Streamlining asset maintenance throughout analysis of its usage data

Recently, with the advent of emerging technologies such as radio frequency identification (RFID), various sensors, and wireless telecommunication, we can have the visibility of asset status information over the whole asset lifecycle. It gives us new challenging issues for improving the efficiency of asset operations. One of the most challenging problems is the predictive maintenance that makes a prognosis of the asset status via a remote monitoring, predicts the asset's abnormality, and executes suitable maintenance actions such as repair and replacement. In this study, we will develop a prognostic decision algorithm to take suitable maintenance actions by analyzing the degradation status of an asset. To evaluate the proposed approach, we carry out a case study for a heavy machinery.