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Preventive Maintenance is one of the golden nuggets of IOT. How does this focus affect the deployment of personnel?

  • Efficiencty of personnel: more and better results
  • Challenge of scarcity of personnel
  • The challenges of the aging engineer

Efficiency of personnel: more and better results

There was and is a lot of attention what sensors can do for preventive maintenance: with preventive maintenance, huge costs of big repair costs are avoided by acting on time. One aspect in this way of thinking, was that existing personnel could work more efficiently. In old days, mechanics and engineers did their regular scheduled rounds of maintenance, where every device got similar time of attention, whether the device was in a bad state or not. Sensors measure the state of maintenance of devices real-time. As such, personnel can give attention to devices which really needs it. By using your existing personnel in this more efficient way, high personnel costs are saved because no other personnel would have been hired.

Challenge of Scarcity of personnel

When Preventive Maintenance became popular some years ago as one of the fields of Internet of Things, the world was still in the last phase of the economic crisis. Industry has in some ways still crisis thought: yes, personnel is hard to find. But they don’t make the connection that efficiency has changed in the guise of ‘cost saver’ to ‘benefit most from opportunities’. Because personnel is so hard to find, industry has to use the available personnel as efficient and effectively as possible. Besides, engineering for infrastructure isn’t a popular study any longer. So, engineers are even harder to find.

With preventive maintenance with the aid of sensors, personnel can give attention to the devices which really needs them.

The challenges of the aging engineer

There is more: most infrastructure has been built 20 years ago. Already, there’s the challenge that those engineers have moved on to other jobs. So, it’s very possible indeed that in a company, nobody knows how this infrastructure works exactly any longer. Last years, a new challenge has come up: those engineers are beginning to retire. That means that a pool of this specific knowledge is already decreasing and will even lessen more in the years to come. Therefore, it is very important to have measures for maintenance in place, before this knowledge has disappeared completely.

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Benefits:

  • A longer lifetime for your equipment with Preventive maintenance
  • Create the future. Better serve your client, with solutions which weren’t possible until now!
  • More satisfied customers
  • More control on your processes
  • Better Security

One of the most important areas for IoT is Preventive Maintenance. With the modern solutions, you can measure if assets are working properly. And if not, you can repair or replace them, even before those assets have created damage. Examples are:

  • Are the industrial motors running properly?
  • Is the oil pressure and quality still ok?
  • Are there any glitches in the electrical wiring?
  • How can I save on energy?

With IoT, you can give your equipment a longer lifetime and thus save on repair and replacement costs. Besides, you can spare on costs because you have grip on your processes. For instance: more efficiency on energy costs, better results through optimal deployment of employees

Your customers will become more satisfied with your services. With solutions which weren’t possible until now, products can ‘think’ for their users. In IOT, users raise the expectations and will be dissatisfied with devices which do not help them.

A dashboard helps you to view in one glance which assets are working properly and which are probably in need of repair or replacement. Further, you learn when, where and how intensely your assets are being used, so you use your assets more efficiently.

In a world of connected devices, security is very important. Hackers will try to break in: to steal, to cause harm or to shut down your devices. Without security, hackers can make their entry from anywhere: from one of your devices, but also an unsecured device from one of your employee’s at home. So, in the world of IOT, security of these devices is key.

IoT solutions

IoT solutions prevent accidents from happening and reduce the response time for maintenance. As results, your costs of maintenance will be lower and equipment will have a longer lifetime through Preventive Maintenance.

Sensor measurement solutions look for deviations in normal use. So, you can act upon the first deviations and before the device isn’t working at all. Examples are:

  • Monitoring the health of an industrial motor
  • Monitoring oil quality in chain mechanisms
  • Smart metering for saving energy

Clean sensor data required for sensor fusion and accurate decision making

Sensor data (audio, pressure, temperature, weight, etc.) have to be measured. However, most sensor signals are disturbed by:

  • Powerline interference and glitches.
  • Environmental factors (including: dust and other contaminants).

ASN Consultancy is the modern way of working of algorithm design to separate the wanted sensor signals from the undesirable unwanted signals. So, you can analyze and take action on clean and accurate sensor data.

Dashboard

Our tailormade dashboard solutions provide you all the information you need at one glance. So, you act on devices which are not working properly anymore. You can see the use of each device and can even predict the use in time, based on your history data. With this information, you can gain more efficiency or you can improve the satisfaction of your customers.

