The recharge is the focus of data analysis of the different situations with the integration of the system and development of the two-stage 2/2 proportional cartridge valve for the steel industry machine. Using the statistical analysis technique to visualize the valve signal data behavior identify the accuracy of the machine data and apply the statistical feature extracting model using classification and clustering algorithms of real-time data analysis for the manufacturing. The fundamental principles of data analysis with a particular emphasis on its key function in the collection, cleansing, and analysis of substantial amounts of data to develop significant insights. Moreover, we explore the importance of data visualization in effectively presenting intricate research outcomes. We get the data accuracy of 76 percent for train and test set data in the statistical analysis feature indicating the best accuracy in the early stage. Our model gives high accuracy of the recommendation data automation system of the steel industry. Analysis of the valve data in multiple ways for the predictive maintenance of conditional monitoring of the tubes mail production machine. PdM is used for data processing of predictive manufacturing, behavior patterns of machines data, and correlation of statistical model for decision making for the maintenance activity avoiding downtime.  The data consists of different channels in the steel industry machine. Some automation process is used for the feature combination of the analysis of valve data in industry between each feature and signals. Using a dataset comprised of sensor data, operation logs, and maintenance records industrial control data of machines and use of this predictive model has the potential to yield significant cost savings for the steel industry through the prevention of unplanned maintenance, while also enhancing operational safety manufacturing of machine in the industry.