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Industrial Robotics Cyber Security Challenges in IIoT

Industrial Robotics Cyber Security Challenges in IIoT

The line is blurring between information technology (IT) and operational technology (OT). As more industrial robotics equipment is connected to the industrial internet of things (IIoT), the vulnerabilities increase. Among the many devices being added to networks are robotic machines. That’s raising red flags for some experts. And it has many people worried. What are the risks associated with connecting an army of robots? It’s the stuff of science fiction.

 

Industrial Robotics Cyber Security Concerns on the Rise

 

The World Robotics Report 2016 gives us some insight into the scope of global automation growth: “The number of industrial robotics deployed worldwide will increase to around 2.6 million units by 2019.” It says that the strongest growth figures are for Central and Eastern Europe. The report cites China as the market for growth, and says that North America is on the path to success. “The USA is currently the fourth largest single market for industrial robots in the world,” according to the report.

TechCrunch contributor Matthew Rendall says “Industrial robotics will replace manufacturing jobs — and that’s a good thing”. He writes that the “productivity growth” behind 85% of job losses is all about machines replacing humans. Luddite and famous poet Lord Byron would not have been pleased. But Rendall is not bothered. He says that “more is getting done” by industrial robotics that are safer and more reliable than human beings.  And he believes that this robotics revolution will be beneficial to workers and society in the long run.

All this rush to automation might be the best thing since jelly doughnuts. But one question could make all the difference between abysmal failure and glorious success:  Can we keep them secure?

Challenge in Industrial Robotics Cyber Security

 

We probably don’t need to worry about robots taking over the world any time soon. (Let’s hope, anyway.) What concerns security experts is that our computer-based friends can be hacked. Wired Magazine reports how one group of researchers was able to sabotage an industrial robotics arm without even touching the code. That’s especially worrying when you think that most industrial robotics have a single arm and nothing else. These devices are made to make precise movements. Hackers can change all that.

Industrial Robotics Cyber SecurityGerman designer Clemens Weisshaar addressed the issue in a form at Vienna Design Week in 2014.  “Taking robots online is as dangerous as anything you can put on the web,” he said. In a video from the forum, Weisshaar talked about how even his company’s robot demonstration in London had been hacked within 24 hours. They even tried to drive his robots into the ground.  “If everything is on the internet,” he said, “then everything is vulnerable to attack.”

Industrial robotics cyber security challenges are only one part of what many are calling Industry 4.0. It’s a trending concept — especially in Germany — and it’s another way of referring to the Fourth Industrial Revolution. To understand what this is about, we should first reach back in the dim recesses of our minds to what we learned in history class in school.

The Industrial Revolution, as it was originally called, took place in the 18th and 19th centuries. It started in Great Britain and involved the harnessing of steam and tremendous advances in production methods – the 1st.  Next came the 2nd roughly from 1870 until World War I in the USA. This involved the use of electricity to develop mass production processes. Th 3rd brought us into the digital age. Part four is upon us now.

A video from Deloitte University Press introduces us to the Fourth Industrial Revolution — Industry 4.0. It gives a good summary of the four “revolutions”, and it talks about some of the new technologies that now define our age:

  • Internet of Things (IoT)
  • Machine Learning
  • Augmented Reality
  • Mobile and Edge Computing
  • 3D Printing
  • Big Data Processing

“These technologies,” says the narrator, “will enable the construction of new solutions to some of the oldest and toughest challenges manufacturers face in growing and operating their business.” They also make up the environment in which hackers flourish.

Industrial Robots Cyber Security Challenges for IoT

 

In this space we have already discussed the security vulnerabilities of IoT devices. We told you how white hat hackers proved that they could commandeer a Jeep Cherokee remotely by rewriting the firmware on an embedded chip. Imagine what hackers with more sinister motives might be planning for the millions of robotic devices taking over the manufacturing shop floor — supposing they are all connected.

Some researchers tackled the issue in a study called “Hacking Robots Before Skynet”. (You will remember from your science fiction watching that Skynet is the global network that linked robots and other computerized devices in the Terminator movie franchise.) The authors had a lot to say about the current state of cybersecurity in the industrial robotics industry.  We can borrow directly from the paper’s table of contents to list what they call “Cybersecurity Problems in Today’s Robots”:

  • Insecure communications
  • Authentication issues
  • Missing authorization
  • Weak cryptography
  • Privacy issues
  • Weak default configuration
  • Vulnerable Open Source Industrial Robotics cyber security Frameworks and Libraries

Each of these topics could probably merit a full article on its own. The researchers explained further: “We’re already experiencing some of the consequences of substantial cybersecurity problems with Internet of Things (IoT) devices that are impacting the Internet, companies and commerce, and individual consumers alike,  Cybersecurity problems for industrial robotics could have a much greater impact.”

