Using Functional Safety and Reliability for a competitive advantage

Functional Safety Blog Post

Functional Safety and Reliability is a hot topic that stimulates many discussions, depending on the person’s business and previous experiences. Some see it as complying with the functional safety checklist in their industry, and some see it as costing the risks. For some, it‘s a critical success factor for their brand image and competitiveness in the market.
What is Functional Safety?
According to the Oxford dictionary, safety is the condition of being protected from or unlikely to cause danger, risk, or injury. When discussing products and their development, safety is often defined as the ability of the product to be safe for its intended use. This type of safety is called Technical Safety, also known as Safety of the Intended Function (SOTIF), and is relatively easy to define because it’s passive and designed into the product. It ensures that machines and systems operate in a safe way when they work as designed.
“Functional safety focuses on electronics and related software and activates built-in safety mechanisms to reduce potential risks that could harm somebody or destroy something to a tolerable level.” – IEC
Functional Safety is a little more complicated; it’s active, like an independent control system to ensure safe operation. Safety is achieved by ‘actively’ doing something or stopping something from happening.
Functional Safety – some real-life examples
A simple example of a functional safety system is a domestic coffee maker with a sensor that detects the coffee temperature or the volume of coffee in the flask. If the sensor detects the temperature has exceeded a threshold, it switches the heating element off. Think about the negative business impact on the leading domestic appliance brand if this didn’t work!

Another example of Functional Safety is a forest machine equipped with a safety radar. Should the radar notice any movement around the vehicle, it will halt the movement of the harvester head. The normal environmental conditions of a forest machine can be anything from beautiful sunny weather to a stormy night. When operating in difficult wet snow conditions, the radar sensors can get dirty. Should this happen, the control electronics will notice the unreliable radar signal, and the vehicle will notify the operator for reliability before the safety risk. Think about the responsibility of the control electronics manufacturer!
Components of a safety-related system
The components of a safety-related system are quite basic. They are typically comprised of 3 elements

Sensors – to detect the state of something, e.g. what’s the temperature of the coffee in the coffee maker?

Logic Solver – a programable electronic device to decide what to do, e.g. if there is movement around the vehicle, then warn the operator.

Actuators – to do something, e.g. to isolate the power to the flask’s element or warn the forest machine’s operator about the current reliability and the potential safety risk.

Functional safety doesn’t mean no failures. The standards define a maximum allowable rate of unsafe failures to achieve As Low as Reasonably Practicable (ALARP). As the UK Health and Safety Executive (HSE) states “…making sure a risk has been reduced ALARP is about weighing the risk against the sacrifice needed to further reduce it.”
A common definition of Reliability is the probability that the product will perform its intended function when operating under normal environmental conditions for a specific period of time.

Whilst Reliability Analysis is a well-established branch of engineering, and even though most of the methods and techniques used are very straightforward, it is until recently an under-utilized technology. This is now changing with new proposed legislation such as the Regulation on Ecodesign for Sustainable Products. This encourages more focus on product durability, reliability, reusability, upgradability, repairability and ease of maintenance. The role of reliability in product development and equipment operation can only increase. This highlights the close relationship between Reliability and Sustainability prevalent in today’s society.

Let’s consider our two examples – the coffee maker and the forest machine. Two different products with very different markets – one high volume and one low volume and Reliability Analysis will be used differently for each product. The role of design Failure Mode and Effects Analysis (FMEA) is critical for understanding how to mitigate potential product failure during the product concept and design phases, and process FMEAs for limiting the introduction of product failures during the manufacturing phase. Effective FMEAs will go a long way to a successful product.

With the best will in the world, sometimes unexpected failures happen. Successful companies handle this by demonstrating to their customers that they are in control of the situation and have the tools and processes in place to resolve the problem. Tools such as FRACAS (Failure Reporting and Corrective Action System) can play a significant role in this.

For the coffee maker machine manufacturer, better reliability analysis during the product development phase can provide the manufacturer more confidence to offer better warranty periods – 5 years rather than 1? An interesting feature of Reliability performance often observed is that the more features present in machines (and thereby more components) the poorer the reliability performance can be – so perhaps less is more?

For the forest machine manufacturer effective reliability, availability and maintainability analysis can help define the best strategy for maintenance, spare part and logistics management – particularly for equipment operating in very remote locations – providing the machine operator the confidence that any downtime is minimal.


Support Management using Simulation
Companies add the cost of risk to the equation: fire, injury, death, brand, supplier reputation. CAE and simulation provide methods to look what’s behind what we see. Simulation models along with empirical data from prototype testing and field environment can us help design products that are safer for the customer and better for our business. A simulation model that is adjusted to empirical data is the digital twin of our product. The digital twin will help us understand the root cause of a failure, optimize your product design, and mitigate the risk of an unsafe failure.

