Condition monitoring system for process industries a business approach

In this paper, condition monitoring system (CMS) applications to process machines are presented. The benefits of deploying intelligent fault diagnostics techniques on machines increase productivity and ultimately profits for the business. Stork dairy filling machine is used for implementing the CMS plan. The performance and effectiveness of different fuzzy inference system on the machine is also evaluated. First fuzzy system is described for a case study of a predicted fault and then results from online neuro-fuzzy time series based algorithm is shown to detect anomaly and future faults in the drive train of the Stork liquid filling machines. The methods used in this paper have shown the promising results on the real time data to find any variation in asset health over varying speeds of the machine. The results from a gearbox failure analysis are also presented in the paper.