What might that impact be? Well, to start with, robots have moving parts. They tell how a robot security guard knocked over a child at a shopping mall. A robot cannon killed nine soldiers and injured 14 in 2007. And robotic surgery has been linked to 144 deaths. It’s not Skynet yet, but connecting robots has its risks.

How we communicate with machines and how they communicate with each other are matters that require significant attention. Arlen Nipper of Cirrus Link Solutions talks about MQTT, which is a protocol for machine-to-machine (M2M) messaging. Manufacturing designers and operators send instructions to one-armed industrial robotics, who work in a variety of industries from automotive to aerospace to agriculture to packing and logistics. All this talking back-and-forth with industrial robotics cyber security has to be regulated. NIST’s Guide to Industrial Control Systems (ICS) Security has a few references to robots. But maybe not enough.

Distributed Power Generation Balance Evolving Utility Grids

Distributed Power Generation Balance Evolving Utility Grids

Distributed power generation and other distributed energy resources (DERs) in the modern power grid is undeniable. It seems that electric distribution companies have two options: fight the inevitable rise of DERs or embrace them and benefit from new opportunities. After years of resistance, the time has come to enable the deployment of DERs by restructuring not only grid infrastructure and technology but also rethinking utility revenue models.

Don’t Resist, Restructure Distributed Power Generation

 

With the pace that DERs are being deployed it makes sense for utilities to embrace new technologies and their associated challenges, but there are still battles to be fought. It is unlikely that utilities will ever be comfortable with net metering policies that reimburse distributed generators at their billing rate.

Many states are changing their net metering policies to either add fixed fees to net metering customers or to reduce reimbursement to the wholesale distributed power generation rate, but that fight is far from won. It is obviously unsustainable and unacceptable for utilities to lose massive revenue streams to distributed generation and energy efficiency while also being responsible for maintaining an increasingly expensive system to support these DERs, but fighting net-metering and government subsidies doesn’t have to be the solution.

Although revenue is lost to power generators, there is also untapped potential from DERs that is not being exploited because of the way that utilities earn on capital investment. While utilities dismiss net metering as unfairly shifting costs, a similar argument of unfairness could be made for guaranteed return on capital investments.  Currently, utilities are incentivized to build distributed power generation infrastructure because they earn on those projects, but they are not incentivized to solve problems efficiently. Using grid-scale storage to offset an 18-million-dollar transmission investment is a nightmare for utility revenue despite being a simpler and cheaper solution. Perhaps it is time that utilities earn on the services they provide rather than the infrastructure they build.

Electricity as a Service

 

Electric power is bought and sold as a product. Customers pay for how much power they use. This model works very well until customers start making their own product. While utilities understand that they are providing the infrastructure that enables the customer to utilize their power, customers and legislatures rarely understand or care to see the difference. Since many of us already see grid infrastructure as a service that enables the consumption of power, it is only natural to formalize that notion and create new business models that align with selling a service.

Can control be localized based on utility specifications or should it be centralized? Will locational marginal pricing be calculated on a decentralized system and how will that impact the economics of DERs?  These are difficult questions, but utilities should play a critical role in answering them.

Adapting the Utility Workforce

 

Distributed-Power-Generation-and-DistributionNot many utility engineers have experience analyzing terabyte sized data sets and implementing drone-like distributed power generation control systems.

The skillsets of utility engineers and analysts need to adapt in order to keep up with these changes. How can we expect a utility to transform into a DSP without a workforce that can help build and maintain the platform?

With such a massive disconnect between traditional utility operations and the way a modern grid full of DERs must operate, it makes sense for utilities to invest in tech startups. While larger companies are investing in these startups, it makes sense for any size utilities to utilize their skills and platforms.

Regardless of how much the utility workforce may evolve, there will still be an increased dependence on these third-party tech companies to enable many of the advancements that will allow DER integration. We will still need a traditional workforce to design substations, size equipment, manage projects, and maintain GIS records. Partnerships with startups and tech companies can help close the gap between the keeping-the-lights-on workforce and the grid-of-the-future skill sets.

Take a company like Enbala Power Networks, which enables utilities to “aggregate, control, optimize, and dispatch distributed power generation energy in real time”. Partnering with companies like Enbala to perform demand response, peak load management, and a multitude of other services can allow utilities to maintain a focus on their traditional skills while still enabling a completely modernized grid.

