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Where ‘smart traffic’ has already 417 billion hits on google, I only found ‘smart air’ for a kind of door lock and ‘smart drone’ for an advanced toy drone. But definitely, drones are so hot that they will become part of something called ‘smart air’. The SESAR project predicts that drones will make 250 billion hours of flight in the European Union alone. For comparison: this is far more than the air traffic of ‘normal’ airplanes today.

Because drones are using many sensors, we did some research how the use of drones can grow to maturity and fuel ‘smart air’. Today we talk about challenges for delivery drones.

Delivery drones

No wonder, drones have proven to be very convenient already and have even more promises in store. Soon, it will be commonplace that drones are delivering packages, from hot pizzas to even more urgent medicines. And even humans: the first drone taxis are already being tested. At this moment, drones are already used for drag-and-drop deliveries in some rural and faraway areas. Most articles on the internet talk about the use in drones in big city areas. And there they have the big advantage of an -still- almost empty sky instead of congested roads and overfull parking places. For that, delivery by drones will be faster and more predictable.

But until use of drones are entirely tried and tested, most drone developments will take place on rural environments. Because here the risk of large damage is a lot smaller when something will go wrong. In time, delivery drones will still be used in rural places. Maybe as a standalone, maybe in combination with self-driving trucks. Reach will not be a big problem, since the whole word is getting connected fast. So, reach will almost only depend on battery endurance. And for now, these batteries have only a limited capacity for distance and cargo.

Challenges while travelling

Like all delivery services, drone delivery has to a pick-up a package, travel to the destination and drop-of the package.  While travelling, drones have to know how to reach their destination. Meanwhile, there are some challenges:

  • Risk of colliding, with other drones, birds and other air users. Just like other traffic
  • And at point in time, some traffic rules have to be set in place. Sensors can help to let the drone follow these rules
  • How drones can stay on course, even with wind
  • Preventing drones to cross over forbidden (known) areas and unexpected ‘wrong’ areas (e.g. a building or a wood on fire)
  • How to prevent a package from falling? How to alert that a package will probably fall? Or maybe the drone itself? If so, measurement can be taken. Already, there are experiments with self-destruction. But maybe more practical solutions can be found to let the drone aim for a ‘safe area’, such as a park, river, etc. for an ‘emergency landing’
  • Acceptance of drones beside safety: how to guarentee privacy when drones are flying over peopled areas? Then there is the issue of noise: research shows that people find the noise of drones one of the most annoying forms of noise

Challenges with dropping the cargo

For now, the drop-of is literally done by dropping-of the cargo. Maybe with the aid of a cord which places the package as soft as possible to the ground. But anyhow: the drone stays in the air. So, technology has to get safe: for the package to be delivered undamaged. How does the drone know that the right person gets the package? And we have to prevent dogs from biting the package. And of course, to prevent that the dropped cargo will harm humans, animals or buildings or even worse.

The use of sensors

The application possibilities of drones are very promising for delivery uses. It is still in its experimental phase. But with developments going fast, soon it will reach the maturity phase. For this, there are two-fold kind of challenges.

Some are challenges on privacy, safety and security. These challenges have to be solved before the use of drones will get widespread trust and acceptance. The other are technical and communication issues: where multiple drones are being used – especially in cities- challenges how drones can and have to behave in traffic has to be solved.

In both challenges, sensors play a pivotal role in solving the technical questions. In all cases, ASN Filter Designer can help with sensor measurement with real-time feedback and the powerful signal analyzer. How? Look at ASN Filter Designer or mail ASN consultancy: designs@advsolned.com

Do you agree with this list? Do you have other suggestions? Please let us know!

Typical challenges faced by assets managers include:

  • How to measure mechanical component fatigue?
  • How to assess electrical wiring health?
  • How to reduce overall operating costs, but not comprise on public safety?
  • Risks posed by hackers & terrorists
  • Asset damage due to vandalism

Preventative Maintenance aims to solve the aforementioned problems by acting pre-emptively. This is achieved by constantly monitoring the performance of critical components (usually with sensors) and then alerting the maintenance team that a component is about to fail. The asset management team can then schedule maintenance in order to replace the failing component(s) with minimum disruption to the public, and overall lower operational costs.

