Thanks. For joining our session, I'm Adam, Michaelson I'm a product, manager here at Google on, the IOT, team joining. Me shortly, will, be a, couple, of folks from our partner, cog night gear. N'gou and sundry. Ham Erland so, they'll. Be coming up in a minute and describing. In detail their. Expertise, around IOT. And industrial. But. Before we dive in on that I wanted, to just do a quick, informal. Survey, so. For the folks in the room around, IOT. Internet of Things, how. Many folks in the room have. Deployed, an IOT. Implementation. Just by show of hands or are planning to do so say the next 12 months. Wow. That's, that's, a lot of people so, that's, exciting to hear what. We've seen from. Analysts. Is. There's. Basically a claim, that somewhere between 8 billion and 14. Billion IOT. Devices exist, out there today and that's a lot of devices it's also a big range 8 to 14 billion I, think one of the reasons might be when you think about IOT a lot. Of the implementations. We see use. A gateway, right look at think of this room and if you want to control all the lights via. IOT, then, a lot of the implementations, we see is will be a central box somewhere, some Linux machine that, all these lights are talking to on-premises, and then that gateway will connect, to, a cloud so. What. Is it that is, the, counting. As the 8 billion is it the one gateway or is it each individual, light I, don't, know I feel sorry for the poor analyst who's going around counting those 8 billion devices but. It's a lot of devices more, and more all the time and, all, of those devices are generating, incredible, amounts of data and. Dealing. With the intake of all that data is one of the challenges, when it comes to IOT. Even. Though there are so many devices, that are online there's, another statistic, which isn't so great and I don't know what the experience, of the audience is that we've, seen, that, people, claim that around 1% of the data that's actually collected, for IOT is. Actually. Used, to. Generate some, sort of a positive outcome, business, outcome and, so what's happening to that other 99%. Of the data that is, not generating, that outcome there's. An opportunity there for everyone to say how are we going to collect this data and make sure that where the data were collecting, has, maximal. Value so. That's. One of the, purposes. Where we could say see googles mission come, in so, googles mission is to organize the world's information and, make it universally useful. So when it comes to say Google search that's easy to think about pulling. Information from lots of web sites organizing. It so that it makes sense, providing. A nice handy, search bar that you could type into and then at the end giving you useful results, so you tie that chain together, IOT. Has, the same opportunity. Where. We're, collecting all this data it, needs to be organized. In a uniform, way and then made useful so. We think about the lights in the room are, collecting. Ones type of IOT data or telemetric. Data the. HVAC system might be another one the alarm system is another one how, is all this data being collected. Organized. To make sense and then produce a useful outcome that has business value so some of that some of the topics we want to talk about today, but.
Before Diving, right into the, industrial. Use cases I just want to spend a couple of minutes in baseline, us on what. Are Google's IOT, offerings. So we know what the terms are and all of a common understanding before. We dive deep into the industrial, use cases. So. This is an image of, Google's. IOT. Platform. For. Those who haven't seen it it's, called IOT. Core, IOT. Core is a component, that exists. Within Google. Cloud Google, cloud services. IOT. Core has a few major components. Built into it one, component, is this protocol bridge, that. Is what, will enable us to take these edge devices and safely. And securely, send. That data into, the cloud, we. Often will talk, HTTP, or a more. Popular, protocol, for IOT is MQTT, which. Is very efficient, for IOT use cases the. Other component, of IOT core is this device manager, which. Allows us to understand, each and every device both. In like example, we were talking with before with a gateway like in this room if we had gate with lights we, would under we will run or understand, the Gateway that's talking to us but, also each device that's connected to that gateway would, want to be known to the device manager so, as data is being sent from, these devices into the cloud we, can say oh that's a light and that's, an HVAC end and. That's an alarm or whatever it may be, another. Component of IOT core are some, edge modules, where, we have libraries that, you can take embed. Into your. Edge. Devices, that, have pre-built, connections. To IOT core you, don't have to use those edge components, you can use the raw API of, the, IOT, core cloud. Modules, but, those components, are there and extensible, for you just, for a quick, start so you can get your IOT journey going a little faster. Now. Behind IOT, core is the rest of Google cloud the. First module, we connect to is pub/sub it's, a large message, bus that's, used across many, Google components, to, allow services, to interoperate. Once. The data gets the pub/sub then, it can be sent, to other Google services, commonly. We, see services, such as data flow that. Can transform. Data so for example if you have a data coming in from a light and a late data coming in from an HVAC and they're in different formats, we have to make them look common, cloud. Data, flow will help us do that we can also call cloud, functions, if we want to run business, logic on real-time, data that's coming in in a streaming way and then many services behind that to store the data we have multiple, storage options, that, you, can choose based on what type of data it is how frequently it's coming in how frequently, you want to do, analytics. On that data and then. There are analysis. Tools that, we have such, as data studio as well as a suite of. Machine. Learning tools so. This is a this is all the Google components, clearly, but it's ones that we typically see on an, IOT implementation. You heard potentially. Yesterday in the keynote about multi, cloud and hybrid cloud and a lot of the implementations, we see aren't, quite as clean as every single component, up here is one, of these Google components, which we'd love to see but, we have many partners in, there are many players in IOT, so, oftentimes, this architectures a suite of tools that we see between Google components. Partner. Components, to come together for an architecture, so, one, way to sort of think about the, various components, that, are involved in an IOT architecture, is this, simple, three.
