Posts

There is an increasing use of the water infrastructure, while the current demand is already adjacent to the existing capacity. However, space for physical expansion is limited. On the other hand, there is a tightening of budgets, while maintenance of water infrastructure comes with high costs.

Huge cost savings as well as reducing public inconvenience can be achieved with a preventative maintenance program. Benefits of a preventive maintenance program are:

  • A longer lifetime for your equipment with preventive maintenance
  • Be in control and optimize your processes
  • Optimize your just-in-time management and get more value by delivering guarantees
  • Increase security for your cargo and your equipment

Struggle with the elements

Working at water is a struggle with the elements: water, wind, dust, heat, pressure. So, you want to know if pipelines are going to leak before they are actually leaking. When cables are beginning to wear out. If the oil is still on the right level. That you can act when dust or smear are blocking lenses. With IoT, you can predict and prevent equipment failure by monitoring product wear and replacement rates.  As such, you improve the reliability of your assets and reduce downtime. And if you recognize little faults, you can solve them easily before they have become big and expensive problems.

Rust

Another time- and money saver is the maintenance in the port: one of the worst enemies is rust. No wonder, that the in- and outside of the ship is painted very often. Even when there is no rust, ‘just in case’. It is better to place a rust sensor: it warns when there is rust and those places can be painted or otherwise maintained. And it makes sure spots are not forgotten. Even more: a rust sensor can track rust at places which are hardly reachable. An employee only has to go to this hard-to-reach part when it is really needed.

How preventive maintenance works

In essence, algorithms and analytics monitor sensor data. They look for deviations in a physical process’s normal operation. Examples are the wear and tear in a water sluice’s mechanical components, or even damaged wiring for the pump.

A sensor fusion algorithm merges data from different sensors. Associated analytics determine whether a component’s characteristic is normal for its age. Any deviations outside ‘normal operation’ are fed back to the master system as potential sources of failure.

Energy companies have struggled for years with meeting demand with supply with society’s increasing demand for energy. This been made even more challenging with more people using electric vehicles and smart cities demanding more lighting.

Modern IoT sensors and smart grid solutions help energy companies and consumers improve and optimize the modern grid for the 21st century. But what does all the jargon really mean?

Blackout

The UK National Grid recently experienced a major outage that left almost a million homes in the dark and forced trains to a standstill. The source of the blackout was traced back to two generators that failed, resulting in grid’s frequency falling below the critical 49.5Hz set by the regulator.

According to the media the UK blackout was triggered when the frequency slumped to 48.88Hz, which is well below the legal limits set by the regulatory agencies.

But what do these limits really mean?

Some background information

The energy grid frequency is 50Hz in Europe, 60Hz in the US. Japan has an unusual historical situation in that the East of the country runs on a European 50Hz system and the West of country runs on an American 60Hz system.

In all cases, in order to meet the energy requirements, several generators are needed to work in parallel and must be synchronised. Accurate frequency control is required to control the amount of power delivered by multiple generators in order to provide a stable power supply to consumers. The challenge for the energy companies is meeting the changes in supply and demand, since higher demand than supply will result in fall of frequency and vice versa.

Thus, the challenge for IoT sensors and algorithms is measuring the operating frequency and phase to a sufficient accuracy and adjusting the generators to meet the energy demand requirement at that particular time. But how?

A PMU (phase measurement unit) is typically used the measure and report back (typically 30-60 measurements per second) to the network operator what the actual frequency and phase of various points on the grid are. In order to synchronise the measurements, the PMU internal clocks are time synchronised via a GPS (global positioning system) unit, such that all reported frequency and phase measured across the grid are time aligned.

The frequency limits are shown below:

The challenge for energy managers

As seen above, the normal region in Europe is between 49.85 – 50.15Hz. If the generators exceed 50.15Hz (entering the orange region), there is too much energy and the generators need to be rolled back a little. If the frequency falls below 49.85Hz (also in the orange region), there is not enough energy to meet demand, and more energy is needed. In all cases, the frequency must never enter the red region, otherwise Blackouts will occur.

The energy company is legally obliged to keep the powerline frequency between 49.5 – 50.5Hz (± 1%). This is typically tracked to an accuracy of ± 1mHz resolution.

Blackouts

The UK blackout was triggered when the frequency slumped to 48.88Hz, which is well below the legal limits and in the blackout region. The damage to the UK economy has still yet to be determined, but National Grid UK should be considering adding extra redundancy safe guards in order restore public confidence.

