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Machine Learning / AI [clear filter]
Tuesday, May 14


One Hour Presentation: US Navy's CBM Efforts

CDR Jesse Black

Deputy Marine Engineering NAVSEA 05ZB, NAVSEA

Tuesday May 14, 2019 10:30am - 11:30am
Freedom Ballroom II



Jump Start Your Digital Transformation with a Failure Mode Approach to Predictive Maintenance
In today's economy, manufacturers strive for the highest level of equipment productivity possible, while achieving the lowest operational costs. Fortunately, open and comprehensive industrial innovation platforms (IIoT) allow us to incorporate decades of reliability and condition monitoring domain expertise along with equipment connectivity, enterprise systems connectivity, people connectivity, expert systems, machine learning, augmented reality and more.

Predictive Maintenance promises the larger returns on investment (ROI) opportunities of a digital transformation. The gap between where manufacturers are today and where new industrial innovation platforms can take us stems from the historic inability to connect systems and people together with all data sources in a single location; thus, giving way to higher visibility, machine learning, and ultimately improved insight into the best next steps and strategy that improve equipment productivity.

However, the laws of physics bounding the ability of our production equipment are not changed by IIoT applications platforms. Equipment and processes continue to fail as physical, chemical, and environmental parameters wear and change. Our equipment and processes fail for known reasons. We can map the failure modes of our equipment and processes to machinery, operating process and environmental parameters. We then detect changes in these parameters that lead to equipment and process failure. We then predict a failure, and work to prevent the failure before it occurs. We are on our way to jump start our digital transformation for predictive maintenance.

This presentation introduces a typical production environment and equipment to illustrate failure modes of equipment and process. It illustrates parameters our IIoT platform can monitor, and the decades of domain expertise can build on for immediate ROI while setting the stage for machine learning and augmented reality to join in as our digital transformation and the tools we use mature over time. Example case studies are offered to illustrate the failure mode approach.

avatar for Preston Johnson

Preston Johnson

Platform Lead, Allied Reliability Group

Tuesday May 14, 2019 1:30pm - 2:00pm
Freedom Ballroom II


How to Make Data Work for Your Organization
Analytics Isn't easy let's accept this upfront. While we're at it, let's demystify unhelpful buzzwords, realize It takes more than technology, and requires organizational change: People, Process & Culture. So why do companies continue to mature their analytics capacities knowing it's complex? BECAUSE DATA DRIVEN INSIGHTS ARE TRANSFORMING BUSINESSES! This session will identify ways to explain the complexities of analytics approaches and data science simply, rather than oversimplify the complex. It will help analytics practitioners and business stakeholders speak in a way that inspires collaboration and sustainable best practices. Join Chris MacDonald to learn:
- What it means to ask the right questions of data
- What everyone should know about the advanced analytics process regardless of automated technologies
- How to get value from advanced analytics while building a data driven culture.

avatar for Chris Macdonald

Chris Macdonald

Global Lead, Analytics Center of Excellence, PTC

Tuesday May 14, 2019 2:00pm - 2:30pm
Freedom Ballroom II


Industrial Sensor and IIoT Data and Meta-Data Management
Industrial sensor data is Big Data and largely of time-series nature for asset intensive industries such as oil & gas, chemicals, paper & pulp, pharma, mining etc., including utility companies in electricity, gas and water production/generation and transmission/distribution. Attend this session as we review the challenges in sensor and IIoT (Industrial Internet of Things) data and meta-data management. This involves working with large volumes of high velocity data from automation and control systems with hundreds of thousands to millions of sensors; each sensor collecting measurements every minute, some every second or even few microseconds. The data may also be from CMS (condition monitoring systems) and several other disparate line-of-business data sources such as planning, work management, quality, weather, web pages etc. IIoT and edge devices now bring in even more data with their own new challenges during data collection, and the need to merge this data with legacy sensor data sets.

This presentation will focus on the lessons learnt during our 35+ years of working with data and information management in manufacturing operations (https://en.wikipedia.org/wiki/Manufacturing_operations_management). And, we will cover how data analytics (with machine learning when appropriate) and visualization are used in customer use cases in energy reduction, predictive maintenance and reliability, process yield improvement, product quality, and others.

A data infrastructure approach (scalable backbone) with a faceted and application-adapted digital model allows flexible and extensible methods for data collection, analysis, and visualization that are fit-for-purpose and finally, actionable insights. Also see https://en.wikipedia.org/wiki/OSIsoft


Gopal Gopalkrishnan

Solutions Architect, OSIsoft, LLC

Tuesday May 14, 2019 2:30pm - 3:00pm
Freedom Ballroom II


Driving Operational & Economic Impact with IIoT/Machine Learning Applications
Industry experts suggest that predictive maintenance can reduce breakdowns by nearly 70%. The cost of unplanned downtime in industrial systems and processes can be overpowering. By combining IoT sensor data with advanced ML technologies, enterprises can enable real-time predictive maintenance that can confidently identify and proactively alert operators before mission critical issues arise bringing industrial processes to a halt. Aldo is a global thought leader in delivering Predictive Maintenance and Process Optimization technology solutions for industry's such as manufacturing, transportation, and utilities.

We'll discuss how to take control of the industrial edge and IoT with a low barrier to entry in deploying ML and predictive analytics within the world of maintenance and reliability.

This presentation will also discuss how to shift an industrial systems maintenance and operations paradigm from reactive break/fix to proactive with machine learning and predictive analytics at the physical edge.

avatar for AJ Alexander

AJ Alexander

Solution Architect, ITG Technologies

Tuesday May 14, 2019 3:30pm - 4:00pm
Freedom Ballroom II


Maintenance Free Flow Measurements
This paper describes the relationship between a bathtub curve, failure distributions, and commonly used, but often misunderstood and misapplied, metrics such as the following: mean-time-before failure (MTBF), mean-time-between failure (MTBF), mean-time-to failure (MTTF), and other related terms. A bathtub curve for an object is related to a failure-distribution curve for that object, and when combined, a continuous curve is created. That curve has commonly named regions: infant-mortality, constant-failure, and wear-out, which comprises two subregions that I call degraded-operation and functional-failure. Associated with those curves are numerous metrics that are sometimes misunderstood and misused such as the following: failure rate, prognostic trigger point, prognostic distance (PD), MTBF (two meanings), MTTF, failures-in-time (FIT), and life time (reliability).


David Weeda

Government Program Manager, Chase Defense Partners

Steve Nelson

Senior Account Manager, Emerson Automated Solutions

Tuesday May 14, 2019 4:30pm - 5:00pm
Freedom Ballroom II