A NEW METHOD OF SYMPTOM BASED PROCESSING FOR MACHINERY CONDITION RECOGNITION AND FORECASTING

This paper considers a new method of construction of the average sympt om life curve and symptom reliability for machine condition monitoring , as an alternative to the statistical elaboration of these machine ch aracteristics. It was found that by the sorting up operation of the sy mptom data and extraction of equal symptom readings, similar recogniti on and forecasting abilities can be obtained, as in statistical Weibul l and Frechet characteristics. It is shown that the new method is also good for self-diagnosis, (i.e. self-recognition of a symptom life cur ve) based on a small set of symptom readings taken during monitoring o f a given object. Such a direct construction method of diagnostic char acteristics may be of great value in cases where not enough data for r eliable statistics are available. This is usually the case at the star t of condition monitoring of some machinery set, or critical object.