× Given the current COVID-19 situation in Nashville, the PHM Society has moved the 2021 Annual Conference to a 100 % Fully Virtual Event.

Panel Sessions

The PHM Society provides an opportunity to hear and interact with recognized industry leaders in relevant areas for our PHM work. These 90-minute panel sessions will consist of presentations and open discussion by 4-6 panelists directly engaging with the conference audience on the different topics listed below.

These sessions add an enriching dimension to the conference experience and a welcome networking alternative to traditional paper presentations, which dominate some conferences. We believe balancing the conference time in this fashion provides participants a much more engaging experience and an increased opportunity to gain unique knowledge.


Panel Session Topics:

  1. Cybersecurity and PHM: Securing the OT and PHM Data Streams
  2. Standards
  3. Applying Artificial Intelligence and Machine Learning for Predictive Maintenance and Analytics
  4. Space Applications
  5. Digital Twin
  6. PHM for Manufacturing: Assessing Operations to Advance PHM Capabilities
  7. Unlocking the Potential of Automotive PHM
  8. An Integrated Architecture for Cyber-Physical Systems Health Management
  9. Qualifying Data and Data Use – Assuring Data Capability for Intelligent Systems and Beyond
  10. DOD/Fielded Systems

Panel Committee Chair:

Brian A. Weiss (National Institute of Standards and Technology)


Panel Session Schedule:

No.Panel NameDate/Time
1StandardsTue 13:30 – 14:45
2Cybersecurity and PHM: Securing the OT and PHM Data StreamsTue 14:30 – 15:45
3Applying Artificial Intelligence and Machine Learning for Predictive Maintenance and AnalyticsWed 11:00 – 12:15
4Space ApplicationsWed 12:15 – 13:30
5Digital TwinWed 14:00 – 15:15
6PHM for Manufacturing: Assessing Operations to Advance PHM CapabilitiesWed 15:15 – 16:30
7Unlocking the Potential of Automotive PHMThu 11:00 – 12:15
8An Integrated Architecture for Cyber-Physical Systems Health ManagementThu 12:15 – 13:30
9Qualifying Data and Data Use – Assuring Data Capability for Intelligent Systems and BeyondThu 13:45 – 15:00
10DOD/Fielded SystemsThu 13:45 – 15:00

Panel Session Details:

Panel 1: Standards
Lead: TBD
TBD
List of Panelists:
TBD
Panel 2: Cybersecurity and PHM: Securing the OT and PHM Data Streams
Lead: Radu Pavel, TechSolve
The COVID-19 pandemic has led to an accelerated digitalization of the work environment and the adoption of remote supervision of manufacturing assets and production. In this new digital manufacturing ecosystem, the Prognostics and Health Management (PHM) approach is becoming the strategy of choice for the advanced manufacturing enterprise. The value of real-time data from various functions, and the benefits of new technologies fuel the desire to connect production and non-production devices on the factory floor. However, the appetite for advanced technology is rapidly exceeding the organizations’ ability to protect it, and this connectivity and data-rich environment raise significant concerns and challenges associated with cybersecurity.
 
