Siemens Redefining its Role in Industrial Digitalization

Siemens is leaving no stone un-turned while redefining the industrial digitisation. Today, around 900 software developers, data specialists and engineers are already working together with Siemens customers at these centres to develop digital innovations for data analysis and machine learning.

New solutions are being developed on MindSphere, Siemens’ open, cloud-based operating system for the Internet of Things (IoT). To be close to its customers, the company has distributed its 20 centres across around 50 locations in 17 countries worldwide. “We’re continuously expanding our leadership role in industrial digitisation,” said Joe Kaeser, Siemens President and CEO. “With our global experience in electrification and automation and our industrial software expertise, we’re generating optimal benefits for our customers – benefits that no other companies can replicate at such high levels of performance.”

Siemens has launched its MindSphere IoT operating system across the company about a year ago. Around one million devices and systems are now connected together via MindSphere, and this figure is to reach 1.25 million by the end of fiscal 2018. Beginning in January 2018, it will also be available on Amazon Web Services. This partnership brings Siemens and leading cloud solutions provider. As a result, users enjoy the benefits of a more powerful development environment, additional analysis functions and expanded connectivity. Industrial applications and digital services can be developed and run on MindSphere.

To further accelerate the innovation process, Siemens will again increase its research and development (R&D) expenditures in fiscal 2018 and invest an additional sum of around €450 million.   Read more…

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Industrial asset intelligence startup Alchemy IoT closes $4M seed investment

Alchemy IoT, an IoT Asset Intelligence company helping small- to mid-sized industrial customers use AI-capabilities raised $ 4M in seed funding. The investment was led by Aweida Venture Partners.

Alchemy’s Layout Screens

Alchemy’s main differentiation from similar IoT analytics companies is that it eliminates the need to write complex code for customers. Its SaaS (software-as-a-service) application called Clarity is used for asset health monitoring, operations, and maintenance. The system provides extensive API support, from IoT APIs to analytics APIs and component as well as admin APIs. The REST APIs integrate with both cloud-based IoT platforms or directly with customers’ equipment which allows them to access data from assets.

The application’s core features include sensor data monitoring, AI-based analytics, notifications/alerts, and custom capabilities (such as events and dashboards). Alchemy markets its product by stating that it eliminates the need for data scientists and custom coding, though it appears that Clarity would most likely handle regular usage scenarios and leave out edge cases that so often are a norm in industrial use cases.

“Our mission is to make AI-powered IoT a ‘no-code’ proposition, one that any industrial company can quickly start and put to use to gain fast value. Too many of today’s IoT solutions require a massive budget and an extraordinary amount of customization to even getting started – we aim to disrupt and change that.” Victor Perez, CEO Alchemy IoT


Postscapes: Tracking the Internet of Things

Some thoughts about industrial wireless

The pilot distillation plant at UT Austin.

A field trip this week to a pilot distillation facility on the University of Texas campus gave me new perspective on the challenges of delivering wireless in outdoor, industrial settings.

Peter Zornio, chief technology officer with Emerson Automation Solutions, gave me several examples of how wireless could go wrong in these locales. The most obvious challenge is the preponderance of metal in these environments. Wireless signals can’t go through metal, so mesh networks become critical.

Another challenge is the movement of vehicles such as trucks or trains that can get between wireless sensors and gateways. Zornio shared a story about a plant that had an otherwise perfect network that would randomly go down. After some research, the workers realized that a train pulled up twice a day between the facility and the gateway. Mesh networks fixed that as well.

Chemicals are the other big challenge. In a distillation plant (and in other chemical processes) huge vats are filled with liquids that periodically replenish and deplete. Wireless signals aren’t great when it comes to water, so the changing volumes of liquids around the facilities can change the RF environment.

Any good RF engineer can tell you this, but as more and more companies try to build wireless networks in their own warehouses, or even in their stores, it’s worth a reminder that mesh networks are generally good and the RF environment matters tremendously.

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The evolution of industrial revolution – IIoT

There was a catastrophic incidence of oil spillage from the oil rigs in Gulf of Mexico in April 2010. The drilling rig ‘Deepwater Horizon’ had spilled about 185 million gallons of oil into the sea, endangering breeds of marine creatures. Later, forensic reports proved that a puncture in the pipe due to oil and gas pressure had created the havoc. Had there been a smart automation system in place, the fault would have been detected at an early stage, hence avoiding the mishap.

This incident highlights the necessity of smart automation in industries. And as it is said “necessity is the mother of disruption,” the dire need of a safe, secure and efficient industrial system gave birth to the Industrial Internet of Things or IIoT.

The IIoT roadmap

There was a time in the 18th century when industrial machines were powered by steam. Industry 2.0 was powered by electric energy and industry 3.0 went autonomous. Now we have industry 4.0, which runs on cyber-physical systems.

This paradigm shift of the industry norms from manual to smart automation is helping to remotely monitor and control every part of the industrial facility.

