Can you imagine your work or household chores today without using computers, smart devices and the Internet? Undoubtedly, digital technologies and devices have fundamentally changed work processes and everyday life. Mechanical engineering and many industries that use heavy equipment have been inexorably transformed in recent years under the influence of digitalization of processes. After all, innovation and compliance with the requirements of the time are the key factors that move and develop the mechanical engineering industry.
We bring to your attention a short interview with the director of CAT Digital Analytics, Daniel Reaume, who heads the data processing and analysis department at Caterpillar. Mr. Daniel and his team collect and analyze CAT product data to help make Caterpillar equipment more reliable and better suited to customer needs.
How do Caterpillar technicians talk?
Dan: Our equipment talks to us in different ways. And to collect data about the equipment help:
- Temperature sensors, fuel consumption sensors, GPS sensors and others.
- The “box” that collects, integrates and calculates data and prepares it for shipment to Caterpillar.
Data transmission uses various communication channels and networks.
Can all Caterpillar machines and engines transmit data?
Dan: Today, Caterpillar’s fleet of field-connected assets totals more than 1.2 million units. It is the largest connection park in the world. For quite some time, our machines have been sold as “connected”. And even old equipment can usually be upgraded for connectivity.
But the number and types of sensors on CAT machines and motors depend on the type of equipment and model. For example, our smallest machines and engines typically have smaller and less sophisticated sensors than our largest mining machines.
What does the data tell us?
Dan: We separate car and engine data into two big groups. The first group is similar to the warning lights on the dashboard of a car; the second is more like an actual reading of the car’s current tire pressure or battery voltage.
The first category may include fuel consumption, GPS data, and trouble codes. Advanced analytics allows you to use simplified data, such as fault codes, to predict service issues, performance degradation, and more. However, the efficiency and accuracy of predictions may not be as high as when using more detailed data sources.
The second type of data is richer and more complex. It usually includes samples of sensor readings that are taken once per second or more frequently. Currently, this data is collected mainly from mining and large construction equipment.
The collected data allows you to identify patterns. For example, the data may show that when the operator applies the brake, pressure is not restored as quickly as expected. And we recommend checking the system for leaks. If our assumption based on data analysis is confirmed, the customer can correct minor breakdowns in a timely manner before they become the cause of a larger repair.
Using just the trouble code data, we might have warned of low pressure – but perhaps not as quickly as with more detailed data – and we might not have pinpointed the leak location as accurately.
Please note that we rely not only on fault codes, but also on fault code patterns and trends. For example, a single low level alert may not be of concern, but may be more important if we find a pattern of multiple types of repeated fault codes.
Regardless of the complexity of the data, identifying these patterns helps to discover conditions that might not otherwise be obvious. Even simple trouble codes help to distinguish a serviceable car from a car with an impending problem.
What is condition monitoring?
Dan: Dealers use condition monitoring to track the health of assets and contact customers when something goes wrong with their operations. With our dealers’ large proprietary equipment and industry expertise, no one can better control the health of our customers’ assets, even mixed fleets.
What’s next on the data horizon?
Dan: I think of data as new DNA. At Caterpillar, we use innovative, sophisticated methods to explore, split and recombine data in new ways to solve problems. It’s exciting and constantly evolving – things never stand still.
Perhaps never before has there been such an exciting time to work for Caterpillar as it is now.