In today’s economic climate, pushing the efficiency of refineries to the limit is a must. Operations take a huge hit from maintenance costs and asset failure. It’s no wonder companies are looking to refine their approaches to condition- based maintenance.
Is the industrial “Internet of Things” (IoT) the industry’s greatest hope for optimizing preventive maintenance?
Technology is changing business rapidly — in profound ways. And leaders in industrial sectors have a front-row seat: Estimates show industrial data will make up 50 percent of all digital data by 2020.
The game-changing potential for all this data is profound. Analytics can serve as a crystal ball for asset health. This technology provides historical and real-time performance data, maintenance records, inspection reports and environmental data to correlate performance factors that can inform critical decisions across your operations.
What exactly does this look like?
The concept of condition-based maintenance (CBM) may have been around since 1975, but data analytics has the potential to elevate CBM to new heights. Referred to as “predictive maintenance,” brilliant self-learning programs can analyze data from sensors on equipment to better identify critical issues, allowing maintenance teams to fix machines just before they break. This allows operations to scale back costly and disruptive maintenance schedules and find better balance between CBM and time-based maintenance.
Not only can these programs identify issues, but they can improve first-timefix rates and reduce repair time by providing specific repair recommendations. Imagine every maintenance technician on your team becoming your best technician.
Predictive maintenance also introduces powerful performance modeling, allowing companies to detect degradation in performance at an early stage and prepare a maintenance plan. Niche solutions targeted to petrochemical companies are packaged to measure key performance indicators (KPIs) like gross average production, operating cost per barrel, operating cash flow, total shareholder return and greenhouse gas emissions. These KPIs are then compared with ideal performance data to generate an overall performance assessment. The effectiveness of your operations is measured at regular intervals as powerful, adaptable algorithms continue to evaluate changes in asset performance.
While the potential is great, IoT technology is still emerging. Technology development thrives in an “iron sharpening iron” framework. When a heavy hitter drops out of the game, advancement can lose steam.
Earlier this year, IoT frontrunner GE announced a major pivot to scale back its digital division. The scope of GE’s digital ambitions was widely broadcasted in 2015, when the company set up GE Digital as its own business within the industrial conglomerate. Jeffrey R. Immelt, then chief executive, declared a goal to become a “top 10 software company” by 2020. Today, those ambitions look different, marked by G E’s decision to put parts of GE Digital up for sale.
Whether the future of maintenance is data-driven is not a foregone conclusion. We are just at the beginning of this transformation, and there will continue to be barriers to break and obstacles to overcome. Companies will need to invest in the new technologies, and organizations and managerial practices will need to adapt.
But for everything in front of us, the potential for data analytics to shape the industry is boundless. One thing is certain: We are in a data-driven revolution.
For more information, call Camille Curry-Theis at (832) 459-0055 or email her at Camille@MahaffeyUSA.com.
