Five ways to accelerate predictive maintenance
Organizations around the world are often flooded with “big data” buzzwords that detail how to better use customer data to drive business decisions. But what does this mean for the industrial sector, where data issues are often less understood by the general public? How are emerging technologies legitimately addressing industrial challenges such as preventative maintenance, machine-asset uptime and other real-word industrial problems? Lastly, what can engineers and analysts do to get better answers from their data?
As industrial operations continue to mature, customers in manufacturing, oil and gas, transportation, energy and utilities are challenged with making sense of industry trends and determining how and why they apply to their day-to-day activities. Here are five key technology trends Industry 4.0 is talking about today that are making manufacturers rethink planned versus unplanned maintenance.
Internet of things
The commercialization of IoT is rapidly decreasing costs and opening up the opportunity to not only create, but also correlate new data streams with existing industrial data. By retrofitting machine-assets with inexpensive sensors, then correlating sensor data with traditional industrial control systems, operators are able to monitor the conditions of assets in real time, shifting their maintenance strategy from reactive to proactive.
Big data comes in many different shapes and forms and for most, it is challenging to discern real business value from large volumes of data across IT, OT and IoT systems. Data is often isolated across islands of information with no shared understanding. Emerging big data technologies are simplifying the process of correlating and combining data from alarm management systems, for example, with real signals coming from equipment, providing a holistic view across multiple sources. This new wave of big data for industrial operations enables engineers to troubleshoot issues in a fraction of a second, and moves the organization closer to predictive maintenance and zero unplanned downtime.
Machine learning and advanced analytics
New statistical methods and algorithms allow engineers and statisticians to use large volumes of historical and real-time information to calculate and predict outcomes in ways impossible or impractical for humans alone. Data science and machine learning are opening new opportunities to provide objective investigation of asset data in order to detect novel conditions, make previously unknown connections between assets and data sources and, as a result, forecast future performance with astounding accuracy.
Artificial intelligence is no longer science fiction — it’s real. Manufacturers can use AI to move beyond predicting maintenance needs and into forecasting trends and business requirements. Beyond accurately predicting the time, location and reason for future failure, AI holds the promise of being able to recommend the most effective mitigation of potential failures and provides the foundation for autonomous maintenance and repair.
Augmented reality (AR) has the most impact on the way maintenance technicians and operators interact with assets in the field. In the past, operators were required to bring static information like technical and procedure documentation to the job site. Now, mobile technology gives the mechanic real-time access to the same information, but the hands-on interaction is prone to human error. AR technologies give mechanics hands-off maintenance-related information such as audio and video. This real-time data provides a new dimension of problem detection, changing maintenance strategies on the fly.
As industrial businesses continue to embrace machine data, they should look at technology that is most relevant to their business outcomes. With minimizing unscheduled downtime and advancing maintenance strategies top of mind for industry, analysts and engineers will better serve their business if they are able to communicate these trends and clearly demonstrate the long-term business value each provides.
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