East Japan Railway Company (JR-East) Chooses PARC to Deploy Condition-Based Maintenance
Palo Alto, CA — PARC, a Xerox company, announced today that the East Japan Railway Company (JR-East) selected PARC to deploy its hybrid Condition-Based Maintenance (CBM) technology approach, which combines system modeling and data analytics. Moving from a traditional schedule-based maintenance approach, PARC is helping JR-East transform the way maintenance operations are performed. The initial pilot includes deployment of PARC CBM projects for train doors and track condition image analytics.
PARC’s CBM technologies have been successfully demonstrated in other systems of interest to JR-East such as rail switch machines. Critically, JR-East has realized very high fault detection accuracies from the PARC algorithms, in some cases well exceeding 90%, with negligible false positive rates.
The proliferation of connected sensors, together with advances in data analytics, is enabling maintenance operations to be performed on the basis of actual conditions of the assets, over the traditional schedule-based maintenance that most organizations currently practice.
“Our growth has decreased due to population decrease, so we want to reduce spending on maintenance activities to help improve the bottom line,” said Atsushi Yokoyama, Director General, R&D Center of the JR-East Group. “We examined multiple organizations to help us determine an innovative and futuristic approach to maintenance, and decided to evaluate PARC’s CBM approach. We have been pleased with the pilot results. We are now in discussions around operationalizing the developed CBM solutions with PARC and productizing partner Nomura Research Institute (NRI) for train doors and rail maintenance operations. We look forward to continuing our relationship with PARC and NRI to enable CBM for other train systems of interest.”
The initial JR-East pilot and extended field trials have paved the way for additional projects. PARC’s CBM technology will be extensively field-tested by relevant maintenance teams.
“These problems of labor intensive and schedule-driven maintenance are not unique to Japan or even train systems, and is a pain point across many countries and varying types of infrastructure systems,” said Anurag Ganguli, PARC Senior Research Scientist and Project Lead for the JR-E CBM engagement. “Even within an industry vertical, such as railways, each piece of equipment or asset is unique. Developing technologies that can scale to address each challenge while simultaneously achieving high true positive and low false positive rates is essential. PARC’s multi-disciplinary team is developing a core set of CBM technologies based on model-based reasoning and machine learning that can do just that.”
PARC’s CBM technology is driven by its strong, interdisciplinary expertise in system sciences and engineering. PARC’s low-cost embeddable sensors can be placed into the heart of systems in a non-invasive manner to directly monitor system state. The sensor data is fed into system models that can be automatically augmented with a range of faults that can be anticipated for the system. This allows PARC to develop a deep understanding of how the system works and fails, enabling rapid development of informed failure-sensitive algorithms to detect abnormal system operation.
This understanding feeds into model-based diagnostics that PARC is renowned for pioneering, allowing for highly accurate classification of system faults and isolating vulnerable subsystems that lead to system failures. Additionally, PARC CBM components feed into prognostics algorithms that can predict failure points under different expected load scenarios. Finally, this information is combined into condensed recommendation algorithms with actionable suggestions for optimal responses by operations teams. In addition to the engagement with JR-East, PARC has successfully demonstrated its CBM technologies for leading organizations across different application verticals such as energy, infrastructure, defense, transportation, and manufacturing.
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