What Is a Digital Twin?
Network World has a great overview of digital twins here. It summarizes a digital twin as ” a digital representation of a physical object or system.” These are typically a combination of supply chain information, engineering models, data science models, and historical operational + failure data. The goal of a digital twin is to better understand the operation of a system or asset, optimize usage, reduce maintenance cost, along with many other business value drivers. As data storage and cloud based systems have plummeted in price, this has allowed large and complex systems connecting different technologies and data into a digital view of the expected current and future state.
Who Are The Largest Players?
A recent 2018 report identifies companies like GE, PTC, Siemens, Microsoft, PTC, etc as holding the largest market share and success. Original equipment manufacturers (OEMs) have the greatest competitive advantage given their deeper understanding of the design, supply chain, and operation of the equipment they have provided. Often times in industries such as renewable energy, the OEMs regularly operate the equipment for an extended period of time, giving them another competitive advantage as the largest operators of the equipment.
What Does a Digital Twin Need?
As digital twins become more popular, industries are adjusting their understanding of what is needed from hardware and software. This is particularly impactful in the area of Industrial IoT, where industries are identifying new data points necessary to support better digital twins. This has led to a significant jump in the acceptance that new innovative sensors can provide higher return on investment (ROI) than previously without digital twin technology.
Some areas that Poseidon Systems is innovating sensor technology to support digital twins are within wear debris sensing and online oil quality monitoring. Poseidon’s DM4500/4600 is the fastest growing wear debris monitor in the world, passing 5,000 by the end of 2019 and more than doubling to >10,000 in 2020. This is because only Poseidon Systems provides a full end to end solution that utilizes more advanced data analytics techniques designed to integrate into end customer machine learning solutions.
This innovation in design for digital twin unlocks an ability to use the sensor as a life gauge. This is a unique approach using wear debris data as a closed loop feedback giving the operator the ability to adjust the operation and maintenance of a piece of equipment to optimize its rate of failure. On a larger scale, as this data is collected it is fed into digital twin tools and combined with other operational data to build digital twin models on how the system will react. While Poseidon Systems does not provide digital twin solutions, our customers are using this data to build digital twin models to optimize the performance of their equipment.
Within online oil quality monitoring, Poseidon’s QM/QW3100 is being utilized to move from hours based oil changes to true conditions based. Operators are now able to reduce offline oil analysis and eliminate unnecessary periodic sampling.
Design for digital twin is critical getting the most out of new sensor technology. Further, digital twin providers are partnering with companies like Poseidon Systems to advance their models through more meaningful real time operational data. As Poseidon Systems continues to innovate, our focus will be on how our sensor fit within the new paradigm of digital twins.
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