Buckets. On the. Left we see ingestion, to. Gather the data so, IOT. Core is there to get the data sometimes, you have data that doesn't need an IOT broker it's already ready to be ingested, so, for that you can use something like pub/sub just to take the data ingest, it in. The middle we need to clean, up that data process. It in, its the organized, step in the Google mission here. We see data flow functions. Data, storage, various, components, on the far right is where we have to do analysis and understand. This data and if, we don't see Google components, on all of these that's fine what's, more important, is for, the folks in the room implementing, this they, have a way. Of laying out the components, you're gonna have any Ryo T and know what functions they're going to operate on so. Before when we were seeing. 99%. Of the data in IOT is. Not reaching. Its full potential a lot of the reason why is because the. Process, and clean step isn't. Cleaning up the data as much as we need so that data is looking like it's in data silo, from the lights in the HVAC and etc and the other reason is because there's so much of this data it's. Not hard for a single device to generate gigabytes. Of data in a single year if that device is talking. Multiple. Times per minute or multiple times per second, and then multiply, that times all the devices and we're talking about an enormous, data load to, be able to understand, that data so we just don't have the human. Capital to go through all that data and that's where tools, like machine like like. Let me go back when the machine. Learning tools come in that will help us understand. Over. Here on the right to, review. All that data so, that let the Machine look at the data and figure out how to. Find the insights rather than the people and that's why ml. And IOT go, together so well, so. On the next slide I wanted, to talk about machine, learning where machine learning also has lots of tools you could use so. What's, a framework that we can think about in terms of which ml, components, we want to use and this, is another. Block. Of three so. Here on the Left we have Google components. That are pre-built ready. To use our. Data our model, meaning for example vision, you, we have a Google vision machine learning component, that you can upload an image pizza. Here's a pizza and, Google will say I think that's a pizza and, then you can go ahead and use that data or whatever, it's a building or it's a dog that, sort of thing in these components, on the Left there are more and more of them all the time, very, simple to use a great, place to start your ml, journey with all this data but. Oftentimes you'll say ok I know that's a pizza but I run a pizza company and, I want it to know that it's one of my pizzas, because I have all these different pizzas that I make so. How can i train that model and that's where the middle components, the retrain models Auto ml will come in so like Auto ml vision is a tool you can use and, the. Models are pre-built, but the training is not in here. You'll upload an image and say, here's. Pizza. And it's my supreme, pizza and here's another one and it's my whatever deep-dish, and these are the others so that Google will learn those images, so when it sees images of those in the future maybe, as the pizzas are coming out of the oven and you want to make sure it's matching what the customer ordered something, could take a picture of it match, the point of sale and say yep that's the supreme pizza just like the customer ordered with extra mushrooms and that's, where tools like Ottawa ml can come in any, of that training you do is your, data, none, of it would filter into the left none of it filters into Google it's all your data and then sometimes customers need even more they, need not only custom, trained models, but they need to build their own models, and that's where there's you'll the data scientists. Define, the, rules you need define. The data sets you can create great tools like tensorflow, either the open source or the hosted version on Google and have. Models. That are custom to your business, and customers. Will often start on the left, use the out-of-the-box ones and work their way to the right as they, need to because, the more you go to the right the more coding you have to do and the more the more coding you have to do it unless you're worrying about your core business and so that's why we offer this suite of tools. So. With that we have a baseline. Of, IOT. Core in the components, so, now what I want to do is invite gear up and, he's. Gonna describe to you how, cognate. Has taken these tools assembled. Them use in, an industrial, IOT use case and brought, them into the real, world and they'll share with us some, of the lessons they've learned in. Some of the tips for all of us to take advantage of here. Thank you thank you. Hello. Everyone, my name is gear I'm CTO.
