By: Musab Mohamud
Predictive maintenance is defined by Rockwell Automation as “The use of data-driven, proactive maintenance methods that are designed to analyze the condition of equipment and help predict when maintenance should be performed.” It is usually coupled with A.I. technology and is rooted in data. “The main function of predictive maintenance is on oil rigs and extractors. However, some examples of using predictive maintenance and predictive maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation”. The importance of A.I. technology to oil rigs and their safety is visible in the reduction of failures of equipment and work injuries.
Predictive maintenance is necessary to minimize losses caused by “downtime”, which for oil companies results in massive losses which can quickly stack up, resulting in the loss of hundreds of millions of dollars. Due to the prices of energy sources decreasing, data analysis is even more important. Energy companies must be wary of losses and need to stay at a profit; predictive measures help greatly with that.
BP, a British oil company, partnered with GE (General Electric), to place sensors on 650 of their extraction wells. A program created by GE helps predict failures in BP’s system. BP and GE’s partnership has continued to evolve and keep employees safe through predictive maintenance, which improves the safety and economic state of the company.
Data tags and sensors placed on and around oil fields record data and send them back to the analytical companies. This helps the oil companies predict when failures will occur in the future. The two sides work as a symbiotic relationship; the data companies help prevent failures, and they in turn are paid for their services.
“Predictive maintenance programs have been shown to lead to a tenfold increase in ROI (return of investment), a 25%-30% reduction in maintenance costs, a 70%-75% decrease in breakdowns and a 35%-45% reduction in downtime”, according to Rockwell Automation. With the added security of predictive maintenance, companies can spend funds elsewhere after saving part of their maintenance costs.
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