At the core of any good predictive maintenance program there needs to be data. To determine when and how a machine fault may occur, it’s critical to examine what’s happened in the past. With the advent of IIoT and the massive surge in the predictive maintenance market, it’s undeniable that data collection is at an all-time high. While collecting data is vital going forward, the leaders of the predictive maintenance race will be those who utilize the information in the best way possible.
Machine learning algorithms provide an enormous opportunity to track data and make accurate predictions. Armed with connected sensors, these smart programs can continuously monitor factors like thermography, ultrasound, and electromagnetic emissions to track real-time conditions of the machines and improve the accuracy of diagnostic solutions. The massive benefit of machine learning programs is that as they advance and more data is collected, there will be a higher degree of accuracy when anticipating disrepair allowing technicians immediate access to maintenance suggestions.
For a predictive maintenance program to be effective, everyone involved needs to be thoroughly trained in how to collect, interpret, and implement the data and information that the device provides. As programs advance, we’ll be able to utilize machine learning to find areas of improvement in the data collection process itself so that the technicians can respond accordingly. These programs won’t remove the need for skilled workers, but they will improve the relationship between the human and the machine.
When a company introduces a predictive maintenance program, it’s critical to consider the unique needs and challenges of the facility and machines. Some companies want to minimize downtime, while others focus on reducing maintenance costs. Identifying the specific priorities and goals make the implementation of a predictive maintenance regime significantly smoother and more effective. Any good plan has a good foundation. You must understand not only what you will measure and how, but you must also know why. Fortunately, machine learning enables a much higher degree of flexibility as the program adapts based on the criteria given.
With more and more machines being monitored by devices with specific parameters, such as vibration monitoring, temperature monitoring, and ultrasound, the popularity and value of IIoT has never been higher. Over 60% of manufacturers are committed to investments in IIoT products with an estimated expenditure of $267 billion by 2020. If not prepared, some will find it challenging to keep all the data straight. However, machine learning algorithms are becoming more efficient at identifying the relationship between different metrics and the various components involved in the entire production line.
GTI Predictive offers predictive maintenance platforms and applications that helps manufacturers avoid downtime and maximize profit.
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.