And Co-founder of, cog. Night which is a software, company the. Works with a, set. Heavy, Industries what. That means is it's basically industry, that has big, machines that cost lots of money so. We work mainly with, oil and gas also, in power and utilities and, shipping. Now. This talk will focus on shipping because it is the most challenging. Edge. Environment. That we've encountered. But. Before, we dive into that I I want, to make sure that you are all awake. So. Quick. Show of hands how many of you have had a cup of coffee this, morning. Yes. That's what I thought as. We all know coffee. Is one of the primary, or most, important, inputs, to writing a good software, called. But. What are what. Are the inputs, to, coffee. It. Turns out that coffee sits. At the top of a, very long. Complex. Industrial, value, chain and. You don't have to backtrack, very far to understand, this because. The beans before they came to most only they were on a. Truck, and that truck needed, fuel hence the oil and gas industry, or in. Where. I'm from in Norway I believe. 60%. Of new vehicles sold last, month for electric, so there, you have power and utilities -, and. The truck needed to be manufactured, from steel so you are mining, steel mills, and. Before, the. Beans got onto the truck they were probably on a ship, so. It's. But. There's, this huge value chain which. Touches all the industries that we are in and it's. Not just coffee it's everything that we surround ourselves with that kind of make our lives comfortable. And convenient and that's. Why I think it's such a privilege to. Use. Data and algorithms to, drive. Industry, to be more efficient, produce. More for less energy, input make it safer. Yeah. It's, it's, really quite quite. The potential, there. So. When we started, called night we set out and we had this kind of naive belief that, we. Would use sensor, data and we do ml, on that and. Things would be great now, it turns out. Industry. Is a little bit different from consumer, in. That there are so many, different. Data silos, different, protocols. You. Know different systems that you have to integrate with and in order to understand, the sensor data it's you. Need a, lot of context, around it so maybe you need the engineering, data maybe you need a CAD model to, figure out where the sensor actually is located, or you need the topology of how how, stuff flows through, a power plant. And. So we, were very lucky to, start out with a large industrial customer. Very, early to be exposed, to this reality, of industry, I. Think. The. Core problem. Is. That. The life cycle, of industrial, equipment, is, so much longer than the life cycle, of software. You. Know a plant, that's. Five, years old is new software. That's five years old is old. So. Then with this data complexity, that we witness. Our. Mission. Quickly became, to. Liberate. All this data from, the industrial. Silos. Move. It to. Somewhere where it's always available and. Clean. It so that it becomes understandable, so.
