The chatter around IoT analytics has reached a fever pitch and there is a very good reason for it. In a recent study from McKinsey Global Institute, 69% of decision makers believe Industrial Analytics will be crucial for business success in 2020. In addition, a new study conducted by IoT Analytics GmbH found that “predictive and prescriptive maintenance of machines (79%) is the most important application of Industrial Analytics in the next 1 to 3 years,” reports a recent Forbes article.
Here’s the great news: With the right IoT analytics strategy, industrial manufacturers can capitalize on the opportunity to generate business insights from data, implement safeguards against failures and downtime, and make improved product decisions using predictive analytics enabled by machine learning powered by ThingWorx.
Jordan Reynolds, a machine learning expert at ThingWorx partner and management consulting firm Kalypso, walks you through a hands-on demo using sensors on a spinning wind turbine:
You can take a deeper dive into this use case by watching Kalypso demonstration video. In it, the use case of a wind farm is used to show how analytics can assist with operational performance and predictive failure of equipment in the field. The ability to monitor, manage, and analyze the data from this wind farm leads to better insight and improved decision-making for operations, as well as assists the business with long-term planning. As noted, “the data can also be used by the equipment manufacturer, helping R&D and product engineers understand the circumstances under which failure is likely, so they can make improvements to equipment or develop products that will minimize instances of failure in the future.”