Distributed Power Generation in Disruptive Technologies

 

Disruptive technologies such as DERs are often seen as the downfall of the industries that they disrupt. But unlike many other industries, the role of the utility in the power grid is so critical to society that it is unlikely utilities will ever go extinct. However, it is up to utilities themselves to decide how to respond to the changing grid.  Is it possible to resist new technologies and revenue models and instead continue to focus on capital investments and regulated business?

Would it be better help enable these new technologies and reap the benefits provided by a paradigm shift in the industry?  Certainly, utilities will mitigate risk by combining these two strategies. Duke Energy, for example, continues focusing on its regulated business while ramping up investments in renewables and new tech. It is transitions like these that will allow utilities not just to survive, but to thrive in the modern distributed power generation industry.

Trends in Geothermal Power Generation

Trends in Geothermal Power Generation

Trends in geothermal power generation is technology has some promise  as being proven to be a clean, renewable resource providing energy around the world for centuries in various forms of hot springs.  Keeping special areas with signs like hot springs aside, the heat of the earth is available for everyone everywhere.

Modern use of geothermal energy include electricity production, heat source applications for industrial purposes, and commercial as well as residential HVAC purposes through geothermal heat pumps.

Trends in geothermal power generation shows that plants use geo-fluids extracted by drilling wells into a geothermal reservoir. Such plants pose three main challenges in exploiting geothermal energy for power generation:

  1. High cost and risk of exploration and drilling of a well (around USD 10 million per well)
  2. Low temperature (typically in the range of 80 – 300 degree C)
  3. Disposal or re-injection of toxic brine that comes out of geothermal reservoir

Whenever high temperature super-heated steam is directly available from geothermal wells it can be used with steam turbines for power generation. But this is not the case with low temperature geothermal reservoirs.  Low temperature geo-fluids require use of Organic Rankine Cycle (ORC) turbines through heat exchange mechanisms. This adds to the cost of geothermal power plant as compared to those using steam-turbines, in addition to the cost of wells. However, the high cost of drilling a well can be avoided by selecting abandoned oil wells which have depleted hydrocarbon reserves.

Trends in Geothermal Power Generation since 1989

 

US Department of Energy (DOE) test operated such a plant in 1989 demonstrating depleted reservoir conversion to geo-pressurized thermal power plant as part of its geo-pressured-geothermal energy program. The program aimed to utilize the heat brought to surface in the form of produced hot water (thermal energy), burning any entrained hydrocarbons on site for generating electricity (chemical energy) and high well head pressure (mechanical energy) to generate electricity. Pleasant Bayou in Brazoria County in Texas was chosen as the site for the power plant.

The plant generated electricity from the geo-fluid and separated the natural gas to test the production of electricity from combustion in an on-site hybrid power system.

Trends in Geothermal power generation The binary power plant with a design output of 905 KW (541 KW from ORC turbine, 650 KW from gas engine and subtracting an operational load of 286 KW). The plant operated at only 10,000 bbl of water per day with small volumes of gas flow.

Bottom hole temperature was given as 154 degrees C, with a maximum brine T of 136 degree C. The overall plant availability was 97.5%, at par with many other geothermal plants.

BP Statistical Review 2016 reported total consumption of coal, natural gas, oil, nuclear, hydro-power and renewables as 13147.3 MTOE in year 2015 to produce electricity.  The renewable sources including geothermal power generation contributed 364.9 MTOE (on the basis of thermal equivalence assuming 38% conversion efficiency in modern thermal power station) which is less than 3%.

The representative of Enel Green Power Innovation Department has following views on the future of geothermal power plants:

 “Renewable sources can interact between each other in order to fully exploit the characteristics of the single technologies and to use Balance of Plant to increase utilization factor,”

Hybrid among Trends in Geothermal Power Generation

 

Enel Green Power has taken lead where 33 MW Stillwater geothermal power station in Nevada was commissioned in 2011, got paired with 26 MW of photovoltaic facility in 2014 and another 17 MW CSP (Concentrated Solar Power) facility in 2016. The triple hybrid power plant has been reported by National Renewable Energy Laboratory to achieve 5% reduction in the levelized cost of energy (LCOE).

Like solar energy, the resource is indefinitely available with demonstrated potential of these trends in geothermal power generation via hybrid power systems as reliable source of green energy which is now receiving the attention of engineers, technologists and investors in proportion to the benefits that it will deliver.

Electrical Power Generation Distribution sees Seismic Changes

Electrical Power Generation Distribution sees Seismic Changes

Electrical power generation and distribution took a big leap in 2007 when the trajectory for electrical use in America peaked and started down a different course, declining for reasons we don’t fully understand yet. No, this wasn’t a one-time drop but a clear shift, moving in a new downward direction that continues to this day. While the seismic forces in electrical power generation are occurring, there should have been celebrations and parades, even dancing in the streets, but no one noticed.

