Business Intelligence System Infrastructure Algonquin

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Business Intelligence System Infrastructure Algonquin – Siemens invited me to Munich. along with other analysts Another number to attend the innovation conference. This is a showcase of new and upcoming technology. Day 1 was a visit to Siemens’ R&D center in Munich. Day 2 showcased market-ready technology.

Siemens’ reluctance to launch new developments to market is admirable from a quality and reliability perspective. But there’s a risk in a market that’s in need of the latest innovation right now. I feel like Siemens isn’t satisfied with how the solution works. But want all solutions to work?

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Indeed, it has been commented that Siemens has been working on the Digital Twin concept for 10-15 years, but expects it will take another 10-15 years to perfect it.

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Siemens is not just a company that provides software to improve the way manufacturers work. But it is also a producer in its own right. With more than 270 factories worldwide, that is one of the company’s key strengths – in-depth knowledge of the challenges manufacturers face. For customers, this means that solutions are beta-tested by the manufacturing department within Siemens. Before general release

For infrastructure customers The same thing applies. Siemens has its own real estate division, which manages buildings at 2,000 locations, as well as manufacturing. Building technology solutions are beta tested by an internal team.

Siemens has a great story. To tell the story of digital transformation, the redesigned Digital Twin concept with Product, Production and Performance Twins (paired by Design, Build and Operate Infrastructure Digital Twins) is a neat way to bring together Siemens’ traditional strengths. in design and production control alongside newer ‘Industry 4.0’ technologies that are emerging. I hope that this reluctance to send new developments to market will not allow the competition to overtake it and leave it behind.

Disclosure: Siemens paid for the airline tickets. airport transfer and cost of accommodation in Munich Siemens is also a customer.

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Much of the technology is based on the MindSphere cloud system, which is described as an open IoT operating system. Users can access native applications built by Siemens but can also develop their own applications. Includes access to applications written by a rapidly growing ecosystem of third parties.

Every device will be connected and intelligent. But they have to deal with longer cycle times. For example, you might change a cell phone every 1-2 years, but you wouldn’t replace a machine in a factory (or train) for 10 years or more.

MindSphere provides a single, coordinated layer that brings together software tools and physical devices. It is used by early adopters. It’s already a user-friendly platform (as well as internal Siemens users), but it’s still in its infancy in terms of maturity.

Digital Twin is one of Siemens’ core technologies. Like MindSphere, it is already in use – of course, with some basic technologies such as product simulation. well accepted But packaging them into virtual products linked to the real world by MindSphere is still a relatively new concept.

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Knowledge from engineering models is used to improve the accuracy of the Digital Performance Twin, rather than requiring the system to learn everything from scratch.

Generative design modules can learn from performance twins. It helps to design a product that is most suitable for how the product will actually be used in practice. In this way, engineers can increase the quality of their designs.

The digital performance twin performs online simulation during product operation. The simulation is fed with real data from the equipment, allowing the Digital Twin to predict potential problems. For example, it can help the operator determine why a motor is malfunctioning. or predict the lifespan of the product This continuous simulation helps to validate design decisions or improve designs in the future. However, in order to perform this continuous simulation, A much smaller model is required. It went from 1 million degrees of freedom to just 100 degrees.

Some of the benefits expected from using Digital Twins are impressive. Siemens claims that you can accurately predict the temperature of parts inside the motor that cannot be measured by conventional methods. One research demonstration demonstrates this ability. This is coupled with an Augmented Reality user interface that allows you to actually change the speed of the motor.

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Simulation is one of the keys to Siemens’ machine learning strategy. The reason is that even though industrial systems continue to produce huge amounts of data, But sheer variety and even quantity are not the same as consumer data. This means that teaching machine learning systems takes much longer in industrial environments. Siemens intends to run many simulations. times to generate enough data to train a machine learning system in cases where there is not enough real data. Siemens also offers to calibrate simulation models using results from the same machine learning system. The intention to avoid simulation to verify itself is not clear.

A ‘cognitive’ engine will understand those inputs. Reasoning allows the system to draw conclusions from raw data. Learning allows the system to continuously adapt and improve. Creativity allows the system to generate hypotheses.

Various tools They are combined to define a set of operations to be performed. The feedback loop then helps the system learn from its mistakes.

The system brings together many data silos using artificial intelligence and machine learning to simultaneously consume different data signals.

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The challenge for machine learning systems in industrial settings is that Even though there is a lot of information But they are not as useful as consumer applications. This means that machine learning needs to be supplemented with human knowledge.

The Industrial Knowledge Graph is one way Siemens responds whenever there is a lack of data for artificial intelligence to make predictions. Basically It is a way of pre-loading knowledge into the system. Therefore, there is no need to learn everything from scratch. This semantic knowledge means that we automatically know that a lion is dangerous without having to attack it ourselves. In the same way An experienced service technician will notice a problem and know what needs to be fixed based on their prior knowledge of the system.

The combination of these ‘knowledge graphs’ and artificial intelligence algorithms is what Siemens describes as ‘Augmented Intelligence’. A typical workflow is as follows:

The capabilities of the artificial intelligence solution were demonstrated alongside factory automation. The ‘playful’ demo was a demonstration of ping pong being played by two PLCs. The learning mode consisted of observing two humans playing together. From this they derive rules and objectives. Keep in mind that the inputs for the PLC are the pixels displayed on the screen, so AI systems must process large amounts of data in real time to make decisions.

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The second demonstration featured a pair of robots deciding how to assemble electronic components on the fly. Each robot uses a camera to identify the required parts. Then decide what needs to be done to assemble the parts correctly. It doesn’t matter if the part is rounded, sideways, or even somewhere specific, the robot (or at least the AI ​​that drives it) has to find a way to ensure that each part is positioned correctly. Sometimes even a combination of the two robots is used to achieve results. Each build operates slightly differently. But the end result is always the same. It’s clear how slow the system is compared to standard production systems, however, this is only a very early prototype, so I expect similar systems to be much faster in a few years’ time. When the technology is ready for the market

There are other technologies on display. Another number at the research center We were told these were early prototypes. That is at least three years old from general release. Having said that, some of the protests seem familiar.

The Virtual Reality headset allowing service personnel to ‘see’ inside a large gas turbine is very impressive. But it sounds very similar to the solution GE talks about.

Even more impressive is the Augmented Reality solution that simulates the internal conditions of a working motor. But it also allows the user to control the speed of the motor to control the temperature. Again, other vendors have experimented with these to increase service personnel. Although these solutions generally do not allow for control via AR systems.

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During the evening, analysts were invited to attend the Innovation Awards, an internal event within Siemens that honors employees who have made significant contributions to innovation. There was also another award, sponsored by Siemens, which presented Awards innovations that make a significant contribution to the health of the German economy. This seemed a little surreal. This is because the entire ceremony is conducted in English. (official language of Siemens)…

But the purpose of inviting analysts is clear – Siemens wants to highlight the depth and breadth of its innovations. This isn’t just design and automation software and hardware. But it also includes a full range of medical equipment, trains and gas turbines. During this time it is important to remember e.g.

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