That It's easy, to build, value from it, as. You can see it's quite a complex. Reality, out there as. Adam. Was saying the the scale of data is enormous a single. Sensor is sending one value, per second, will, produce. One gigabyte of data every, two years and. That. Doesn't sound like a lot but. Typical. Ship has 5000, of these an. Oil platform can have a hundred thousand. So. It becomes a lot of data and that actually informed, our choice of Google Cloud as the vendor because we believe in the ability of Google to run distributed, systems, at a scale that's one, step ahead. Of everyone else so, we want to reuse the same infrastructure, that's powering YouTube, Google, search and Gmail. To. Store all this data for. Instance we're storing all the IOT data all the sensor data time series in, system. Called BigTable, which, is a, ridiculously. Distributed. Key-value store, and, how. We structure. Data into, big BigTable. To. Be able to query, it efficiently, and you, know store data efficiently could, fill an entire talk, and in. Fact it does I. Did, a talk on that last, year up next so if you're interested, in the details of that you can google code, night next BigTable. And you'll find out talk. As. Someone, who loves. Technology. It's. Really. Easy. To. Get kind, of sidetracked. Into, building, technology. For technology's, sake. And. That, usually doesn't lead. To great, outcomes, in the end and. I've been guilty of that in my past. So. It, makes sense to sit down with the people in industry, figure out what the real valuable, use cases are and some, of these use cases will, span we, see in every vertical every industry that we are in such a fuel efficiency or energy, and fishin see in more in general nobody wants to waste energy energy. Is a cost in. Fact in shipping it is. The number-one cost a single. Product percent. Reduction, in fuel, costs for. A you, know an average fried. Chip is a. 50k. Per, ship annual, savings, and. For. A fleet of 100 ships that's 5 million and by the way we're not targeting 1%, so. Our customers, estimate, anywhere. From 5 to 15%. Savings. Depending, on the type of ship and the, age of the ship and, then. There are other use cases which are specific, to each industry such a weather impact, on cargo you, want to be able to tell, your customer, that you didn't damage the goods. And. You can measure the motion, of the ship at any time so you can even say. Hey look I didn't, go. Through two rough seas. This. Is what. A data silo, looks like. We. Have this great. Piece. Of equipment a sensor, that. Is using lasers, to measure. The torque on the, propeller, axle. On a, ship. So. It's like the laser underwater. Sending. A beam along the axle it's reflected, it's detected, it. Measures, the microscopic. Deformation. Of the axle. Due. To the force and acted on it. Now. Contrast, that with the. Way that this data, is, sent. To the cloud. Because. What happens, is every. Day. This guy walks. Down to the, engine control room and writes. Down the value on a piece of paper and, it goes into a report. But. That's not like the reason this particular, measure. Is important, is if, you want to solve. The energy efficiency, use case, for. A ship you, need to be able to break down, the. Fuel. Used to, ship, motion into its two main parts that's the engine efficiency so. How much force do, you get. Onto. The water via the propeller for, each unit of fuel used and the. Whole performance, so. For the force that the propeller acts on the the. Water how. Far do you go and. A. Once. Per day resolution. Is nowhere near what, you need to. Solve the use case. Because. The engine settings, change too, often weather changes, all the time so. Here. To. Get the data out. We. Have to install an adapter and this is a pretty common pattern. So. The data, sets in a separate. Network. You can't talk to it on the ship's network, which, converts, the, data and sends, it onto the ship, Ethernet, where it can be picked up by an edge box. And. You have many of these systems. That you need to liberate data from. Here's. Another issue, connectivity. The. Good news is that you can be connected virtually anywhere on the planet. The. Bad news it's it's kind. Of expensive, and the, bandwidth, is not great. One. Of the things that surprised, me about this is that you. Actually get quite decent latency. Your. Data can go from the laser to, the cloud in 200 milliseconds, I find. That pretty pretty, awesome. But. We have too, much data to send, over this connection. And, most of the data is not that useful for this use case anyway. So. Given those constraints here's, how we approached. Solving. The. Situation. First. Given, the bandwidth, constraints, there is a need for an edge component. It. Needs to manage the. Bandwidth, so. Some. Of the data is high-priority, and should be sent directly, to the cloud some of the data needs, to be buffered. On the edge stored. There until connectivity, is better when the ship is close to shore it, will get cellular, coverage which, is much cheaper and much wider, when.
You Can bulk upload, that, data. You. Also may want to do some processing on the data down, sampling. Or. Compressing. The data in other ways for instance, vibration. Sensors, on rotating, equipment will, often send you data at, 50, kilohertz so that's 50,000. Data points per second that's like 10 times more than the rest, of the sensors on the ship you can send. All that data and you don't need to because. If you do a Fourier transform, convert. The signal into the frequency space, and send the coefficients. Of each, frequency. That, constit. Constitutes. The signal, then. You get almost. Exactly, the same curve. With. One thousandth of the data. Transmitted. Which. Is also a very cool stat. And. You may also want to use it as a cache if you. Have applications, on the ship if you have like a digital worker application. Running. On android phones where the workers, on the ship can get all. The sensor data from the ship at any time they. May not want to go. Via the cloud for that so you can ask the hedge box directly, and. There. Are also more interesting use cases which we'll get into which does more, interesting, compute, and, predictions. On the edge. Also. This is not your typical. IOT. Consumer. Scenario, where you have to have very very light Hardware, it. Actually makes sense to invest in a rugged, Hardware that won't break. In this case and, it, also needs a bit of storage because, it needs to buffer all this data. It. Pretty like an average ship will produce something like 500 megabytes, per day. Of. Data, and it can be at sea for up to 2 months at a time so. You need. Some storage, there and you also don't want to lose that data if one disk dies so it's a raid configuration. Has. A rugged PC with no moving parts. So. Here's what the. Architecture, looks like. You. Have all these. Silos. At, the bottom the green boxes, they're, typically on their own networks you can't access them via the ship Network so you need a physical. Adapter. And. In our case those are always set up to be read-only. Which, makes the whole situation. Less. Scary from a security point of view then. It's picked up by the edge box what it, needs to do is translate, from. All these different, industrial. Protocols. The. Modbus, the OPC, OPC. UA MQTT. And a wide, variety of other. Dialects. That you need to talk, once. You've translated the data into, into. The cab same. Format. Put it on another cue and then. It's processed. It's. Matched, against, a list of high-priority tags, and some. Is sent directly. Over the satellite link which, is managed, by cloud. IOT core, and. The, other data. Is stored, and sent. Over, a cellphone connection. One. Of the things that we didn't do what we are considering. Doing is to, move to EDX. EDX. Is a framework for fore-edge. Solutions. Which, will, do some, of the translation, for you and it has a lot of components that you can put together to, solve for from any of these scenarios.