Dips and swells tracking

Another common problem that occurs is that of energy dips, i.e. the voltage momentarily drops for a few cycles. Think about lights temporarily flickering in your house.

In factories running machinery, this usually occurs when a machine is started up, indicating imminent component failure. Swells are the opposite of dips, but are much less common.

ASN’s IoT sensor and algorithms play an essential role in keeping the grid healthy, as demonstrated in the video below.

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: http://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!

 

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.

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!

It’s estimated that the global smart sensor market will have over 50 billion smart devices in 2020. At least 80% of these IoT/IIoT smart sensors (temperature, pressure, gas, image, motion, loadcells) will use Arm’s Cortex-M technology – where the largest growth is in smart Image sensors (ADAS) & smart Temperature sensors (HVAC).

IoT sensor measurement challenge

The challenge for most, is that many sensors used in these applications require a little bit of filtering in order to clean the measurement data in order to make it useful for analysis.

Let’s have a look at what sensor data really is…. All sensors produce measurement data. These measurement data contain two types of components:

  • Wanted components, i.e. information what we want to know
  • Unwanted components, measurement noise, 50/60Hz powerline interference, glitches etc – what we don’t want to know

Unwanted components degrade system performance and need to be removed.

So, how do we do it?

DSP means Digital Signal Processing and is a mathematical recipe (algorithm) that can be applied to IoT sensor measurement data in order to clean it and make it useful for analysis.

But that’s not all! DSP algorithms can also help in analysing data, producing more accurate results for decision making with ML (machine learning). They can also improve overall system performance with existing hardware (no need to redesign your hardware – a massive cost saving!), and can reduce the data sent off to the cloud by pre-analysing data and only sending what is necessary.

Nevertheless, DSP has been considered by most to be a black art, limited only to those with a strong academic mathematical background. However, for many IoT/IIoT applications, DSP has been become a must in order to remain competitive and obtain high performance with relatively low cost hardware.

Do you have an example?

Consider the following application for gas sensor measurement (see the figure below). The requirement is to determine the amplitude of the sinusoid in order to get an estimate of gas concentration (bigger amplitude, more gas concentration etc). Analysing the figure, it is seen that the sinusoid is corrupted with measurement noise (shown in blue), and any estimate based on the blue signal will have a high degree of uncertainty about it – which is not very useful if getting an accurate reading of gas concentration!

Algorithms clean the sensor data

After ‘cleaning’ the sinusoid (red line) with a DSP filtering algorithm, we obtain a much more accurate and usable signal which helps us in estimating the amplitude/gas concentration. Notice how easy it is to determine the amplitude of red line.

This is only a snippet of what is possible with DSP algorithms for IoT/IIoT applications, but it should give you a good idea as to the possibilities of DSP.

How do I use this in my IoT application?

As mentioned at the beginning of this article, 80% of IoT smart sensor devices are deployed on Arm’s Cortex-M technology. The Arm Cortex-M4 is a very popular choice with hundreds of silicon vendors, as it offers DSP functionality traditionally found in more expensive DSPs. Arm and its partners provide developers with easy to use tooling and a free software framework (CMSIS-DSP) in order to get you up and running within minutes.

With the advent of smart cities, and society’s obsession of ‘being connected’, data networks have been overloaded with thousands of IoT sensors sending their data to the cloud, needing massive and very expensive computing resources to crunch the data.

Is it really a problem?

The collection of all these smaller IoT data streams (from smart sensors), has ironically resulted in a big data challenge for IT infrastructures in the cloud which need to process

massive datasets – as such there is no more room for scalability. The situation is further complicated with the fact, that a majority of sensor data is coming from remote locations, which also presents a massive security risk.

It’s estimated that the global smart sensor market will have over 50 billion smart devices in 2020. At least 80% of these IoT/IIoT smart sensors (temperature, pressure, gas, image, motion, loadcells) will use Arm’s Cortex-M technology, but have little or no smart data reduction or security implemented.

The current state of play

The modern IoT eco system problem is three-fold:

  • Endpoint security
  • Data reduction
  • Data quality

Namely, how do we reduce our data that we send to the cloud, ensure that the data is genuine and how do ensure that our Endpoint (i.e. the IoT sensor) hasn’t been hacked?

The cloud is not infallible!

Traditionally, many system designers have thrown the problem over to the cloud. Data is sent from IoT sensors via a data network (Wifi, Bluetooth, LoRa etc) and is then encrypted in the cloud. Extra services in the cloud then perform data analysis in order to extract useful data.

