Developing a Total Productive Maintenance Framework with RAM (Reliability, Availability and Maintainability) Techniques in Gas Compression Plants

Abstract

The gas compression plant is one of the major unit in gas industries and always classifies as critical and hazard zone due to high surrounding processing parameters with associated risks. Thus, such plants are designed and constructed through high competent technical entities with high values. Investment in gas industries is always attractive due to continuous demand of gas and its products. This study is focused on a new gas compression plant consist of three identical compression units recently commissioned in year 2015 but high failures were recorded considering it was well designed and constructed from reputed entities having international testing and manufacturing certificates. Nevertheless, the working team are following the manufacturer‘s maintenance recommendations and common maintenance practices. Gas compression loss leads to have power trips from gas turbines as well affect the power generation at the nearby villages. The study is conducted for single unit (A) along the duration from May 2015 to April end 2019 (1,460 days as total time) with loading time equal to 19,500 running hours (812.5 days). In this period, the recorded failures were 34 caused 460 days unplanned down time. A new maintenance proposed framework was built to apply a new integrated maintenance frame work. This proposed framework was assessed by using of RAM and OEE % techniques from July 2019 to May 21, 2020. Forty five days were the total down time that contained thirteen failures and the gained results showed clear improvement in the unit (A) availability from 68.5% to 86.2% as well there was an improvement in the overall equipment effectiveness (OEE%) from 48.7% to 60.44%. The research offered clear constructive new method on how to rank a list of failures based on their contribution into the total down time. This new technique is known as the total down time (TDT) importance and also improved the key performance indicator for the large maintenance sectors.