In much the same way animals, not humans, are able to pick up on weak signals for an impending earthquake, our ability to sense an industry’s peak still mystifies us. To make matters even more complicated, it may not be the peak.

Seismic Forces in Electrical Power Generation

 

 

Electrical Power Generation Seismic ChangesThe future of electricity can best be broken into four fundamental categories – power generation, power distribution, electric storage, and changes in demand.

After looking at some of today’s most important trends, it was easy to uncover a few emerging trends that analysts haven’t been considering.

While some of these may only represent a miniscule probability over the next few years, the interplay between emerging technology and social acceptance, coupled with an exponential growth curve or two inserted into the mix, will make electrical power generation and the energy industry a truly dicey market to predict over the next 2-3 decades.

Our emerging electric car and trucking industries coupled with plummeting battery prices, solar roofs, IoT devices, industrial automation, artificial intelligence, home battery packs, and energy efficient everything are just a few of the interrelated issues that will turn virtually every prediction about our future electrical power generation and distribution needs into a low probability forecast before its even mentioned.

Full article on Seismic Forces Change Electrical Power Generation

 

The electrical power generation industry has already entered a state of disruption, but is ripe for much more. Today’s politics will be a distant memory 2-3 decades from now.  At the same time, wind and solar have proven to be a lower cost form of electric power generation across some parts of  the U.S., even without subsidies. Renewables are already at grid parity and will continue to drop in price.

Electric power will endure to be a battleground industry for decades to come. Our shifting base of technology, startups, lifestyles, culture, and politics will continue to make this a highly unpredictable landscape for the foreseeable future.

Read the full article on seismic forces changes in Power Generation and Power Distribution from the futurist Thomas Frey

Industrial IoT Predictive Maintenance – a Killer Application

Industrial IoT Predictive Maintenance – a Killer Application

Industrial IoT predictive maintenance is expected to generate the large scope of B2B transactions that require data analysis.  Indeed, IIoT is on such a growth pattern many of the billions of connected things in the coming years will be industrial assets, which will be deployed in settings like factories, agricultural, oil refineries and energy plants.

According to McKinsey the Industrial Internet has the potential to deliver up to $11.1 trillion on an annual basis by 2025 and 70% of this is likely to concern industrial and business-to-business solutions i.e. the Industrial IoT is expected to be worth more than twice the value of the consumer internet.

The Industrial IoT is at the heart of the fourth industrial revolution (Industry 4.0), which is driven by the interconnection of all industrial assets and the ability to collect and analyze data from them. In the scope of the Industrial IoT, assets are cyber-physical systems, which enable the control of physical devices through their cyber representations and the processing of digital data about them.

The applications of cyber-physical systems span a very broad range, including production control, process optimization, asset management, integration of new technologies (such as 3D printing & additive manufacturing), as well as various industrial automation tasks. Nevertheless, the most prominent application is the ability to continually monitor, predict and anticipate the status of assets, with emphasis on industrial IoT predictive maintenance using predictions about when a piece of equipment should be maintained or repaired.

Industrial IoT Predictive Maintenance Key to Industry 4.0

 

Maintenance and Repair Operations (MROs) are at the heart of industrial operations, as they involve repairing mechanical, electrical, plumbing, or other devices as a means of ensuring the continuity of operations. Nowadays, the majority of MRO operations are carried out on the basis of a preventive maintenance paradigm, which aims at replacing components, parts or other pieces of equipment, prior to their damage that could catastrophic consequences such as low production quality and cease of operations for a considerable amount of time. However, in most cases preventive maintenance fails to lead to the best usage of equipment (i.e. optimal Operating Equipment Efficiency (OEE)), as it maintenance is typically scheduled earlier than actually required.

In industrial IoT predictive maintenance (PdM) alleviates the limitations of preventive approaches. PdM is based on predictions about the future state of assets, with particular emphasis on anticipating the time when an asset will fail in order to appropriately schedule its maintenance.

PdM is empowered by models that estimate when the cost of maintenance becomes (statistically) lower that the cost that is associated with the risk of equipment failure.

Based on an optimal scheduling of maintenance, PdM leads to improved OEE, enhanced employee productivity, increased production quality, reduced equipment downtime, as well as a safer environment where failures are anticipated and repairs proactively planned. McKinsey & Co. estimates that the economic savings of predictive maintenance could total from $240 to $630 billion in 2025.