  • Plan maintenance
  • Machine health care
  • Motor health care
  • Secure firmware updates and anti-tampering

Plan maintenance

Monitoring the health of critical component, such as a lamp, motor or machine component and input power supply. Our algorithms and analytics help asset management departments provide planned maintenance.

A better maintenance program is achieved by constantly monitoring the performance of critical components (usually with sensors or other devices) and then alerting the maintenance team that a component is about to fail.

Machine health care

The health of a machine can be determined by ‘listening’ to the sound it makes via microphones. Algorithms filter and compare recorded audio to fingerprint templates of known failures.

Motor health care

The health of an industrial motor be determined by analysis the phase currents. Algorithms filter and compare captured data to fingerprints templates of known failures. The phase current data can also be used to check for wire breaks or phase failure.

Secure firmware updates & Anti-tampering

ASN’s security module provides asset protection up to military grade, and while at the same time allowing for secure (encrypted) firmware updates.

ASN contactless measurement, smart algorithms and alerting offers the ideal condition for this programme. The asset management team can then schedule maintenance in order to replace the failing component(s) with minimum disruption to the public, and overall lower operational costs.

Let’s make an appointment to see how can help you create an effective maintenance programme and reduce your Total Cost of Ownership.

Industrial induction motors are found everywhere: Lifts, escalators, cable cars, water sluices, cranes, and even washing machines etc. Motors form the backbone of these devices. Since they are mission critical, a failure of a motor may disrupt the whole production line, crippling your precious infrastructure as a whole. As an example: if the motor fails on a water sluice, the disruption means that ships can’t deliver their cargo on time. Our experience has shown that with preventative motor maintenance, you can save up to 51% of your maintenance budget!

Common sources of industrial motor failure

Of course, each industrial motor has its own characteristics. However, common sources of failure in an industrial induction motor are:

  • Ball bearing and rotor crack/break
  • Stator winding faults
  • Rotor winding faults (rotor bars, end-rings etc.)

Save up to 51% with preventative maintenance

For public infrastructure, industrial motors are mission critical. They need to be regularly be checked under expensive maintenance programmes. With ASN’s IoT solutions, you can predict and prevent equipment failure by monitoring product wear and replacement rates.  And if you recognize a slight disturbance, you can solve them easily. Before little faults have become big and expensive problems. When little faults are recognized, they can be repaired without any signifcant downtime. At a time it suits your client best. As such, you can improve the reliability of your assets and reduce downtime.

Effective and efficient use of an engineer’s precious time

Motor health care starts with sensors. With these sensors, you can monitor the running of your monitors automatically by placing sensors in the vicinity of your motors. When a signal pops up that there might be a problem, an engineer can repair this motor. Previously, engineers did their inspection rounds, giving every motor the same attention. Now, engineers can focus on motors that really need attention.

With preventative maintenance, your customers  can save a fortune and minimise any disruption to service. You can save up to 51% on your maintenance costs with our Preventative Maintenance solutions. They are based on safe contactless sensor measurement, and optimize the life expectancy of your industrial motor. Learn more at: https://www.advsolned.com/motor-health-care/ or drop us a line at: info@advsolned.com

Until now, the professional use of drones is mostly still in an experimenting stage. However, drones are one of the golden nuggets in IoT because they can play a pivotal role, for instance in congested cities and faraway areas for delivery. Further, they can be a great help to give an overview of a large area or for places which are difficult or dangerous to reach.

In one of our previous blogs, we concluded that sensor measurement has mostly been a case of trial and error. In this blog, we list some of the challenges we see for sensor measurement which has to be solved to bring the professional use of drones to full maturity.

Practical challenges which can and must be solved with sensors

Here are some of the challenges we have found:

  • Risk of colliding, with other drones, birds and other air users. Just like other traffic
  • And at point in time, some traffic rules have to be set in place. Sensors can help to let the drone follow these rules
  • How drones can stay on course, even with wind
  • Preventing drones to cross over forbidden (known) areas and unexpected ‘wrong’ areas (e.g. a building or a wood on fire)
  • Challenges with unloading the package:
    • Without damage
    • Without harming people, animals, buildings
    • How the drone will know that the right person gets the package? Can we prevent dogs from biting the package?
  • How to prevent a package from falling? How to alert that a package will probably fall? Or maybe the drone itself? If so, measurement can be taken. Already, there are experiments with self-destruction. But maybe more practical solutions can be found to let the drone aim for a ‘safe area’, such as a park, river, etc. for an ‘emergency landing’.