One. Of the things so, cognate is almost. 200, people and it's, just. Over 2 years old one. Of the things that we value very highly is speed and. One of the things that we to get speed is to. Always, try to use managed solutions, where we can you don't want to. Reinvent. The wheel and. When. You're looking at this, next slide you might wonder if, we, do anything at all because. It. Looks like Google is doing all the work but. There. Is some stuff there like cloud functions, we actually write the stuff that's in the cloud function, and. Kubernetes. Engine, where we have our business logic. So. On the cloud side, basically. What i io T core gives us is a managed connection we. Don't need to scale that thing and, it. Has, encryption. Authentication. Is taken care of and, it conveniently, puts, the data on the pop sub-q which we use for the, rest of our system so it's to, us it's we, just interact with the pub sub q which. Triggers cloud, function, which. Translates. The data on the Q into, API calls, to. Our API gateway. So. Our. Our. Solution, is used. By many customers. In many verticals. Many. Industries, which have, slightly different, ways. Of sending, data so. Not all of it is going through IOT, core and that's why the API kind of access, to the, one gate. Which. Is the. Common denominator there, that's why you need the translation. Same. Thing with the daily logs the the backfilled, data, it's. Uploaded. GCP, no, Google. Cloud Storage I mean and that. Triggers, a cloud function whenever, a new file is uploaded, which. Does the same translation, so it's the same, protocol. Buffer based format. Now. When we get to the yellow. Box. We. Have a pipeline for. Processing. The data. Structuring, it so that it doesn't. Use a lot of space and computing. Roll ups so that you can get you. Know do advanced, queries with millisecond, the, latency. And this, pipeline we've run that ten. Million data points per second, so every component there the kubernetes, engine, autoscaler. Pops. Up skeltox. Incredibly. Well and. BigTable, so. It all scales horizontally. It's very fast and then. When we do, run predictions, on this data we. Use ml engine to host those models, and we, use cloud scheduler, to. Do periodic. Predictions. No, no so. We. Don't write back to the, control systems, so. The use cases that we have are about advicing. Say the captain you, know you need to slow down a bit because you're. Using too much fuel or. You. Need to clean the hull because there's marine growth which is slowing you down it's. Not it's. Something that you can do parallel. Periodically. Maybe you do a prediction every. Minute. But. We do. There. You can, think of cases. Where you'd want to kind. Of do streaming. Analytics. When, it comes in but right now it's not necessary. Security. So the edge environment, is kind of scary. The. Control, systems that you deal with there are. Set. Up they. Usually don't have the concept, of authentication. So. If, you can talk to them you can do, whatever you want with, them a. Couple, of principles that we implied we don't want to invent our own encryption, scheme without Google IOT core handle.
