Health Diagnosis of Industrial Equipments through Used Lubricant Analysis Process: A Rule Based Inference Approach

Abstract

Any real-time engine diagnostic monitoring system deals with fault and off-nominal conditions as they are occurring. But prognostics are the ability to assess the current health of the machinery and predict the future health for a fixed time horizon or predict the time to failure of the machinery. Fault diagnosis of industrial machineries improves the quality of manufacturing as well as to reduce the cost for product testing. In modern manufacturing environment, a fast and reliable diagnosis system has turned into a challenging issue in the complex industrial atmosphere. In the present work, the diagnosis of bearing system has been considered as the platform of health monitoring system by used lubricant analysis process. Rule based inference system has been adopted in the present work, where possible characterized wear particles from the bearing system have been interpreted into the fuzzy inference mechanisms. Based on the proposed strategy, the possible maintenance policies have been taken into consideration.