This panel will explore the latest trends regarding standards, regulations, strategies, and technologies aiming to secure the operational technology (OT) and PHM data and information. The panel also aims to reveal perceived challenges faced by the developers, implementers, and providers of PHM technology, and their current strategies for mitigation.
List of Panelists:
TBD
Panel 3: Applying Artificial Intelligence and Machine Learning for Predictive Maintenance and Analytics
Lead: Andrew Harper, Georgia Tech Research Institute
With the increasing prevalence of Artificial Intelligence and Machine Learning (AI/ML), and the widening adoption of Model-Based Systems Engineering practices (MBSE), applied AI/ML and MBSE are having a significant impact on the PHM community. From predictive maintenance planning through neural net data training and digital twin development to distributed enterprise-level systems engineering, these cutting-edge capabilities are impacting all operational domains across the public and private sectors. Panelists will discuss lessons learned and best practices leveraging these emerging
technologies. Topics will include integrating and leveraging SME expertise jointly with data science, system-level challenges to real-world MBSE implementation, and demystifying considerations when applying AI/ML to fielded challenges in the field.
List of Panelists:
TBD
Panel 4: Space Applications
Lead: TBD
TBD
List of Panelists:
TBD
Panel 5: Digital Twin
Lead: TBD
TBD
List of Panelists:
TBD
Panel 6: PHM for Manufacturing: Assessing Operations to Advance PHM Capabilities
Lead: Brian A. Weiss, National Institute of Standards and Technology
Manufacturing has evolved over the last few decades to leverage emerging and advanced technologies. Many of these technologies enable the growth of PHM capabilities including the advancement of monitoring, diagnostics, and prognostics to enhance decision-making and maintenance strategies. Manufacturers recognize that these emergent PHM capabilities can enhance their maintenance strategy – optimize planned downtime and minimize unplanned downtime – to achieve more reliable, and ultimately, more profitable operations. For manufacturers to realize advanced PHM within their facilities, they face a challenging reality – How do they assess their PHM capabilities and the value it obtains? And, more importantly, what is the value they want to achieve and the corresponding PHM capabilities to be added? This panel will focus on how manufacturers can assess their current PHM capabilities and how they can determine what levels of PHM are most desired by their organization. This will be paired with individual value propositions in terms of the expected return on investment of additional PHM capabilities along with a discussion of current maintenance expenses.
List of Panelists:
TBD
Panel 7: Unlocking the Potential of Automotive PHM
Lead: Steve Holland, VHM Innovations, LLC
The automotive industry has proven to be one of the most fertile application domains for PHM technology in terms of financial impact, analytics sophistication, and sheer scale. Successful examples have been implemented for both manufacturing systems and the automotive vehicles themselves. The case has been made for even greater opportunity in coming decades as the continuing electrification of vehicles takes place. Similarly, the potential impact for fleets is anticipated to be huge. This applies to conventional automotive and trucking fleets as well as for future autonomous fleets. But, the pace of PHM introduction continues to lag behind what it might be. This panel seeks to understand the key enablers for recent industry successes as well as the barriers that have limited more rapid progress. The discussion will be centered on strategies that effectively exploit those enablers while mitigating the barriers.
List of Panelists:
TBD
Panel 8: An Integrated Architecture for Cyber-Physical Systems Health Management
Lead: Frank Zahiri, United States Air Force
As the complexity of modern manufacturing, transportation, and industrial systems increases, the need for improved system resilience, reliability, and safety, follows an increasing trend. Technologists attempting to develop and introduce integrated process/system methods for such complex systems must introduce new and novel system engineering concepts that integrate facets of modeling, testing, analysis, and algorithm development. The Cyber-Physical Systems community has an expressed interest in the health status of integrated processes/systems, i.e., questions are raised as to whether specific processes are available to perform the next work tasks, or a sequence of processes is not suffering individual process losses that might compromise the operational objectives of the whole system of systems. It is of interest to investigate technologies that can address such questions. Large scale systems (industrial and manufacturing processes, transportation systems) are subjected to fault/failure modes at the component level. They might propagate to other healthy components and, eventually, migrate to the subsystem (inspection station, repair/overhaul, etc.) and system levels. High-fidelity models at the subsystem level are difficult and time-consuming to develop. Moreover, sensing modalities at the component level monitoring their health status are mostly absent.  It is imperative, therefore, that investigations of the health status at the integrated process level must rely on simple methods that take advantage of the structural and functional properties of such complex systems. We developed and adopted two reasoning paradigms, based on AI techniques, to assist in determining appropriate diagnostic and fault propagation routines for typical subsystems. We reason about the status of systems at the fleet or swarm levels. We developed and used models and simulation platforms that capture the structural and functional attributes of complex subsystems yet avoiding the development of high-fidelity models. This modeling framework is used to detect the presence of fault modes and their propagating behaviors to other components. A Model-Based Reasoning paradigm sets the stage for representing the structural and functional attributes of such complex processes/systems. A Dynamic Case-Based Reasoning paradigm is the “smart” knowledge base housing data/information, algorithmic developments, and decision support processes. We illustrate the efficacy of these reasoning platforms with examples from the manufacturing and industrial domains.
 
This panel will discuss the following topics:
 
* We will begin with a definition and typical examples from Cyber-Physical Systems (CPS) domain
* Determining the health status of integrated CPS
* Modeling of CPS at the system and subsystem levels
* Reasoning paradigms for determining diagnostic and prognostic approaches at the subsystem level
* Examples from the industrial and manufacturing areas
List of Panelists:
TBD
Panel 9: Qualifying Data and Data Use – Assuring Data Capability for Intelligent Systems and Beyond
Lead: Michael Sharp, National Institute of Standards and Technology
Reliable information and quality data are the foundations of the PHM philosophy. Qualifying that data for a range of applications can build trust in end users by providing expectations and limits to how the data should be used. This can also aid developers and solution providers who need an understanding of the data to make the best use of its capabilities. Understanding information, such as where the data comes from and how it can be used, is integral to the creation of optimal intelligent systems, viable models, and trustworthy information capable of providing actionable decision support.
 
This panel seeks to discuss the mechanisms for qualifying data collection, documentation, and use as it applies to specific domain applications within the PHM community. Although some qualifications of data are agnostic of application, other questions such as ‘how much data do I need’ or ‘is this an acceptable level of uncertainty’, can only be answered within the context of the end goals and application. Some data collection and storage methods may also dictate the capabilities of that data. Ex. just because a data set is appropriate to build a time series model – it may not work for frequency. Can metadata or data provenance help to communicate this type of information? The goal of this panel is to present and discuss mechanisms for measuring the quality of the collection, use, and return on investment for data and any associated models primarily with current goals in mind while leaving room for potential expansion in the future.
List of Panelists:
TBD
Panel 10: DOD/Fielded Systems
Lead: TBD
TBD
List of Panelists:
TBD