Nevertheless, there are still many industries that dispose with preventive maintenance, since they have no easy way to integrate and analyze data sets from thousands of heterogeneous sensors that are typically available in their plant floors. As a result only a fraction (i.e. 1% according to McKinsey & Co) of the available data is used, which is a serious setback to unlocking the potential of predictive maintenance applications, such as maintenance as a service, on-line calculation of OEE risk, maintenance driven production schedules and more.

The advent of Industrial IoT predictive maintenance is gradually unlocking the potential of PdM technologies facilitate the collection and integration of data from thousands of different sensors, while at the same time providing the means for unifying the semantics of the diverse data sets. Furthermore, IoT analytics technologies (notably predictive analytics) facilitate the processing of IoT data streams with very high ingestion rates based on machine learning and statistical processing techniques that can predict the future condition of components and equipment.

In several cases, IoT data are processed by Artificial Intelligence based techniques such as deep learning, in order to identify hidden patterns about the degradation of assets. Deep learning techniques are capable of leveraging (multimedia) data from multiple maintenance modalities such as vibration sensing, oil analysis, thermal imaging, acoustic sensors and more. Moreover, advanced deployments of industrial IoT predictive maintenance are not limited to deriving predictions about the future state of assets. Rather, they are able to close the loop down to the plant floor, through for example changing configurations in production schedules, altering the operational rates of machines or even driving automation functions.

Rise of Industrial IoT Predictive Maintenance Products and Services

 

PdM is looming as one of the killer applications for the Industrial IoT, which is evident not only on its potential savings but also on the rise of relevant IoT-based products and services. Most vendors have been recently releasing IoT-based solutions for PdM. In addition to empowering data collection and analytics, vendors are striving to enhance their products with added-value functionalities that help them stand out in the market. For example:

  • IBM predictive maintenance solution is able to perform root cause analysis in a holistic way, including predictions about where, when and why asset failures occur.
  • Software AG’s solution for industrial IoT predictive maintenance integrates with ERP and human resources systems to automatically plan the optimal allocation of tasks to technicians.
  • SAP integrates predictive maintenance information with business information (e.g., CRM and ERP systems) and enterprise asset management (EAM) systems. To this end, it benefits from its strong presence and installed base in the ERP market.
  • Microsoft offers PdM solutions over its Azure IoT suite in a way that offers preconfigured solutions (templates) for monitoring assets and analyzing their usage in real-time.

Recently, the DataRPM platform has been also established by a consortium of different vendors and manufacturers. DataPRM claims ability to deliver Cognitive Predictive Maintenance (CPdM) for Industrial IoT, based on the use of Artificial Intelligence for automating predictions of asset failures and closing the loop to ERP, CRM, and other business information systems.

Other major players in industrial engineering and automation, such as SIEMENS and BOSCH are offering their own platforms, while all major IT consulting enterprises have relevant services in their portfolio. Nevertheless, it is indicative of the market momentum of PdM and its positioning as one of the most prominent applications in the growing Industrial IoT predictive maintenance market.

 

Industrial IoT Automation Digital Wave Whitepaper

Industrial IoT Automation Digital Wave Whitepaper

Industrial IoT automation dictates that all predictive maintenance systems hinge on the processing of data from many IoT devices, which renders predictive maintenance one of the most common applications.

Industrial IoT Automation Challenges

 

 

Industrial IoT Automation whitepaperMoreover, as predictive maintenance leads to improved OEE, reduced labor for performing the maintenance and better planning of related supply chain operations, it is increasingly considered one of the killer applications for IIoT.

IIoT reconfigurations take place at the cyber world based on digital technologies rather than at the physical world where processes are much more tedious and time consuming.  

This ensures that changes in the IT configurations are properly reflected on the field.  

 

Extensive Whitepaper on Industrial IoT Implications

This whitepaper discusses IIoT Disruption and Digital Transformation.  It defines the business cases, predictive maintenance, flexibility in Industrial IoT Automation, optimal Supply Chain operations and how to improve the quality of operations.

Moreover, we identify the simulation of complex processes, technology enablers and building blocks, as well as IIoT’s deployment challenges.  Discover the adaptation and migration of legacy systems; security, privacy, and trust, and leveraging standards in Digital Transformation.

As experts in recruiting senior executives and functional leaders for Internet of Things data and devices, LED lighting, and industrial IoT automation, in this whitepaper we also reveal the talent Gap in IIOT technologies.  The talent gap is evident in senior engineering and executive levels.  Riding the Industry Digitization Wave with IIoT:Challenges and Implications whitepaper.  Click here to view and download.

 

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