How komatsu is driving operational efficiencies using io t and machine learning 6.7.18

Editor's Notes

• #2: GM – welcome to the session on IOT Vijay Raja – Solutions Marketing Lead for IoT Dave Shuman – In the next 50 minutes we’ll try and dive into How Cloudera fits in the IoT Ecosystem What are some of the key enabling capabilities specifically around IOT How we are working with partners to drive an end-to-end architectures for IOT More importantly, we will talk about use cases We’ll talk about what Customers are doing with Cloudera on IoT today We will walk through a number of customer case studies and examples Hopefully we will have some time towards the end for some Q&A as well…
• #4: IoT will generate the volume and variety of data greatly exceeding those with which information leaders are familiar with today, requiring modernization of information infrastructure to realize value. Some of the key characteristics of IoT data include: Massive volumes as we saw in the previous slide But it is not just about the volumes that organizations need to be prepared for We are talking about a wide variety of data sources, structures, schemas and formats – from sensor readings of temperature and pressure at one end to doing video analytics on live video streams Most importantly it may come in streams (real-time) for lot of the IOT use cases or in batches – You need a data management platform that can manage both data-in motion and data at rest Most importantly, the real value from IoT comes when you can combine that sensor data with other internal and external data sources – adding context to that sensor data
• #6: Let’s take a look at how Cloudera’s EDH fits into the IoT Ecosystem Can ingest data from multiple sources including real-time streaming sensor data You can combine the sensor data with data other internal and external sources to drive business insights You can deploy EDH on prem (in your data center) or on hybrid cloud environments and still be able to manage it centrally And you can serve and analyze the data in a number of different ways - integrate it with existing BI solutions, do SQL queries with impala, or do machine learning or integrate it with real time applications (such as OnCommnad Connect in the case of Navistar)
• #9: Our equipment and service offerings cover a full range of mining needs and can be combined to provide full-system solutions for surface and underground mining. Integrated services and solutions help customers
• #12: Serve customers for the life of the machine
• #13: MarComm Read the definition Dissect the graphic: “We realize the definition is fairly long and cumbersome, so the intention of this graphic is to simplify the understanding of what JoySmart Solutions are. At Joy Global, we have been offering smart products, direct services and analytic services for a while independently. This new approach, called JoySmart Solutions has the intention of integrating these 3 main offerings with the objective of delivering value to the customer. The key aspect here that differs from everything else we have been doing until today, is that we are partnering with our customers to understand their challenges, and will offer them an integrated solution of smart products, direct service and analytics that will drive to an specific result, which will move the needle. Hence the colored dashboard on the center of this graphic. JoySmart Solutions are the means to an end result.”
• #20: Onsite team contacted our Smart Solution Analytics teams to pull data prior to the anomaly to determine if they could detect the event prior to need for manual intervention Through data preparation multiple machine sensors were identified as possible predictors, additionally from our domain knowledge of our equipment we created additional features based on engineering principles.
• #21: We Applied the Principle Component Analysis (PCA) to the 6 traction gearbox Sensor data. We utilized a random sample of 50% of the data from the first 40days assuming this will represent “normal” operation Calculated the principle components and their coefficients to be used to score the full data set After further evaluations of the axis coefficient’s it was determined that 2 of the engineered features were the best at describing the irregularity in the gearbox. In the end we utilize a Gaussian Mixture Model of the 2 features to describe the machine's digital finger print. Since conditions can vary from customer to customer we apply a digital finger print to each machines. Live data coming from the machine is evaluated against its own model. Our engineering team evaluated these models and conditions to determine the best methods to redesign the gearbox for the customers tougher conditions. Since then these gearboxes have been deploy to tackle these new conditions. This is a good example of how we close the data value chain that I spoke about before this example. Our 3rd generation platform enables us to develop analytics and models like this example in days rather than months when comparing to our previous platforms. The key driver in the improvement was breaking down the data silos and enabling quicker data wrangling and feature creation. We are excited to see the rapid development of the IoT space with new technologies to enable us to deliver these types of analysis to our customers. Here is a video highlighting the path forward for Komatsu.
• #23: There’s no out-of-the-box solution for mining, but every solution we create in today’s digital mining environment focuses on … Data is critical part of our journey to a more sustainable, less impactful mining environment Still allow us to produce the essential minerals needed for modern society in a more efficient way