AI for Predictive Maintenance seminar series – August 29th
Using AI/ML in Improving Industrial Reliability through PM
Predictive Maintenance (PM) is becoming ubiquitous for improving availability and reliability along with reducing O&M costs in industrial systems.
Despite significant research and development investment in the last decade most deployed solutions still tend to be piecemeal (component or failure mode specific) point solutions and generally lack trust with respect to automated decision making. Full end-to-end deployment with system-wide coverage and autonomy still remains an elusive goal in industrial setting.
This is primarily due to high cost and limited scalability of conventional modeling approaches for underlying complex systems and processes in large fleets. Specifically, capabilities to safe-guard against unknown-unknowns, lack of explain-ability and trust tend to be key bottlenecks. Given these systems are heavily instrumented generating large volumes of high-speed data and compute costs continue to go down, recent advancements in data-driven methods using machine learning (ML) and artificial intelligence (AI) have shown promise in a number of areas that previously led to valley of death between PM technology and commercialization.
GE’s Digital Twin technology for Predictive Maintenance is leveraging AI to bridge a number of such critical gaps that were otherwise very challenging to tackle through conventional methods.
This session will enumerate key challenges in enabling system-wide predictive maintenance and how AI is being used to overcome these. Specifically, a causal deep learning-based approach will be described that provides a causal graph of inter-variable relationships allowing validation of deep learning model with domain experts. Further, by providing causal factors for identified anomalies root cause analysis can be facilitated for alert disposition in efficient manner at the fleet level. We will also describe our approach towards competency awareness of AI models, which aims to solve uncertainty management and trust for industrial applications of AI. Various applications and use-cases will be shared to show effectiveness of AI and ML using both structured and unstructured data in the context of intelligent PM.
Speaker: Dr. Abhinav Saxena is a Principal Scientist in AI & Learning Systems at GE Research. Abhinav has been developing ML/AI-based PHM solutions for various industrial systems (aviation, nuclear, power, and healthcare) at GE and has been driving integration of AI-based PHM analytics in GE's industrial systems. He is the PI for ARPA-E GEMINA program on AI-Enabled Predictive Maintenance Digital twins for Advanced Nuclear Reactors.
Using AI/ML in Improving Industrial Reliability through PM
Predictive Maintenance (PM) is becoming ubiquitous for improving availability and reliability along with reducing O&M costs in industrial systems.
Despite significant research and development investment in the last decade most deployed solutions still tend to be piecemeal (component or failure mode specific) point solutions and generally lack trust with respect to automated decision making. Full end-to-end deployment with system-wide coverage and autonomy still remains an elusive goal in industrial setting.
This is primarily due to high cost and limited scalability of conventional modeling approaches for underlying complex systems and processes in large fleets. Specifically, capabilities to safe-guard against unknown-unknowns, lack of explain-ability and trust tend to be key bottlenecks. Given these systems are heavily instrumented generating large volumes of high-speed data and compute costs continue to go down, recent advancements in data-driven methods using machine learning (ML) and artificial intelligence (AI) have shown promise in a number of areas that previously led to valley of death between PM technology and commercialization.
GE’s Digital Twin technology for Predictive Maintenance is leveraging AI to bridge a number of such critical gaps that were otherwise very challenging to tackle through conventional methods.
This session will enumerate key challenges in enabling system-wide predictive maintenance and how AI is being used to overcome these. Specifically, a causal deep learning-based approach will be described that provides a causal graph of inter-variable relationships allowing validation of deep learning model with domain experts. Further, by providing causal factors for identified anomalies root cause analysis can be facilitated for alert disposition in efficient manner at the fleet level. We will also describe our approach towards competency awareness of AI models, which aims to solve uncertainty management and trust for industrial applications of AI. Various applications and use-cases will be shared to show effectiveness of AI and ML using both structured and unstructured data in the context of intelligent PM.
Speaker: Dr. Abhinav Saxena is a Principal Scientist in AI & Learning Systems at GE Research. Abhinav has been developing ML/AI-based PHM solutions for various industrial systems (aviation, nuclear, power, and healthcare) at GE and has been driving integration of AI-based PHM analytics in GE’s industrial systems. He is the PI for ARPA-E GEMINA program on AI-Enabled Predictive Maintenance Digital twins for Advanced Nuclear Reactors.