In all cases, ASN Filter Designer can help with sensor measurement with real-time feedback and the powerful signal analyser? How? Look at ASN Filter Designer or mail us: info@advsolned.com

Do you agree with this list? Do you have other suggestions? Please let us know!

Motor producers are beginning to see that they can add value through preventative maintenance. However, when we speak to motor producers, sometimes companies begin to laugh when we ask them if they deliver health care monitoring through sensors to their customers already. They think that preventative maintenance is an enemy of their motor production:

“if motors can be made to run longer, we have less to sell”.

And sometimes companies just look glassy-eyed:

“We’re an old-fashioned company”.

Customers want you to deliver solutions, not motors

This is old fashioned thinking indeed. And like every other lagged thinking, these companies will get obsolete.  In old days, you could sell a ‘product’ with features such and such. Nowadays, customers are solely interested in the solution a company delivers. Customers want their business to run smoothly and without downtime. In this way of thinking, a motor is not a thing with a rotor, bearings and such, but it is a means which guarantees that a whole production line runs smoothly and without interruption.

Safe and sound running motors makes a customer satisfied

So, customers are more satisfied when their motor is running properly. And when it begins not to run properly, they want to know beforehand before a slight disturbance has become a real problem. When they know beforehand, they can take proper action on time, which means lesser costs and in most cases without downtime or at least as short as possible. Because downtime affects the production line in the whole. When the motor has really problems, your customer is forced to get their production on hold for a long time. Then customers not only have to face bigger repair costs. But mostly, costs are higher because now the whole production line has fallen out.

Motor health care starts with sensors

By placing sensors in the vicinity of your motors or even building them in, you can monitor the running of your motors automatically. When a signal pops up that there might be a problem, an engineer can repair this motor. This is also the modern way: previously, engineers did their rounds of motor inspections, giving every motor attention. Now, engineers can focus on motors that need attention.

At Advanced Solutions Nederland, we can help you to deliver real solutions to your customers once again. Visit: https://www.advsolned.com/motor-health-care/ or drop us a line at: info@advsolned.com

How to choose between analog signal processing (ASP) and digital signal processing (DSP). How too chose for ASP or DSP; analog filters or digital filters?

 

Drones and DC motor control – How the ASN Filter Designer can save you a lot of time and effort

Drones are one of the golden nuggets in IoT. No wonder, they can play a pivotal role in congested cities and far away areas for delivery. Further, they can be a great help to give an overview of a large area or places which are difficult or dangerous to reach. However, most of the technology is still in its experimental stage.

Because drones have a lot of sensors, Advanced Solutions Nederland did some research on how drone producing companies have solved questions regarding their sensor technology, especially regarding DC motor control.

Until now: solutions developed with great difficulty

We found out that most producers spend weeks or even months on finding solutions for their sensor technology challenges. With the ASN Filter Designer, he/she could have come to a solution within days or maybe even hours. Besides, we expect that the measurement would be better too.

The biggest time coster is that until now algorithms were developed by handwork, i.e. they were developed in a lab environment and then tested in real-life. With the result of the test, the algorithm would be tweaked again until the desired results were reached. However, yet another challenge stems from the fact that a lab environment is where testing conditions are stable, so it’s very hard to make models work in real life. These steps result in rounds and rounds of ‘lab development’ and ‘real life testing’ in order to make any progress -which isn’t ideal!

How the ASN Filter Designer can help save a lot of time and effort

The ASN Filter Designer can help a lot of time in the design and testing of algorithms in the following ways:

  • Design, analyse and implement filters for drone sensor applications with real-time feedback and our powerful signal analyser.
  • Design filters for speed and positioning control for sensorless BLDC (brushless DC) motor applications.
  • Speed up deployment to Arm Cortex-M embedded processors.

 

Real-time feedback and powerful signal analyser

One of the key benefits of the ASN Filter Designer and signal analyser is that it gives real-time feedback. Once an algorithm is developed, it can easily be tested on real-life data. To analyse the real-life data, the ASN Filter Designer has a powerful signal analyser in place.

