Yokogawa unique approach for APM with Sushi Sensor and Machine Learning.

Yokogawa unique approach for APM with Sushi Sensor and Machine Learning

Today everything is being automated and digitally transformed. However, manual operator rounds are still the most common method of checking non-mission critical plant equipment. This limited information about equipment health status causes maintenance to be reactive and ultimately results in equipment and production downtime. By adding a wireless sensor network and AI anomaly detection function, plants can gain visibility to all assets, detect abnormal conditions and take early action to keep the equipment healthy and running optimally.

Hydrocarbon Processing has recognized Yokogawa IIoT Plant Asset Management solution as the Best Asset Monitoring Technology in 2020! Join our webinar to see why!.

In this webinar we will discuss:

IIoT, Asset Performance Management, and Condition Based Maintenance
Benefits of Yokogawa Sushi Sensor based on LoRaWAN® technology and AI anomaly detection function

March 30, 2021 (Tuesday)
04:00 PM (SGT)

Koji Watanabe

Section Manager, AI Business Development

Koji Watanabe starts his carrier as an electrical circuit engineer in 2006 in Yokogawa Electric. After the R&D experience of Giga-band hardware and network algorithm of ISA100 wireless, he moved to the Middle East for business development of the industrial wireless business from 2014 for 4 years. Now he engages in the IIoT and AI business development at Yokogawa HQ.

Who Should Attend?

CEO
Presidents
Vice Presidents
Maintenance Managers
Project Managers
Maintenance Engineers
Operations Personnel
Cloud computing specialists
Chief Engineers
Researchers & Academics

Maintenance Personnel
Managing Directors
Plant Managers
Technical Directors
Operations Managers
System Integrators
Design Engineers
Software Engineers
Investors
R&D Manager