Security

With a world where everything is connected, security is very important. Because of its importance, its size and the results of an eventual disruption, infrastructure is an important target for terrorist and (future) enemy governments.

Do you want to learn more: https://www.advsolned.com/asn-condition-monitoring/

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Container thefts are increasingly common. “What should you do with such a thing?” headlines the newspaper article. Recently, the police found a number of containers that were once stolen. Tracy, the IOT track and trace device, can help you.

Why should someone want to steal a container?

So, why should someone want to steal a container? For an outsider, it might sound a bit strange. Customers see the container mostly as a kind of large, metal ‘box’ to dispose waste. For a container company, the hiring of the container means trust in your logistic solutions. But for a thief, a container means an easy to steal loot: it’s already packed and stands ready to pick-up!

Stealing is that simple: the scrap metal booty is already packed!

Stealing containers with scrap metal is especially popular. That does not have to mean that a container actually contains scrap metal or is completely full: the thief’s hope for loot is enough. Stealing a container is pretty simple: all the thief needs is a truck. He can put the container on the back with a cable or grab arm in no time. This theft means a major loss for companies: a container can easily cost 5 to 10 thousand euros, beside the eventual value of the cargo. And possibly the trust the customer has in you.

All that most companies do untill now is to share on social media camera images of their container or the truck that was stolen. Hoping to find the thief. Or at least to prevent a recurrence.

Tracy IoT helps: track and trace

• Perimeter detection

• Track and trace on container: Immediate theft signal

Perimeter detection

Tracy checks whether persons enter the site. When “strange” people enter the site, a signal is immediately triggered. Besides, Tracy monitors the movement of people and assets within the perimeter. Tracy uses Ultra Wide Band (UWB). One of the big advantages of UWB is its accuracy, so you know immediately where to look.

Track and trace on container: Immediate theft signal

When there are movements around a container, a signal goes off. If these are “strange”, for example at late times when nobody should be present, you can take immediate action. If the container is taken along anyway, it can be detected by the UWB signal.

Read more: https://www.advsolned.com/tracker-assets/

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Although the design of FIR filters with linear phase is an easy task. This is certainly not true for IIR filters that usually have a highly non-linear phase response, especially around the filter’s cut-off frequencies. This article discusses the characteristics needed for a digital filter to have linear phase, and how an IIR filter’s passband phase can be modified in order to achieve linear phase using all-pass equalisation filters.

Why do we need linear phase filters?

Digital filters with linear phase have the advantage of delaying all frequency components by the same amount, i.e. they preserve the input signal’s phase relationships. This preservation of phase means that the filtered signal retains the shape of the original input signal. This characteristic is essential for audio applications as the signal shape is paramount for maintaining high fidelity in the filtered audio. Yet another application area that requires this, is ECG biomedical waveform analysis, as any artefacts introduced by the filter may be misinterpreted as heart anomalies.

The following plot shows the filtering performance of a Chebyshev type I lowpass IIR on ECG data – input waveform (shown in blue) shifted by 10 samples (\(\small \Delta=10\)) to approximately compensate for the filter’s group delay. Notice that the filtered signal (shown in red) has attenuated, broadened and added oscillations around the ECG peak, which is undesirable.

Figure 1: IIR lowpass filtering result with phase distortion

In order for a digital filter to have linear phase, its impulse response must have conjugate-even or conjugate-odd symmetry about its midpoint. This is readily seen for an FIR filter,

\(\displaystyle H(z)=\sum\limits_{k=0}^{L-1} b_k z^{-k}\tag{1} \)

With the following constraint on its coefficients,

\(\displaystyle b_k=\pm\, b^{\ast}_{L-1-k}\tag{2} \)

which leads to,

\(\displaystyle z^{L-1}H(z) = \pm\, H^\ast (1/z^\ast)\tag{3} \)

Analysing Eqn. 3, we see that roots (zeros) of \(\small H(z)\) must also be the zeros of  \(\small H^\ast (1/z^\ast)\). This means that the roots of \(\small H(z)\) must occur in conjugate reciprocal pairs, i.e.  if \(\small z_k\) is a zero of \(\small H(z)\), then \(\small H^\ast (1/z^\ast)\) must also be a zero.

Why IIR filters do not have linear phase

A digital filter is said to be bounded input, bounded output stable, or BIBO stable, if every bounded input gives rise to a bounded output. All IIR filters have either poles or both poles and zeros, and must be BIBO stable, i.e.