Design and analyse filters the easy way

You can easily design, analyse and implement filters for a variety of drone sensor applications, including: loadcells, strain gauges, torque, pressure, temperature, vibration, and ultrasonic sensors and assess their dynamic performance in real-time for a variety of input conditions.  With the ASN Filter Designer, you don’t have do to any coding yourself or break your head with specifications: you just have to draw the filter magnitude specification and the tool will calculate the coefficients itself.

Speed up deployment

Perform detailed time/frequency analysis on captured test datasets and fine-tune your design. Our Arm CMSIS-DSP and C/C++ code generators and software frameworks speed up deployment to a DSP, FPGA or micro-controller.

An example: designing BLDC motor control algorithms

BLDC (brushless DC) BLDC motors have found use in a variety of application areas, including: robotics, drones and cars. They have significant advantages over brushed DC motors and induction motors, such as: better speed-torque characteristics, high reliability, longer operating life, noiseless operation, and reduction of electromagnetic interference (EMI).

One advantage of BLDC motor control compared to standard DC motors is that the motor’s speed can be controlled very accurately using six-step commutation, making it a good choice for precision motion applications, such as robotics and drones.

Sensorless back-EMF and digital filtering

For most applications, monitoring of the back-EMF (back-electromotive force) signal of the unexcited phase winding is easier said than done, since it has significant noise distortion from PWM (pulse width modulation) commutation from the other energised windings. The  coupling  between  the  motor parameters, especially inductances, can induce ripple in the back-EMF signal that is synchronous with the PWM commutation.  As a consequence, this induced ripple on the back EMF signal leads to faulty commutation. Thus, the measurement challenge is how to accurately measure the zero-crossings of the back-EMF signal in the presence of PWM signals?

A standard solution is to use digital filtering, i.e. IIR, FIR or even a median (majority) filter. However, the challenge for most designers is how to find the best filter type and optimal filter specification for the motor under consideration.

The solution

The ASN Filter Designer allows engineers to work on speed and position sensorless BLDC motor control applications based on back-EMF filtering to easily experiment and see the filtering results on captured test datasets in real-time for various IIR, FIR and median (majority filtering) digital filtering schemes. The tool’s signal analyser implements a robust zero-crossings detector, allowing engineers to evaluate and fine-tune a complete sensorless BLDC control algorithm quickly and simply.

So, if you have a measurement problem, ask yourself:

Can I save time and money, and reduce the headache of design and implementation with an investment in new tooling?

Our licensing solutions start from just 125 EUR for a 3-month licence.

Find out what we can do for you, and learn more by visiting the ASN Filter Designer’s product homepage.

Generations have been entertained by the gadgets and future technology portrayed in Sci-fi series, such as Star Trek, but is it all science fiction?


The Tricorder

One device that intrigued me for years was the so called ‘Tricorder’ and the ability for the doctor to read a person’s vital life signs or VLS (e.g. heart beat and respiration) with a handheld device from about a metre away.


Back to the 21st century!

With advances in radar technology over the last few years, a few chip manufacturers are now producing affordable radar devices suitable for biomedical VLS measurement. Radar technology that used to cost thousands of Euros, and was primarily aimed a military technology, is now available for a few hundred Euros, making it viable for home medical products.


Sounds great, but what can UWB (ultrawide band) pulse doppler radar do?

  • millimetre accuracy: allowing for detection of the smallest changes, such as respiration and heart rate from several metres away.
  • Penetrates duvets, blankets and clothes: The virtue of the small wavelength ensures accurate detection of humans in a bed or sitting in chair watching TV or reading a book.
  • Penetrates Walls and doors: Tracking of VLS and movement when radar is mounted within a ceiling or behind a wall – no ugly module on the wall!
  • High sensitivity: able to see the VLS from tiny premature babies.
  • Ultra-safe technology: RF emission is 0.01% the energy typically found in a household WiFi router – meaning that prolonged exposure will have no detrimental effects on human health.

After receiving a request from a client about monitoring the health of an eldery person living alone or in a nursing home, we decided to conduct a few tests of our own to see what was possible with this new technology.


