Development of a condition-based maintenance model for grease lubrication : a case study of a paper mill

Abstract

This research highlights a vital challenge in optimization of grease usage as a lubrication in the industry through a development of condition-based maintenance model for grease lubrication throughout a case study of a paper mill. Even though application of grease is found to be a minor task, grease as a lubrication plays a major role in the machinery’s life span, increases the efficiency and reliability of machine elements and optimize maintenance cost as well. The inappropriate grease lubrication causes lot of interruption to production and factory output as well. Old traditional grease application methods required skilled manpower continuously for inspection, application and maintenance. This model has changed this situation to condition based grease application method with the supervision of measurable physical parameters for effective lubrication management. This research highlights the relationship between the measurable physical parameters such as temperature, vibration and noise level and the condition of grease in the machine elements throughout the case study in a paper manufacturing factory. Data collection is done at twenty different spots in different machine elements. These spots are continuously observed with the condition of grease level. Temperature is measured using infrared thermometers, vibration is measured using smart based vibration application and sound level is measured using sound level meters for acoustic pattern monitoring. These provide reliable data with the usage of grease how the parameters change. This has given a clear basis for model creation. The methodology used in this research is based on the physical condition of the machinery with the lubrication levels. Data analysis done which converts the inputs to understandable outputs which is then Processed using data visualization tool which showed a clear pattern in trend what happens with the condition of grease and refilling of grease. With the pattern the threshold level of all parameters is selected. Then the model is developed accordingly using python language. This leads to the successful completion of model. This condition-based maintenance model is designed based on the data collected by the sensors in different spots which is continuously monitored and analyzed. This changes the fixed schedule grease application method to condition based grease method which alarms and inform the manpower to apply grease which prevent the machineries failures. This research contributes to bridge the problem caused by the application of grease and maintenance work and the health of machines which is increased by the grease application at correct time reducing machinery failures by successfully developing a condition-based maintenance model in pilot stage. This also provides a foundation for future work which can be enhanced and advanced for fully automated systems with machine learning technology and increasing accuracy in data collection and processing as the way to Industry 4.0.