That We have no inbound routes to, the edge and. There's. A physically. Separated, network so we only read to, these sensors, so. That makes it a lot less, scary to be on the edge but security is definitely, a big, concern here. So. Bring. That together here, we have one. Of the dashboards, which will show you and, verify, that. Was not damaged. This, is the ship's motion in all the different directions. Axles. 10. Hertz with, 200, milliseconds, latency, so. If you're shipping cars for instance you could share this with a car manufacturer, and really you, know assert. That you didn't damage any. Other goods. It's. Also very, important input to the energy efficiency use case because if you have rough seas your. Engine will have to work more in. Fact very, rough seas, can, make. The engine shut down entirely, which is what happened, with the cruise ship Vikings. Guy a couple of weeks, ago outside the coast of Norway rough. Seas and. The. Engines tripped as a result, of that leaving. The ship in a very precarious, situation. And. Then when we move on to the more advanced, use cases that. Involve. Machine. Learning you. Have an of course anomaly, detection which is very much used in predictive, analytics. The. The. Models they're usually based on clustering, or forecasting, so. You're. Looking for data that's that. You haven't seen before basically and then you're, alerting someone. Inspection. We. Have a lot of sensors, that are not just you know pressure temperature. Flow. We. Have a sensor in in our pocket, which is a camera. And you can use that to. Extract, loads of, interesting. Information using. Image recognition so. Inspection, and we with, trained. For instance last year we trained an auto ml model which detected, damaged wires. And. You, can imagine having, drones, to fly around and, detect, these. These. Faulty, things or corrosion, which. Is already being done by some companies. And. In those cases you definitely, do. Not want to send all the footage to, the cloud if you're on a. Satellite. Connection. Yes. So guide, I heard, some rumors about your, demo last year that you did some had, a windmill, on stage and did some machine, learning as well that. Must have been pretty terrifying. Yes, a good demo is. Exciting. To the audience and very, scary to, the presenter. So. I think. You. Know especially when you are moving parts, and you're doing machine learning which is never, really, 100 percent that's that's always scary but. You know the one trick is you can always blame the, Wi-Fi. But. Yeah. So I think, we should which would try to top that and do something even scarier this year yeah definitely. And that's. Why we brought this tiny. Model of a chemical plant, it. Continues. The streams live. Sensor, data to. Cognise. Data fusion via, Google, IOT core. So. Typically. You use this sensor, data to, visualize. Visualize. It, in charts, like. You can see. Soon. On this screen so. Here you can see the temperature, it's, about. 27, degrees that's, pretty. Hot. But. There are other ways to visualize your data as well. In. Cognate, we. See. The power of, using. 3d. Models, as a tool, to visualize your data, and. That is definitely. I use. A tool for the future I see. That but what what if you don't have a 3d model, that's. Definitely. A, big, issue and a challenge for the industry because. Either, you, don't have a 3d model or perhaps the. 3d model already, got. Got. All from the day of assembly. And was, has. Become kind of useless so, that's, why we have developed an application that, helps you generate this. Up-to-date. 3d. Models in order, to. Visualize. Your, data in, a better way you're. Not connected. I think, okay. The. Way it's the Wi-Fi yeah. Definitely. The Wi-Fi so, what. We can see here is the, model on screen I take. A single picture and I, detect, features, on it I, could. Take one more and then, it guides me through the, entire process so, this, is something. That I do now, on my phone, but, this is something that you can do with any kind. Of camera and it's. Not something that you have to do yourself either okay, so you just need the photos, for this to work you don't need a special camera you, might wonder why there's a helipad, here.