VLS of a subject lying in bed

After building a demonstrator, and placing the radar sensor about 1 metre from a subject sleeping (similar to the doctor in Star Trek), we obtained the following waveform:

Captured VLS data captured from a UWB radar – containing both heart beat and respiration information

Wow! Was our initial reaction to the test data – this is millimetre movement through a duvet! Notice how slow the biomedical signal is, as an average adult’s respiration rate at rest is about 12bpm, requiring relatively long data acquisition times (tens of seconds) for meaningful data analysis.


What’s the respiration and heart beat?

Passing the signal through our algorithm, we could easily estimate the respiration (RR) and heartbeat (HB) from the plot (see the two red squares on the two peaks). However, in order to be objective, we attached a clip-on pulse oximeter to the subject finger, and as seen they matched very well.

What does this all mean for me?
Contactless VLS measurement for home use is closer than you think, and is certainly not science-fiction anymore. This technology opens up many possibilities for monitoring when normal sensors are infeasible, such as premature babies, patients with dementia and even sleep trend analysis. We’ll improve our algorithm in order to make it more robust and faster, but as seen our results are very promising indeed, and open up the possibility of contactless vital life signs (VLS) measurements for many practical applications!

The internet of things (IoT) has gained tremendous popularity over the last few years, as many organisations strive to add IoT smart sensor technologies to their product portfolios. The basic paradigm centres around connecting everything to everything, and exchanging all data. This could be house hold appliances to more blue sky applications, such as smart cities. But what does this particularly mean for you?

Almost all IoT applications involve the use of sensors. But how do SME and even multi-national organisations transform their legacy product offering into a 21st century IoT application? One the first challenges that many organisations face is how to migrate to an IoT application while balancing design time, time to market, budget and risk.

Sounds interesting? Then read further….

We recently completed a project for a client who manufactured their own sensors, but wanted to improve their sensor measurement accuracy from ±10% to better than ±0.5% without going down the road of a massive re-design project.

 

The question that they asked us was simply: “Is it possible to get high measurement accuracy performance from a signal that is corrupted with all kinds of interference components without a hardware re-design?”

Our answer: “Yes, but the winning recipe centres around knowing what architectural building blocks to use”.

Traditionally, many design bureaus will evaluate the sensor performance and try and improve the measurement accuracy performance by designing new hardware and adding a few standard basic filtering algorithms to the software. This sort of intuitive approach can lead to very high development costs for only a modest increase in sensor performance. For many SMEs these costs can’t be justified, but perhaps there’s a better way?

Algorithms: the winning recipe

Algorithms and mathematics are usually regarded by many organisations as ‘academic black magic’ and are generally overlooked as a solution for a robust IoT commercial application. As a consequence very few organisations actually take the time to analytically analyse a sensor measurement problem, and those who do invent something tend to come up with something that’s only useable in the lab. There has been a trend over the years to turn to Universities or research institutes, but once again the results are generally too  academic and are based more on getting journal publications, rather than a robust solution suitable for the market.

Our experience has been that the winning recipe centres around the balance of knowing what architectural blocks to use, and having the experience to assess what components to filter out and what components to enhance.  In some cases, this may even involve some minor modifications to the hardware in order to simplify the algorithmic solution. Unfortunately, due to the lack of investment in commercially experienced, academically strong (Masters, PhD) algorithm developers and the pressure of getting a project to the finish line, many solutions (even from reputable multi-national organisations) that we’ve seen over the years only result in a moderate increase in performance.

Despite the plethora of commercially available data analysis software, many organisations opt to do basic data analysis in Microsoft Excel, and tend to stay away from any detailed data analysis as it’s considered an unnecessary academic step that doesn’t really add any value.   This missed opportunity generally leads to problems in the future, where products need to be recalled for a ‘round of patchwork’ in order solve the so called ‘unforeseen problems’. A second disadvantage is that performance of the sensors may be only satisfactory, whereas a more detailed look may have yielded clues on how make the sensor performance good or in some cases even excellent.

 Algorithms can save the day!

 “Although many organisations regard data analysis as a waste of money, our experience and customers prove otherwise.”

