Predictive Maintenance: Optimize Efficiency & Grow Revenue
Since the dawn of the industrial revolution, maintenance engineering has aimed to reduce downtime and maximize efficiency. Equipment maintenance has especially come a long way within the past decade. Data science is poised to play a major role in the evolution of maintenance as we enter the age of Industry 4.0 and IoT. The first companies to figure out how to automatically convert their vast data into actionable insights will gain a huge competitive advantage. In a recent webinar, RapidMiner’s Leslie Miller and Jeff Chowaniec discussed how to leverage machine learning and data science on your available manufacturing or operations data to maximize operational productivity, build highly efficient maintenance services and reduce the costs of downtime & repairs.
Evolution of Maintenance
Engineers used to focus on reactive maintenance: every time something would break they would fix it. This approach leads to unexpected downtime – a real problem for highly critical equipment – so engineering teams adopted a preventative, or scheduled maintenance approach: repairs based on a fixed calendar date regardless of the condition of the equipment at the time. While this minimizes equipment downtime, it can also lead to unnecessary costs and engineering time.
Fast forward to today and many engineers are starting to adopt predictive maintenance, a form of maintenance which uses data science to predict the optimal time to conduct repairs and replacements, balancing out the need for repairs with minimizing costs.
Putting All those Data Points to Use
Machines and equipment are increasingly connected with ever more sophisticated data gathering methods, generating billions of data points every year. Think of some of the things we have out there: infrared tomography, sonic and ultrasonic, analysis, motor current analysis, vibration analysis, oil, just to name a few.
But while we are gathering all these data, according to Gartner, 72% of the manufacturing industry’s data is unused due to the complexities involved in today’s systems & processes. As the amount of data we collect continues to grow, it becomes increasingly difficult for humans to extract meaningful insights.
What humans need is data science. Data science uses machine learning algorithms to automatically comb through vast amounts of data to point towards what is relevant, what is interesting, and what is valuable. Predicting when repairs will be needed drives efficiency in your maintenance process and opens up new possibilities for your company.
Predictive Maintenance and Your Business
In today’s global economy, even minor differences in efficiency and productivity can determine which companies thrive and which ones fail. Maintenance engineering is one area that can make a huge difference to both the top line productivity, and also to the bottom line efficiencies.
Data science can find the critical signals hidden in the “noise” of your operational maintenance and inspection data. It can automatically pinpoint deviations that identify the probability of damage or wear and tear that will lead to partial or complete machine failure. You can predict when, where and why failures are likely to occur. That means no more guesswork for your engineers.
Leading Airline Uses RapidMiner for Predictive Maintenance
RapidMiner has helped implement predictive maintenance in many companies across a variety of industries. Read our recent case study with Lufthansa and learn how the global airline uses RapidMiner to predict and prevent failures of components and devices in order to reduce out of service times and associated costs.
Get Started with Predictive Maintenance Today
Download RapidMiner today and get started with the new stage of maintenance: Predictive Maintenance.