Predictive maintenance is undoubtedly one of the foremost deeply divisive topics in asset management and maintenance. I investigated numerous publications, whitepapers, and analysis methods so on critically assess the facts and certain outputs. We used a paradigm that identifies four degrees of predictive maintenance maturity to look at the instant practices. This is the aim at which the digital revolution collides with routine maintenance. Fronted with fierce global competition, every sector is continuously trying to take care of equipment efficiently and effectively to fulfill planned production and productivity standards. As a result, it's even more critical to gauge equipment performance. With this in mind, this study uses KS Probability tests and convolutional neural network (CNN) model of a load haul dumper (LHD) to forecast the share of reliability, availability, and preventative maintenance schedules.