Investing in detailed data analysis at the beginning of a project usually results in some good clues as to what needs to be filtered out and what needs to be enhanced in order to achieve the desired performance.   In many cases,  these valuable clues allow  experienced algorithm developers to concoct a combination of signal processing building blocks without re-designing any hardware – which is very desirable for many organisations! Our experience has shown that this fundamental first step can cut project development costs by as much as 75%, while at the same time achieving the desired smart sensor measurement performance demanded by the market.

So what does this all mean in the real world?

Returning the story of our customer, after undertaking a detailed data analysis of their sensor data, our developers were able design a suitable algorithm achieving a ±0.1% measurement accuracy from the original ±10% with only minor modifications to the hardware. This enabled the customer to present his IoT application at a trade show and go into production on time, and yes, we stayed within budget!

Author

  • Dr. Sanjeev Sarpal

    Sanjeev is an AIoT visionary and expert in signals and systems with a track record of successfully developing over 25 commercial products. He is a Distinguished Arm Ambassador and advises top international blue chip companies on their AIoT solutions and strategies for I4.0, telemedicine, smart healthcare, smart grids and smart buildings.

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The internet of things (IoT) devices have been around for a number of years now, but very few smart sensors have any decent level of data security. For many organisations the issue of data security and secure remote updates to legacy products has become of paramount importance. Unfortunately, many of the engineers who design sensor products have little or no understanding a security algorithms, leading to systems that can be easily hacked – the fiasco of the UK smart meter system is a good example.

 Algorithms to the rescue

Algorithms and mathematics are usually regarded by many organisations as ‘academic black magic’ and are generally overlooked as a solution for a robust IoT commercial application. Nevertheless, some of you may be surprised by how old the concept of algorithms actually are in solving real world problems.

A few weeks ago, I looked through my old PhD thesis and stumbled across a reference to one of world’s first documented algorithms from the 9th century mathematician, Al-Khwarizmi (where, the word ‘algorithm’ is derived from al-Khwarizmi’s name).

Al-Khwarizmi undertook pioneering work in algebra, which was popularized in his book, “al-Mukhtasar fi Hisab al-Jabr wa l-Muqabala” and altered society’s perspective of analyzing problems, be they a simple domestic chore or a complex mathematical concept.

 An excerpt from “Al-Mukhtasar fi Hisab al-Jabr wa l-Muqabala” for the solution to x^2 + 10x = 39.

Translation: For the squares and roots equal to a number, it is as saying: a square and ten of its roots is equal to thirty-nine dirhams. The solution is to halve roots, equal to five in this problem, then, multiplying the root by itself then this will be twenty-five. Then add it to thirty-nine and this will be sixty-four. Then take the square root, which will be eight and subtract from it half the root, which is five. The remainder is three and that is the root you are seeking and the square is nine.

I had forgotten (well, it was 14 years ago!) how elegant Al-Khwarizmi work actually was, and how I’m sure he would probably smile at the challenges that we’re facing today. Nevertheless, without his pioneering work, we wouldn’t have any of the IoT and security algorithms that we take for granted today.

Solutions in the 21st century

We’ve been pleasantly surprised by the rich offering from commercial IC vendors, such as: Atmel, NXP and Analog Devices in producing secure micro-controllers for the IoT market. Many of these micro-controllers include all of the necessary hardware encryption building blocks needed for building a secure IoT sensor, and some even offer a decent amount of processor power for data analytics algorithms.

Sounds ideal, right?

The Achilles heel of all of these solutions is how engineers implement them in a system. The micro-controller itself may be ‘secure’, but what about the system architecture (i.e. the algorithmic building blocks and and how they interact with each other). And what about encryption keys? How are they stored and updated? For the UK smart meter system mentioned above, the system just used one key for the whole system – not very secure ! It is this fact that is painfully overlooked by many, and as such, which eventually leads to the system being hacked and rendered useless.

In short, hardware based encryption technology is a great step in right direction for IoT device security, but without good understanding of encryption technology as part of the system architecture the solution is doomed to failure.

Author

  • Dr. Sanjeev Sarpal

    Sanjeev is an AIoT visionary and expert in signals and systems with a track record of successfully developing over 25 commercial products. He is a Distinguished Arm Ambassador and advises top international blue chip companies on their AIoT solutions and strategies for I4.0, telemedicine, smart healthcare, smart grids and smart buildings.

    View all posts