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Model-aware methods for effective system management solutions

The reliability and uptime of a myriad of infrastructure systems is becoming increasingly critical. As services budgets continue to be stretched - maintenance, operations, manufacturing, and design teams are under tremendous pressure to maximize system life and utilization without compromising safety and operational uptime. PARC’s technologies empower engineers, operators, and maintenance personnel to improve the reliability and maintainability of critical systems and transition from conventional schedule-driven inspections to effective condition-based maintenance (CBM).

The video below provides an overview of PARC's CBM technology suite of innovative software and hardware technologies that work together to offer insights into system health, safety, and performance. The CBM suite leverages PARC’s sensing, modeling, diagnostics, machine learning, predictive analytics, rapid prototyping, and artificial intelligence capabilities and patent portfolio that have been being developed and perfected over more than a decade.

Proven for many critical applications with global clients

Major organizations are already taking advantage of PARC’s CBM technology suite, including East Japan Railway, BAE Systems, General Motors, LG Chem Power, IHI, and Xerox, among others. Train systems, HVAC, elevators, transportation infrastructure, energy storage, and smart manufacturing are just a few industries and systems being disrupted as a result of PARC’s CBM technology. PARC’s CBM is a model-based approach, enabling higher than 90% accuracy and negligible false alarm rates, arming our customers with actionable data for informed deployment. It gives them the ability to deeply understand their systems to smartly manage and keep them in top shape, or be able to know when to take them offline to fix them before any unfortunate events.

CBM: a step towards self-aware systems

CBM is a step towards PARC’s broader mission to enable self-aware, self-adaptive systems. This is driven by PARC’s strong foundation of model-based methods to facilitate a potent first-principle representation of systems. This gives the system a deeper “self-awareness” and makes it easier to scale up to other conditions and climates and enables other system management functions. PARC is working toward enabling a paradigm where systems manage themselves autonomously with minimal to no human intervention, taking system uptime, performance, maintenance, and reliability to the next level.

 

 

testimonials

“We are honored that such a highly regarded organization as PARC, with extensive expertise in system diagnostics, prognostics and condition based maintenance, has chosen ESI to industrialize one of their breakthrough technologies. Building on our existing expertise, the results from research conducted at PARC will enable ESI to deliver industrial solutions to answer system level challenges and to leverage the data generated by our customers systems during operations. Of course, we are also excited by the impact on our efforts to build a strong eco-system in San Francisco Bay Area and its hyper dynamic and innovative Silicon Valley."
Fadi Ben Achour, Electronics Business Development VP at ESI Group

"Our growth has decreased due to population decrease, so we want to reduce spending on maintenance activities to help improve the bottom line. 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."
- Atsushi Yokoyama, Director General, R&D Center, JR-East Group

“We engaged with PARC to immerse ourselves in their distinctive first-principles system model-focused approach to condition-based maintenance (CBM). We have been very satisfied with this collaboration. PARC researchers helped us build detailed physics-based models of interest for CBM that we validated and demonstrated successfully. We are looking forward to building on this success and continue working with PARC to enable effective CBM for the many critical systems of interest to Hitachi,”
- Smart System Research Department Manager, Hitachi

“The key to our success was PARC’s model-based approach, which allowed us to quickly repurpose a solution initially developed for one domain to other complex ones. Plantrol was successfully adapted to different types of IHI’s manufacturing plants. We are a diversified manufacturer producing a wide range of high-end products such as jet engines, rockets, ships, storage, processing plants, and industrial machinery. PARC’s system would help us quickly understand robust reconfiguration options using failures in real-world scenarios and importantly, is scalable across our various plants.”
- Koji Tanaka, Director, Products Development Center, IHI Corporation

“Given the large interest in the Internet of Things and other data-driven applications, we are excited about working with PARC to expand and deepen our reach into our region. We’ve already been working with PARC for a few years in several customer implementations, and we are happy to formalize and expand our work together.”
- Ayumu Ueno, Senior Executive Managing Director and Member of the Board, Nomura Research Institute

"“Our partnership with PARC allows Booz Allen to provide our government and commercial clients with unique and direct access to the rapidly advancing Silicon Valley technology frontier, while providing PARC and Booz Allen with exciting new pathways to accelerate the introduction and adoption of innovation across a broader array of markets. We are already exploring ways to factor PARC’s research and advances in sensor technology and printed electronics into our IoT, unmanned/autonomous systems and vehicles, and next-generation analytics and cyber security products and platforms”
- Michael Farber, Executive Vice President, Strategic Innovation Group, Booz Allen Hamilton. 

“I think your technology is the way of the future”
- Analyst, Modeling and Simulation Systems, US Marine Corps – on PARC’s automated system fault modeling technology.

contact

Aki Ohashi
Director of Business Development

 

 

 

 

 

blog posts   view all 

Predicting Train Breakdowns with the Internet of Things – JR East Collaboration with PARC
posted 24 August 2017

This post is an excerpt of an article by Soji Saito, Nikkei Computer that currently appears (in Japanese) on itpro.nikkeibp.co.jp. JR East’s Innovation Theme: Make train maintenance more efficient Problems Faced in Work: Find fragile points of the facilities, predict parts that are likely to break Solution: Condition-based maintenance (CBM) Organization: Technology Planning Department Outcome: Improved safety and reliability of trains Challenges: Match physical quantity of measured data and type of breakdown “Where on the tracks

Shedding light on advanced batteries to manage them better
posted 10 September 2014

Lithium-ion and other advanced battery chemistries have shown promise over the last decade for driving the world’s clean energy strategy with hybrid/electric vehicles (xEVs), grid storage, and other enabling technologies

A Vision for the Self-Aware Machine
posted 17 December 2013

Sparked by IT megatrends, manufacturers are currently undergoing an operational transformation with increased agility and efficiency as well as fewer operators per machine