The Key to Leveraging Industrial IoT Data? First Do No Harm
What kinds of hurdles do you see when it comes to collecting IoT data in an industrial environment?
Shuman: These networks are typically configured with individual machines that are recording data, but the metadata of that captures the context of that line isn’t there. For instance, you might have seven devices in a row that operate in a defined sequence on the production line, but, looking at the data, you wouldn’t know that the things operate in a particular sequence. Each one of them is collecting data independently.
The value lies in using data to understand the integrity of a process — not the integrity of an individual machine. But it takes time to come up with that kind of visualization to show that.
The other thing is a lot of the data we want to use isn’t necessarily being digitally collected. You look at the Kanban process in factories where you have valuable information on whiteboards. And the way those organizations record [data related to operational efficiency] is that somebody walks in with a clipboard and records the value on the whiteboard. To even to get to the point of getting that digitally connected and collected to drive predictive maintenance takes time.
And yet there is still this viewpoint: “All of the machines are in there, and they are already connected. Why are we not sucking that data in and using it?” But you have to be able to define the constructs and the data that drive the use case before you can do that.
On a different note, Cloudera had some news related to a different type of industrial environment — mining. What is new there?
Shuman: One of our partners, Komatsu has been doing predictive maintenance and machine-to-machine communication in the mining environment, but now they are getting into optimizing facilities. In a longwall mine in Queensland in Australia, they can use the telemetry data and operator data to not only increase uptime for a machine but also optimize for the productivity of the mine itself. It is kind of like having expert coaching. The net result is that they have doubled their productivity on the device.
Uptime is a big deal in mining where you have a single machine that tends to be in difficult-to-maintain areas. If you know and can structure when you maintain components on the device, you can optimize for all of the other flow that is going on in the mining environment to help maintain productivity.
What are some of the most significant innovations you see in the mining sector?
Shuman: You are starting to see some class 5 autonomous vehicles. Komatsu has released a hauling truck for mining that doesn’t have a cab. In mining, you can leverage innovation that is already out there in ways you can’t in other sectors. You aren’t restricted in the same way we would be if you had to deal with using autonomous vehicles on open roads. You have a constrained environment.
We are also working with a large privately-held company that does a lot of salt mining that is doing predictive maintenance in the seventh layer of a salt mine. Salt, of course, is very corrosive and destructive on materials. They are doing predictive maintenance a mile down. It is essentially an inverted Empire State building underground. To be able to get materials in and out efficiently requires data. You want to not only know when you want to maintain the device but when is it optimal to put the parts, machinery and repair personnel into the pipeline to get them down to where the machine is to perform the maintenance. There is only one of those things down there that is performing work. The mining companies are leading in that because the benefits are so direct and tangible.
In mining, you tend to have expensive capital equipment that is well instrumented. A standard piece of equipment could have 20,000 channels’ worth of data. Each channel is tied to a particular sensor. Not all can be sampled at the same frequency: You have oil pressure, RPMs and things like that. But you can pull an immense amount of telemetry back for these diagnostics.