity points. Therefore, singularity detection has played an important role in signals processing, biomedical, e.g. ECG/EEG event detection, machine condition monitoring and fault diagnostics, etc. The wavelet modulus maxima method has been widely used method for the detection of singularity points. A review of the literature shows that the mentioned method may give very high efficiency in detection of events in the noisy ECG/EEG waveforms and also exist many application of the wavelet in machine fault diagnostics. Actually, both types of systems can be called CBMS - Condition Based Monitoring Systems. Because, we monitor the digital signal for search the specific events in both cases. The paper presents the proposal of system design for biomedical signal and vibration analysis and monitoring of machines, based on the Mallat and Hwang wavelet singularity analysis.KeywordsStructural Health MonitoringContinuous Wavelet TransformModulus MaximumBiomedical SignalDyadic WaveletThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.