So, what’s the problem then?

This model doesn’t take into account invalid sensor data. A simple example of this, could be glue failing on a temperature sensor, such that it’s not bonded to the motor or casing that it’s monitoring. The sensor will still give out temperature data, but it’s not valid for the application.

As for data reduction – the current model is ok for a few sensors, but when the network grows (as is the case with smart cities), the solution becomes untenable, as the cloud is overloaded with data that it needs to process.

No endpoint security, i.e. the sensor could be hacked, and the hacker could send fake data to the cloud, which will then be encrypted and passed onto the ML (machine learning) algorithm as genuine data.

What’s the solution?

Algorithms, algorithms….. and in built security blocks.

Over the last few years, hundreds of silicon vendors have been placing security IP blocks into their silicon together with a high performance Arm Cortex-M4 core. These so called enhanced micro-controllers offer designers a low cost and efficient solution for IoT systems for the foreseeable future.

A lot can be achieved by pre-filtering sensor data, checking it and only sending what is neccessary to the cloud. However, as with so many things, knowledge of security and algorithms are paramount for success.

I recently attended a seminar on advanced instrumentation, where algorithms were heavily featured. The project pitches heavily emphasised implementation rather than analysis and design, which started an interesting discussion, and led me to think about providing some hints that we’ve successfully used over the years:

1. What do we want to achieve? This is perhaps obvious, but I’ve seen that many people do over look this step and jump into Matlab or C in order to try something out. I would urge some caution here, and suggest that you think very carefully about what you’re about to undertake before writing a single line of code. Don’t be afraid to ask your colleagues/network for advice, as their suggestions may save you months of development time. Also consider using established techniques such as, MoSCoW.

2. The specifications: After establishing the ‘big picture’, split up the specifications into ‘must haves’ and ‘nice to haves’. This may take some time to work out, but undertaking this step saves a considerable amount of time in the development process, and keeps the client in the loop. The specifications don’t need to be 100% complete at this stage (they’re always minor details to be worked out), but make sure that you’re clear about what you’re about undertake, and don’t be afraid to do some analysis or short experiments if required.

3. Algorithm design: Sketch out the algorithm’s building blocks (Visio is a good tool), and for each idea produce a short list of bullets (pros and cons) and computational complexity. This will allow you easily review each concept with your peers.

4. Test data: arrange for some test vectors data (from clients or design some of your own synthetic signals), and sketch out a simple test plan of test vectors that you aim to use in order to validate your concept.

5. Development: Depending on your programming ability, you may decide to implement in C/C++, but Matlab/Octave are very good starting points, as the dynamic data types, vector math and toolboxes give you maximum flexibility. Use the testplan and vectors that you’ve designed in step 4. However, in the case of how to best design your algorithm for streaming applications, I would say that many aspects of the algorithm can be tested with an offline (data file) approach. For a majority of our radar and audio work, we always begin with data file comprised of 10-30seconds worth of data in order to prove that the algorithm functions as expected. Subsequent implementation steps can be used to make the algorithm streaming, but bear in mind that this may take a considerable amount of time!

6. Avoid a quick fix! Depending on the complexity of your algorithm, there will be certain testvectors that degrade the performance of your algorithm or even cause it to completely fail. Allocate sometime to investigate this behaviour, but remember to prioritise the importance, and don’t spend months looking for a minor bug. Try and avoid looking for a quick fix or a patch, as they generally re-appear in the future and kick you up the backside.

7. Implementation: after verifying that your concept is correct, you can finally consider target implementation. This step couples back to the previous steps, as the algorithm complexity will have direct influence on the implementation platform and development time. Some good questions to ask yourself: Is the target platform embedded? In which case, do I need an FPGA, DSP or microcontroller? Will it be fixed point or floating point? Perhaps it will be PC based, in which case is it for Windows, Linux or Mac or for a tablet? What tools do you need in order to develop and test the algorithm?

8. Validation: Verify that your implemented algorithm works with your test vectors and that look for any difficult cases that you can find – remembering point 6.

9. Documentation: In all of the aforementioned steps, documentation is essential. Make sure that you document your results, and provide a paper trail such that a colleague can continue with your work if you get hit by a bus.

About the author: Sanjeev Sarpal is director of algorithms and analytics at Advanced Solutions Nederland BV. He holds a PhD in signal processing and has over 20 years commercial experience with the design and deployment of algorithms for smart sensor applications.