\(\displaystyle \sum_{k=0}^{\infty}\left|h(k)\right|<\infty \tag{4}\)

Where, \(\small h(k)\) is the filter’s impulse response. Analyzing Eqn. 4, it should be clear that the BIBO stability criterion will only be satisfied if the system’s poles lie inside the unit circle, since the system’s ROC (region of convergence) must include the unit circle. Consequently, it is sufficient to say that a bounded input signal will always produce a bounded output signal if all the poles lie inside the unit circle.

The zeros on the other hand, are not constrained by this requirement, and as a consequence may lie anywhere on z-plane, since they do not directly affect system stability. Therefore, a system stability analysis may be undertaken by firstly calculating the roots of the transfer function (i.e., roots of the numerator and denominator polynomials) and then plotting the corresponding poles and zeros upon the z-plane.

Applying the developed logic to the poles of an IIR filter, we now arrive at a very important conclusion on why IIR filters cannot have linear phase.

A BIBO stable filter must have its poles within the unit circle, and as such in order to get linear phase, an IIR would need conjugate reciprocal poles outside of the unit circle, making it BIBO unstable.

Based upon this statement, it would seem that it’s not possible to design an IIR to have linear phase. However, a discussed below, phase equalisation filters can be used to linearise the passband phase response.

Phase linearisation with all-pass filters

All-pass phase linearisation filters (equalisers) are a well-established method of altering a filter’s phase response while not affecting its magnitude response. A second order (Biquad) all-pass filter is defined as:

\( A(z)=\Large\frac{r^2-2rcos \left( \frac{2\pi f_c}{fs}\right) z^{-1}+z^{-2}}{1-2rcos \left( \frac{2\pi f_c}{fs}\right)z^{-1}+r^2 z^{-2}}\tag{5} \)

Where, \(\small f_c\) is the centre frequency, \(\small r\) is radius of the poles and \(\small f_s\) is the sampling frequency. Notice how the numerator and denominator coefficients are arranged as a mirror image pair of one another.  The mirror image property is what gives the all-pass filter its desirable property, namely allowing the designer to alter the phase response while keeping the magnitude response constant or flat over the complete frequency spectrum.

Cascading an APF (all-pass filter) equalisation cascade (comprised of multiple APFs) with an IIR filter, the basic idea is that we only need to linearise the phase response the passband region. The other regions, such as the transition band and stopband may be ignored, as any non-linearities in these regions are of little interest to the overall filtering result.

The challenge

The APF cascade sounds like an ideal compromise for this challenge, but in truth a significant amount of time and very careful fine-tuning of the APF positions is required in order to achieve an acceptable result. Each APF has two variables: \(\small f_c\) and \(\small r\) that need to be optimised, which complicates the solution. This is further complicated by the fact that the more APF stages that are added to the cascade, the higher the overall filter’s group delay (latency) becomes. This latter issue may become problematic for fast real-time closed loop control systems that rely on an IIR’s low latency property.

Nevertheless, despite these challenges, the APF equaliser is a good compromise for linearising an IIRs passband phase characteristics.

The APF equaliser

ASN Filter Designer provides designers with a very simple to use graphical all-phase equaliser interface for linearising the passband phase of IIR filters. As seen below, the interface is very intuitive, and allows designers to quickly place and fine-tune APF filters positions with the mouse. The tool automatically calculates \(\small f_c\) and \(\small r\), based on the marker position.

Right clicking on the frequency response chart or on an existing all-pass design marker displays an options menu, as shown on the left.

You may add up to 10 biquads (professional version only).

An IIR with linear passband phase

Designing an equaliser composed of three APF pairs, and cascading it with the Chebyshev filter of Figure 1, we obtain a filter waveform that has a much a sharper peak with less attenuation and oscillation than the original IIR – see below. However, this improvement comes at the expense of three extra Biquad filters (the APF cascade) and an increased group delay, which has now risen to 24 samples compared with the original 10 samples.

IIR lowpass filtering result with three APF phase equalisation filters
(minimal phase distortion)

The frequency response of both the original IIR and the equalised IIR are shown below, where the group delay (shown in purple) is the average delay of the filter and is a simpler way of assessing linearity.

IIR without equalisation cascade

IIR with equalisation cascade

Notice that the group delay of the equalised IIR passband (shown on the right) is almost flat, confirming that the phase is indeed linear.