“Later, five considerations were taken for enabling IIoT—distributed intelligence, rapid connectivity, establishing open standards and systems, real-time context integration and autonomous production lines,” says Narendra Sivalenka, senior manager-IoT team, Cyient.
Extracting data from all systems or extricating data from the main system and placing it into different machines in perfection is the distributed intelligence in the IIoT. This data is then connected with each other in a network after integration. Later, as per customer needs, this data is extracted in a smart way to avoid monotony of work, maximise throughput and minimise labour cost.

Technology behind the IIoT

Hardware components of the IIoT include sensors, RFID, condition monitoring, distributed control systems (DCS), smart meters, camera system, industrial robotics, AHS and networking technologies. Product lifecycle management (PLM) systems, manufacturing execution systems (MES) and supervisory control and data acquisition systems (SCADA) are the software parts.

Processor technology, artificial intelligence, DRAMs and memories, and virtual and augmented reality are fields of the IIoT where something exciting is happening on a regular basis.

Flexible glass sensor (Image courtesy: httpslmts.epfl.ch)

Flexible glass sensor (Image courtesy: httpslmts.epfl.ch)

Driving circuit

As mentioned in ‘Industrial Developer Boards’ article of July issue, these boards are undergoing a complete technology change. These find various IIoT applications including robotics, automotive manufacturing, power grids and many more.

The RF-based detector in industrial developer boards allows 3D mapping of objects using time-of-flight concept. Thus, antennae are embedded in the system itself, making the board condensed while reducing processing time. Such industrial developer boards are beneficial for robotics.

Footprint reduction of FPGAs, antennae and other critical components is creating a boom in the industrial IoT segment. Reducing the component size not only helps in reducing the manufacturing costs but also in thermal suppression. Be it the power supply section for a developer board serving the pick-and-place machine or a developer board for smart lighting, thermal dissipation is considerably low.

For applications like motor drives, machine-to-machine communication and smart grids, real-time processing and analysis, longevity, warranty and flexibility are key factors that make industrial boards the preferred option over normal boards. Typically, these can withstand industrial temperatures ranging from –40 degrees to 80 degrees centigrade. There are also a few developer boards meant for ovens, which can withstand temperatures of more than 300ºC.

Sensor integration allows a device to perform several functions. For instance, automobiles are now well-equipped with Bluetooth, Wi-Fi, indicators, odometer, fuel indicator, theft alarm and many such features.

Similarly, infotainment system, cluster, automotive and automotive advanced driver assistance are now integrated on a single MCU, thus reducing the wire harnessing and simplifying the anatomy of cars, making them safer as well as far more efficient.
In these cases, developer boards are designed to integrate a number of functions, which helps to reduce the design costs and space requirements.

Smart camera and imaging system

Similarly, imaging is no longer restricted to laboratories only. It is now gaining significance at the industrial level as well.

A leading automobile manufacturing company employed a robotic arm for automation, which was used to detect the objects to pick and assemble only at a spatial frame and angle. But, even if there was no work, the robotic arm continued to move, which reduced its service life and wasted electricity.

Now, with smart automation, robots can easily identify objects in 3D space too. This is particularly helpful for the automation segment, where robot arms could identify the objects to be picked and placed in a single direction only. Thus, 3D vision is making manufacturing smarter, saving time, electricity and human efforts.

New measuring techniques have come up for the automotive industry’s paint lines, which combine deflection in 3D and 2D surface inspection. This technology in cameras controls the quality 100 per cent and the inbuilt sensors deliver precise automated results in real time.

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Industrial IoT analytics company UpTake nabs $117M Series D

UpTake, an Industrial-IoT analytics company yesterday raised a Series D round of $ 117M at a post-money valuation of $ 2.3 billion. Investment firm Baillie Gifford led the latest round that brought UpTake’s total funding to $ 250M.

SM

Uptake’s advanced analytics platform

UpTake is a SaaS (software-as-as-Service) product capable of reading ‘machine sensor data’. Its predictive analytics software collects and interprets sensor data for clients in the mining, rail, energy, aviation, retail, and construction industries. The software further utilizes the ML (machine learning) technology to predict incidents/events for the monitored machinery.

The company is trying to go after ‘heavy’ industries like oil and gas and energy. “We’re on a growth trajectory now where there is virtually nothing standing in our way from being the predictive analytics market leader across every heavy industry, from oil & gas to mining and beyond,” said Uptake Co-founder and CEO, Brad Keywell. Brad is a co-founder of Groupon as well.

Two primary use cases of UpTake’s technology and products are predictive and preemptive maintenance for the industrial machinery. The startup boasts customers such as Caterpillar, Berkshire Hathaway Energy, and Panduit.

Two key competitors of the company are SparkCognition and Konux. The former raised a $ 32M Series B round in July this year. The Chicago-based company has several pending patents while one of the key patents it currently holds deals with the adaptive handling of the operational data (coming directly from machine sensors).


Postscapes: Tracking the Internet of Things