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in this example weamp;#39;re going to see how seekamp;#39;s prediction tool can be utilized in predicting heat exchanger maintenance events using historical data the premise here is that heat exchangers begin to fall over time which leads to reduced efficiency therefore we need to take the unit offline for cleaning historically you may have done this on a regular schedule but this could mean youamp;#39;re taking the unit offline more frequently than needed alternatively you may have waited too long and had to cut flow rate due to unplanned shutdowns which can eat up precious time and become very costly so in this example we need to figure out how to monitor and quantify a degradation in performance and use that to predict time until failure so we can schedule maintenance ingly to minimize downtime in the long run our solution involves a predictive model based on multiple operating conditions as new operational data becomes available the model can be retrained in order to give an upd
