Accession Number : ADP010196

Title :   Multiscale Statistical Modeling Approach to Monitoring Mechanical Systems

Corporate Author : JOINT OIL ANALYSIS PROGRAM PENSACOLA FLTECHNICAL SUPPORT CENTER

Personal Author(s) : Chou, Kenneth C.

PDF Url : ADP010196

Report Date : APR 1996

Pagination or Media Count : 11

Abstract : Signal processing for condition based maintenance and equipment monitoring has focused in recent years on non-stationary signal analysis using time-frequency representations of the signal. These representations are used to identify non-stationary events in the signal that indicate some change in the state of a structure or a machine. It is important to be able to reliably detect such changes in real time to do necessary preventive maintenance and also to minimize unnecessary maintenance. While transformations such as the Wigner-Ville, Gabor, and wavelet transforms are useful in highlighting time-frequency features of the signal, the application of such transforms to the monitoring problem requires additional for making decisions concerning the condition of the object being monitored. In particular, the interpretation of the transform coefficients in terms of physical events is essential to making such decisions. We develop a methodology for identifying the physical state of the object based on statistical models of the signals, which could comprise, for example, multiple outputs from devices such as accelerometers, strain sensors and acoustic emission sensors. Classification of machine states based on monitoring signals is performed by comparing likelihood scores for each machine state. We present examples of applying our system to various data, including damped sinusoids and noisy chirps, as a way of illustrating system performance for the case of transient monitoring signals. We compare our system to one which is trained using a DFT-based (non-time-frequency-based) representation (in particular, LPC coefficients) and show that our system exhibits both superior performance as well as greater robustness to noise in the signals.

Descriptors :   *SYMPOSIA, *STOCHASTIC PROCESSES, *PREVENTIVE MAINTENANCE, SIGNAL PROCESSING, FREQUENCY, REAL TIME, TIME, STATISTICAL ANALYSIS, WAVELET TRANSFORMS.

Subject Categories : Statistics and Probability
      Machinery and Tools
      Logistics, Military Facilities and Supplies

Distribution Statement : APPROVED FOR PUBLIC RELEASE