Reliability engineers have an important role in the modern manufacturing workplace. Everything from end-user safety, to maintenance cost reduction, falls under the domain of the reliability engineer. While the job dates back to the 1920s at Bell Labs with the introduction of statistical process control, the advent of modern computers has undoubtedly advanced the accuracy and opportunities within the field. Just as it was essential for reliability engineers to familiarize themselves with computer modeling in the past few decades, it will be just as necessary for specialists in this field to become experts of predictive maintenance tools and techniques going forward.
IIoT Enabled Devices
The heart of predictive maintenance has always been the programs that analyze machine performance data, but the fact remains that the collection of this data would be far less accurate and efficient without the automated devices that have been designed to communicate with each other on the Industrial Internet of Things (IIoT). Everything from vibration analysis, to lubrication levels, and temperature data can be recorded, tracked, stored, and analyzed through cloud computing tools that are connected to both permanently mounted devices and user-controlled equipment. Thanks to this network of sensors and computers, reliability engineers can monitor equipment health from anywhere in the world, effectively giving them a staff of technicians on call 24/7.
Real-Time Data Monitoring & Failure Alarms
This data is only useful if it is used to optimize workflows and improve production. Fortunately, reliability engineers can monitor real-time machine health, and track data overtime to make more accurate predictions about performance and repairs. Failure alarms can be programmed to respond to everything from concerning vibration analysis data, and irregular temperature readings to low lubrication levels. While OEM’s do offer valuable guidelines about their equipment, and there is a great deal of predictive data available, the fact remains that all machines perform differently based on operating conditions. When the tools available are used as part of an overarching predictive maintenance plan, reliability engineers are better able to plan for downtime to reduce repair costs and maximize the life of their machines.
Cloud computing & Artificial Intelligence
Cloud computing provides the opportunity to store data long-term for a wide variety of uses. This is especially valuable in larger companies with tens-of-thousands of hours of machine time to track. Cloud computing removes the legwork of consolidating and storing information. Furthermore, artificial intelligence programs can significantly streamline the process of data management. Machine learning systems are capable of analyzing massive amounts of data and outputting recommendations and predictions about machine health. Every manufacturer needs to take advantage of this technology as soon as possible to begin data collection for future applications. From supply chain management to labor-force optimization, the future possibilities are endless, but the data must be collected first.
Contact the professionals at GTI Predictive Technology today to take the first steps to start your predictive maintenance program.
About GTI Predictive Technology
At GTI Predictive Technology, it is our mission to provide the best predictive tools on a single iPad platform with services to monitor nearly any asset. We strive to bring our customers the portability, connectivity, and affordability offered by the latest available technologies. Our product family combines wireless portable and online vibration data collection and analysis, balancing, shaft alignment, thermography, and ultrasound into an affordable and completely scalable solution on one simple to use platform. GTI’s predictive technology apps feature many additions that have come directly from customers. Click here to learn more.