We often meet product development requirements which need to analyse, interpret or transform data from different business sources. Most manufacturing industries use their product manufacturing purposes for high-cost equipment. The impact of a failure cannot be afforded and production will be suspended. The machine is subject to preventive maintenance, often time-in-service inspections and repairs, to avoid such an enormous loss. The challenge of proper planning is increasing with the complexity of machinery: working together and effecting the lives of one another in a system with several components. Predictive maintenance is aimed at developing models that evaluate the risk of failure of a machine at any time and use these analytics to enhance the maintenance schedule. The achievement of predictive maintenance frameworks relies on various principal components: that the right data are available, that the problem is properly framed and predictions are evaluated appropriately. This paper will provide insights on how to choose the best modelling technique for the daily usage, service life of the machine and how to predict certain breakthrough points.