Automatic code generation to Arm processor cores via CMSIS-DSP

The ASN Filter Designer’s automatic code generation engine facilitates the export of a designed filter to Cortex-M Arm based processors via the CMSIS-DSP software framework. The tool’s built-in analytics and help functions assist the designer in successfully configuring the design for deployment.

Before generating the code, the IIR and equalisation filters (i.e. H1 and Heq filters) need to be firstly re-optimised (merged) to an H1 filter (main filter) structure for deployment. The options menu can be found under the P-Z tab in the main UI.

All floating point IIR filters designs must be based on Single Precision arithmetic and either a Direct Form I or Direct Form II Transposed filter structure. The Direct Form II Transposed structure is advocated for floating point implementation by virtue of its higher numerically accuracy.

Quantisation and filter structure settings can be found under the Q tab (as shown on the left). Setting Arithmetic to Single Precision and Structure to Direct Form II Transposed and clicking on the Apply button configures the IIR considered herein for the CMSIS-DSP software framework.

Select the Arm CMSIS-DSP framework from the selection box in the filter summary window:

The automatically generated C code based on the CMSIS-DSP framework for direct implementation on an Arm based Cortex-M processor is shown below:

The ASN Filter Designer’s automatic code generator generates all initialisation code, scaling and data structures needed to implement the linearised filter IIR filter via Arm’s CMSIS-DSP library.

What we have learnt

The roots of a linear phase digital filter must occur in conjugate reciprocal pairs. Although this no problem for an FIR filter, it becomes infeasible for an IIR filter, as poles would need to be both inside and outside of the unit circle, making the filter BIBO unstable.

The passband phase response of an IIR filter may be linearised by using an APF equalisation cascade. The ASN Filter Designer provides designers with everything they need via a very simple to use, graphical all-pass phase equaliser interface, in order to design a suitable APF cascade by just using the mouse!

The linearised IIR filter may be exported via the automatic code generator using Arm’s optimised CMSIS-DSP library functions for deployment on any Cortex-M microcontroller.

 

 

Download demo now

Licencing information

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Automotive is one of most important sectors in the development of IoT. This interest comes from Industry as Consumers as well. That’s why governments in Europe and elsewhere are supporting these new developments.

Benefits

  • 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 garantuees
  • Increase security for your cargo and your equipment

A longer lifetime for your equipment with Preventive maintenance

With IoT, you can create new ways of automotive and do more within the same budget. Automotive is an industry which deals with heavy circumstances like dust, wind, heat and pressure. So, it’s important to recognize if the equipment is still working properly.  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.

Be in control and optimise your processes and increase value

On the other hand, you can add value for yourself and for your clients as well by monitor and interconnect your devices. To monitor and control one device in itself (like the development of self-driving cars). But also IoT delivers new possibilities to interconnect automotive in other grids. An example: a smart grid, where an office notices that a visitor will arrive in some minutes and already appoints a parking place to this visitor. By creating a network in which you know which device is on which location, you can optimize your just-in-time management and get more value by being able to deliver guarantees to your clients.

Security

Security has long time being disregarded, but is becoming one of the more important issues in Automotive. And with reason: think about the hack on harbour terminals

Read more: https://www.advsolned.com/automotive/

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Competition on costs is ever increasing. Meanwhile, customers are more demanding in their expectations. In 2024, global smart sensor market will have a value of $80 billion. In others words: become part of the future or become obsolete!

Challenges Asset Managers

Asset managers are faced with the following challenges:

  • Asset managers demand huge cost savings
  • Tightening of budgets for maintenance programmes
  • Less service disruptions and customer complaints
  • Increasing demands from users
  • No Control and optimal use of my assets
  • Risk of hacking by terrorists
  • Remote firmware updates

With IoT, you can give your equipment a longer lifetime and thus save on repair and replacement costs.

Your customers will become more satisfied with your services. With solutions which weren’t possible until now, products can ‘think’ for their users. Like: the health of the lamp and power quality of street lights, refrigerators which will signal to a car that owner is out of milk, a space on a parking lot is reserved for the visitor when he’s close to the office etcetera.

And the other way around: remember the first time you went in a hotel which had Wi-Fi and you thought: “great”! You’ve probably forgotten; nowadays, not having Wi-Fi has since long became a standard. In IOT, users raise the expectations and will be dissatisfied with devices which do not help them.

A dashboard helps you to view in one glance which assets are working properly and which are probably in need of repair or replacement. Further, you learn when, where and how intensely your assets are being used, so you use your assets more efficiently.

