Strategies for a Successful IoT Predictive Maintenance Program

Now that the Big Data hype has slightly eased, data practitioners are realizing that while we can, or do, collect large volumes of data, it is more important to be tactical with the data we are using. But a seemingly new contender for the data spotlight has sprung up: IoT data.

But is IoT data and the attempt to adapt it for data driven decision-making all hot air? Or is there actual substance to it? We at Hitachi Solutions argue that the IoT takes one of the more tractable and immediately beneficial aspects of modern data capture, high velocity data, and helps us drive highly responsive and, when utilized effectively, pre-emptive data driven decision-making.

It is important to distinguish between predicative and preventative maintenance. Unlike preventative maintenance which seeks to decrease the likelihood of a machine’s failure through the performance of regular maintenance, predictive maintenance relies on data to determine a machine’s likelihood of failure before that failure occurs. This allows manufacturers to move from a repair and replace model to a predict and fix maintenance model using predictive analysis —  which relies on data, statistics, machine learning, artificial intelligence and modeling to make predictions about future outcomes.

In recent years, predictive maintenance has risen to the forefront of the IoT data industry for two key reasons:

This means that in the next few years we will see more business problems, even those that are not machine maintenance specific, become restructured as predictive maintenance or predictive service questions. For example, the highly sensationalized story of smart fridges that are able to monitor our supply of food and ingredients could potentially lead to an automation of our consumption choices. This opens the door for predictability in our expenses —and gives us the ability to  better control our diets through planned food expenditures over the impulse buys that grocery stores encourage today.

I use this example, specifically, to demonstrate that predictive maintenance or predictive service programs and solutions will eventually solve problems we never even thought about — once we learn to creatively recast those problems in a way that can be solved using IoT data and predictive analysis.

Today, however, the most common use cases for predictive maintenance are in the following industries:

Additionally, the newer the machinery and technologies used, the more sensors and IoT data that is collected.

For most cases today, the objectives of predictive maintenance programs can be boiled down to one of two outcomes:

Production efficiency can be improved by maximizing the time that machines are up and running through predictive maintenance, or it can be also be improved by predicting the number of goods that will pass or fail a quality inspection, based on the readings from a machine. Another, less intuitive but interesting example is how we could even predict the condition of a machine based on the defects it creates in its outputs; in this case, the predictive directionality is reversed.

We can make these predictions by taking the most recent data points from a device and running predictive algorithms against them. In the case of predictive maintenance, this enables us to customize our maintenance activities to each specific machine, or even for each specific component on a machine; a noteworthy departure from the traditional method of basing our maintenance off of schedules or usage thresholds, such as miles driven in a car.

For example, let’s imagine a renewable energy production facility such as a wind farm. We have enormously complex machines that are riddled with sensors and computer chips that not only control each wind turbine, but also send constant readings of the state of each component on each wind turbine.