San Jose Water (SJW) is an investor-owned utility providing water service to over 1 Million people in the Silicon Valley and greater San Jose metropolitan area.
The digital transformation journey begins with the development of an overall strategy and the creation of a digital roadmap. Digital transformation integrates many different businesses and functions across the enterprise in order to turn data into actionable information.
With 89 percent of all equipment failures being random, knowing the status of your facility is key to improved operations. To understand the current operating status, plants collect data from a variety of sources.
The manufacturing industry has been following a route-based monitoring approach for ages. Without question, AI and IoT has changed the way we look at condition monitoring and diagnostics.
The application of Industry 4.0 principles often involves the enhanced use of CMMS applications and the automation of data collection, all resulting in the world of Big Data.
Most vibration analysts are operating at full capacity today. They are collecting their route data and analyzing this data month after month. Many of these reliability programs were established many years ago and the evolution of the programs over time have been slow, or maybe non-existent.
It's well-known that the primary costs and losses in processing industries come from sudden failures of equipment. But, why is everyone looking for a digital solution for more precise failure forecasting?
Cascades is an organization of more than 70 plants in the pulp and paper industry. Before implementing a holistic condition monitoring program, every plant had different condition monitoring equipment.
This presentation will focus on how to move your organization to asset condition monitoring 4.0 without losing the value of strategic discussions while focusing on the enhancement of data collection and analysis.
Implementing digital twins is becoming a strategic necessity for many industrial companies. In this session, learn the basics of how digital twin models should be built, how to scale and implement these models on assets and systems to create a twin, and discover best practices for how to leverage these twins operationally to predict, diagnose and forecast performance degradation as a part of your reliability work processes.
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