In a world of connected devices, security is very important. Hackers will try to break in: to steal, to cause harm or to shut down your devices. Without security, hackers can make their entry from anywhere: from one of your devices, but also an unsecured device from one of your employee’s at home. So, in the world of IOT, security of these devices is key.

Read about solutions: https://www.advsolned.com/asn-condition-monitoring/

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How do you get the best performance from your IoT smart sensor?

The global smart sensor market size is projected to grow from USD 36.6 billion in 2020 to USD 87.6 billion by 2025, at a CAGR of 19.0%. At least 80% of these IoT/IIoT smart sensors (temperature, pressure, gas, image, motion, loadcells) will use Arm’s Cortex-M technology.

IoT sensor measurement challenge

The challenge for most, is that many sensors used in these applications require 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. So ther’s no need to redesign your hardware: a massive cost saving!
  • To reduce the data sent off to the cloud by pre-analysing data. So send only the data which 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. Now we are able to estimate 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). So, you’ll be up and running within minutes.

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Water and rail infrastructure are one of the cornerstones of smart grids, such as smart cities. In them, algorithms are found everywhere.

Challenges in Water and Rail infrastructure

  • Many parts of the infrastructure are decades old and have high maintenance costs
  • Preventative maintenance of components (motor, chain, wiring, jackscrew, etc.) is required to reduce costs and maintain safety
  • Less service disruptions and customer complaints
  • No control of assets, and so no idea if assets are working properly
  • New analysis methods required, as existing infrastructure cannot be dismantled for installation of traditional sensors
  • Most of the infrastructure has been built when security was not an issue. This makes the infrastructure an easy target for hackers and terrorists

Decades old infrastructure

Many parts of the infrastructure are decades old. That’s also one of the reasons that they have high maintenance costs. Besides, regular maintenance consists of doing regular maintenance rounds. Here, every device gets the same attention. However, with preventative maintenance, you can focus on devices which really need it.

Less service disruptions and customer complaints

So, with preventative maintenance, you’ll not only reduce costs. But even more important: devices maintain to be safe for users. Due to timely recognition, you can plan maintenance before a little fault has led to real damage. So, you have less service disruption and more customer satisfaction.

No control of assets

Another challenge we hear is that companies have no control of assets, and so no idea if assets are working properly. Maybe companies have control of the assets they recognize. However, they have no idea if all devices are in scope and how these are connected.

New analysis methods required

The above-mentioned means that new analysis methods are required. However, the existing infrastructure cannot be dismantled for installation of traditional sensors.

Security of assets

Most of the infrastructure has been built when security was not an issue. This makes the infrastructure an easy target for hackers and terrorists

Find out how you can solve your IoT solutions with our algorithms!

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Biomedical devices are one of the golden nuggets of IoT.

What are the challenges?

  •  Tightening of health system budgets
  •  Higher treatment costs due to an aging population
  •  Long patient waiting times
  •  Protection of patient medical data from hackers

Biomedical devices are one of the golden nuggets of IoT. The medical industry has the challenge that health system budgets are being tightened. This is further complicated by an aging population with higher life expectancy and higher demands for medical treatment. As a consequence, serving a population with an increasing aging population means that there will be longer patient waiting times and increased medical costs.
Smart medical devices are viable solution to facilitate this for many people, especially the elderly who greatly value their independence.

Exercises at home

A lot of time is lost travelling to therapy appointments, and for elderly people with limited mobility, this is not always possible. A much more efficient method is to allow patients to do their exercises at home. Smart sensors provide a simple way of ‘measuring if they do their exercises correctly’ and if they are on track for recovery. Patients don’t have to travel and spend hours sitting in a waiting room. The therapist just has to follow the patients’ developments and make an appointment when necessary. And at an appointment, the therapist can easily dive into details, because the patient has followed his recovery themselves. This frees up the therapists’ time, and allows them to focus on the patients with more serious injuries.

Security

Meanwhile, there is the need for protection of patient medical data from hackers. Hospitals are an interesting target for terrorists and other evil-doers. That’s why prevention from being hacked is very important. And if you are being hacked, then you want to know as soon as possible, so you can take action in time, before a hacker has caused any serious damage.

In the IoT of medical devices, algorithms play an important role. Use our algorithms to filter and analyse your ECG and EMG signals. Read more about help with your challenges: https://www.advsolned.com/biomedical/

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