That's, Because sometimes. When we run this we. Use, a drone. Unfortunately. If, you look closely on the sign outside the door here it says no drones are allowed and. Yeah. That complexity. Of getting. That, permission in time was unfortunately. Too high for us but it doesn't matter you can use anything like your mobile. Phone or. Drone. Or you know GoPro on someone's helmet it. Doesn't really matter yeah so, what's. Happening to those pictures right, now yeah now, that I'm finished taking pictures I just upload, them to cognise, data fusion where, the magic. Happens, and, right. Now it's, processing, all the pictures, and. Should. We take a look at the final result all right let's try that nah. Wi-Fi. Has. Failed. And. Here it looks like we've got a nice model to it so. Suddenly. We have a 3d. Model that's up to date with, the exact same dimension. The exact, same proportion, and also, you, get a good overview, of the texture, on the model as well. But. If. You want to view the time, series in, their right context, here I guess you have to do some kind of manual yeah yeah, so, the thing is that this, is definitely a really good tool for the for, the future of the industry, and. But. The thing is that wouldn't, it be cool if we could link this model with, the data that, we have inside, the cognate data, fusion platform, and. That's. Actually, something, that we can do so. If we take a close look on, the tags that, are located, all around, this model, you. Can see tio2, tier, one, those. Kinds of tags is something, that is present, on all platforms or, in, ships, as well and, identify. The different, assets that. Identifier. Is something that we also have in, cognize. Data fusion. So. What we can do is to. Use the same pictures, that we took here and. Try. To. Detect. All, the different, tags, using. Google, vision and. When. We have detected, them we. Store the location of, all the different assets and also. Which, asset it is. Shall. We see if it got. Them in there and the end result is. Here. So. What was happening behind the scene there is we used a. Model. Called Yolo you only look once which, is an object detection, model, and. That. Thing is trained, to look. For this, this. Mission. Here right target's tear the tags but it it can identify other stuff too like rust. Or faulty wires etc and place it right there in in the 3d, model yeah that's instant. Situational. Awareness there, Andy, this room is too, hot it's. So, we can demonstrate this, for you let's, see if I'm hotter, or cooler than, twenty, eight point two degrees. So. I'm cooler yeah, I'm. Almost dead I guess, maybe, it maybe you have some cold feet yeah, but. Then we're the. Inside, operational, limit again and that's good because it turns red when it's above 27. Yeah you're. Actually cooling the room down yeah. But. All right so yeah, and. This, is the model that you can update. 24/7. At least when you have a robot to take the pictures for you and. That's really. Convenient when it comes to detect. When corrosion. When. Corrosion, started you can actually travel back in time you can check whether.
The Corrosion mark was there only. Two. Weeks ago or perhaps two. Years ago so. To. Complete, five. Minutes ago we, only had a chemical plant that. Streamed live, sensor, data but. Now we, also have an up-to-date. 3d. Model that displays. Our, sensor. Values in a, really intuitive, way. Thank. You. You. Can you, can do this with real. Industrial equipment, - all right this is data now streaming in from the North. Sea and. It identified. The plates, on that equipment, -. And. Also here you can find some things that shouldn't be here for instance, these. Ladders they're not supposed to be in this zone so it's a health. Safety, environment issue right there. Then. Let's. Wrap, up here's. My checklist I wish this checklist, was shorter, but it's not I mean this is the reality of why, it's hard to create, value. In industry so, you have to start with the valuable use cases, start. With that and talk to the people in the business because you will depend on them to implement it later so it better be something that they think is valuable -. Then. You need to figure, out if you have the data that's, necessary to solve those, use cases usually, you have the sensors but you. May have to do some work to liberate the data. And. Then. You need. To clean the data make, it so. That you can actually solve your use cases for, instance linking, it to a 3d model it may seem trivial when, you have kind of a plant with four. Sensors. But if you have a hundred thousand, sensors and you send the data scientist, in to. Find, the right sensors, to, do predictive, maintenance on, your one, compressor, or whatever it's going to take months if you don't have good tools to figure out what, sensor, is is where and which and relevant. To the use case. Then. You. Need to do. Stuff with the data so if that's algorithms. Usually. You don't build your own you. Merely, apply you, know other people's, work. Most. Companies don't do machine, learning research they do application, of, known. Research, to new problems. Now, once you're you're satisfied, with those results you need to monitor the quality of the data because, your. Model probably won't, handle. You. Know garbage, data coming in and these systems these, sensors will give you a lot, of crap data from time to time there will be connectivity. Issues there will be you know flipping. Bits here and there it's it's. It's, a quite. Challenging thing. To do. So. You have to build that trust in. The data and, then. You have to change the way that people work in. The company there it's very useful, if you kind of introduce your use case early on so they know this is not, something you made up in.
Worst Case you just, you've. Done, a lot of work and you, get to this stage and it's like way this. Isn't, useful. That. Sucks so, and, then. Last. That's. When. You've done all those things you, profit and that's what LT is really about, otherwise, it's not sustainable. Here. Are some pointers to, get you started so for, IOT core all the AI solutions. That. Google has to offer. We. Have a booth down, in the lower level. And there, are industry, solutions, so it's on the left after taking the escalator down. Contact, us there if you have questions. Thank. You and also. Don't. Forget to. Rate. This talk we. Definitely. Appreciate your, feedback so that you, know next, year we can have an even better talk and even scare, your demo. Thank. You.
2019-04-16