Take control of Functional Safety and Reliability
Whatever business and industry you´re in, however you define Functional Safety and Reliability, it is undisputedly important to define the risks, manage the processes and tools, and maintain the safety of your products.

PDSVISION and Renholmen extend their collaboration

Thoroughness drives innovation, and no detail is insignificant. Work hard to make the customers satisfied and strive for perfection. That is what drives Renholmen and is their core philosophy.

For 50 years, Renholmen has designed and delivered high-tech machines with automated solutions for lumber handling in projects. Providing service, upgrades, and spare parts and continuously pushing innovation forward.

It is an honor that they have chosen to continue their journey with PDSVISION and expand their collaboration around the usage of CAD and PLM.

Their further investment has secured them a continuous solid platform for product lifecycle management and CAD, ensuring they can keep contributing more innovative solutions, build a better community, and provide a different kind of future for their customers.

We look forward to our continued collaboration and are happy to be able to support Renholmen in their mission to keep delivering state-of-the-art lumber handling equipment for sawmills.

For more information on Renholmen – click here

To see how we at PDSVISION can help you with your solutions – contact us here


Getting Started with IoT for Manufacturing

“In the beginning … the earth was formless and empty.”  – Genesis
Where to begin? That is a good question. One of my colleagues suggested I just start writing and not stop until I’ve finished a couple of paragraphs. Admittedly, that was advice about writing a blog, not for starting an Industrial Internet of Things (IIoT) project.

Most likely, you are beginning at the point where someone in your company has undoubtedly heard about the Fourth Industrial Revolution (4IR), Factory 4.0, or one of these IIoT terms.  I am willing to wager that there is at least some interest, if not urgent pressure, from leadership to take advantage of these new technologies to drive profitability and become more competitive. So now what?

Research into the success of such projects indicates that only 26% of all surveyed companies believe that they are successful with their IoT initiatives. Interestingly 35% of IT executives considered their initiatives successful vs 15% of business executives.  So, your initial research has led you to conclude that there is disconnect with the definition of success among the ranks.

I have been implementing IIoT technology for over a decade, so I have started from the beginning many times. The IIoT parallel of “where to begin” should read: Start by setting reasonable expectations and connecting just a few machines.  Or, as I often say, “switch on the lights”. A sensible approach would be to identify some of your most ubiquitous and meaningful machines, investigate their data interfaces or control systems, connect to these, and collect the data.  Once you have an understanding of the data that is available, it’s time to consider the vision for the next round.

The first round of IIoT, and possibly the first few, will be a learning experience.  Structuring your goals around learning and celebrating failures (and learning from said failures) will serve you well.  For example, when I first looked at connecting to a brewery system, it took a whole day to connect into the Beckhoff TwinCAT PLC (not always easy with people looking over your shoulder). It took so long, not because of anything complex, but just because and we had to guess config settings. As one might expect in a brewery, there was some beer drinking after connection to the PLC was established and we could see some real live data.  But, we also learned that there was much less useful data presented than hoped for.

In brainstorming workshops at the beginning of projects, the ideas are very practical and focused on business value. The first project looks great. Lofty expectations are set across the board. Unfortunately, this is where things often go wrong.  And that’s why it’s best to start with simply turning on the lights. Sometimes, for various reasons, you just can’t get your hands on the data from equipment in the plant or out there in the real world.  If you are lucky, you have one of the Kepware products and with reasonable ease, you can connect PLCs and other industrial equipment (such as in the aforementioned brewery example). But if you can’t, you’ll need to start there.

Now here comes the data.  What do you do with the data? Let’s pause. What does “the data” mean?

“Reducing downtime” through preventative maintenance is one of the most common use cases for IIoT project workshops. Here is the challenge: If the control system in your equipment does not record downtime, your initiative to reduce downtime becomes impossible. (And hence, it becomes clear why only 15% of Business Executives consider their IIoT initiatives successful). You need a strong team with experience in IIoT technologies, knowledge of the equipment, and expertise with the control systems to move things forward. But, if the data is not collected by the machine, it is not available to collect.

By first focusing on connecting to equipment and acquiring real-world data, you can determine what data points are available and what use cases these data points enable. Armed with this insight, you are in a great position to set realistic expectations and guide your organization to fund initiatives that will have early returns.

Some initiatives companies may choose to prioritize might be:

New or improved sensors to capture additional data to empower teams to make better decisions
Redesigned or optimized control systems to report more information
New or redesigned products or subsystems to enable entirely new business models such as pay-per-use.

The ROI in the initial cycles of your IoT projects lies in time-to-insight. By focusing on immediate goals of connectivity, and using scalable technology architected for industrial equipment and complex use cases you can “switch the lights on” quicker and your acquired skills and experience scales to the next leg of your journey.