Digital Manufacturing on a Shoestring

In a previous post we asked ‘What is Productivity?’ and shared how the first wave of IT productivity related to cloud computing, customer relationship management (CRM) systems and enterprise resource planning (ERP) was only taken up by the top 5% frontier companies.

We explained how IoT, 4IR and AI machine learning will improve productivity but again, likely only for frontier companies. The difference this time is that the newer technologies will have more far reaching consequences. The frontier companies will further extend their reach over the laggards. The majority of the 5% are large companies with large budgets who are able to engage consultances such as IBM, Deloitte, Atos, PwC, WiPro, Accenture and KPMG. But what of the small to medium enterprises (SMEs)? Can they compete?

In most countries, a large proportion of companies are small to medium size. For example, in the UK, the Office for National Statistics says 98.6% of manufacturers are (SMEs). These organisations are more price sensitive and usually don’t have the luxury of significant financial resources for engaging the top consultancies and implementing their expensive solutions. Small and medium sized organisations have previously found it difficult to digitise due to the lack of availability of reasonably priced solutions.

However, solutions doesn’t have to be expensive. Low cost sensors such as Bluetoooth beacons, motion cameras, consumer AR can be combined with affordable cloud services to create solutions on a ‘shoestring’ budget. This is the aim of the University of Cambridge and University of Nottingham’s ‘Digital Manufacturing on a Shoestring’ initiative. The Institute for Manufacturing (IfM) is helping manufacturers benefit from digitalisation without excessive cost and risk. View the project’s latest news and communicate with them via Twitter.

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Bluetooth Mesh for Industrial IoT (IIoT)

There’s an informative video presentation on the Bluetooth SIG web site on Simplifying Multi-Vendor Mesh and Sensor Networks. It provides an introduction to Bluetooth mesh and explains the ways in which it can provide for Industrial IoT (IIoT).

To add to this, Bluetooth Mesh is suitable for use on the factory floor where the environment can be electrically noisy. Standard Bluetooth Mesh uses advertising on several channels rather than (GATT) connections so as to provide for more reliable communication in environments with wireless interference.

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Why Bluetooth is Perfect for the Industrial IoT (IIoT)

U-Blox has a useful article on Seven reasons why Bluetooth is perfect for the Industrial IoT, echoing some of our observations on Bluetooth LE on the Factory Floor. The article explains why Industrial Internet of Things (IIoT) systems and networks should consider Bluetooth as the communications infrastructure.

As U-Blox mentions, there’s predicted to be 5.2 billion Bluetooth device shipments by 2022. This data is from the latest Bluetooth SIG/ABI research:

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How to Start Industry 4.0 and Digital Transformation

There’s lots said about the advantages of Industry 4.0 or Digital Transformation and the associated new technologies but it’s a lot harder to apply this to the context of a business that has legacy equipment and no real way of knowing where to start.

Our previous article on productivity explained how, historically, digital transformation has been only been implemented in the top 5% ‘frontier’ companies. These have tended to be very large companies with large R&D budgets that have enabled customised digital solutions. More recently, the availability of less expensive sensors and software components have extended opportunities to the SME companies. These companies are already realising gains in profitability, customer experience and operational efficiency. Unlike previous technologies, such as CRM, the newer technologies such as IoT and AI are more transformative. Companies that don’t update their processes risk being outranked by their competition with a greater possibility of going out of business. But where do you start?

The place to start is not technology but instead something you and your colleagues fortunately have lots of experience of : Your company. Take an honest look at your processes and work out the key problems that, if solved, would achieve the greatest gains. You might have ignored problems or inefficiencies for years or decades because they were thought to be insolvable. Technology might now be able to solve some of these problems. So what kind of problems? Think in terms of bottlenecks, costly workrounds, human effort-limited tasks, stoppages, downtimes, process delays, under-used equipment and even under-used people. Can you measure these things and react? Can you predict they are about to happen? This is where sensing comes in. You might need some help getting up to speed, determining feasibility and working out the best solution.

The next stage is connectivity. You will almost certainly need to upgrade or expand your WiFi and/or Ethernet network. It can be impractical to put sensors on everything and everyone and connect everything by WiFi/Ethernet. Instead, consider Bluetooth LE and sensor beacons to provide a low cost, low power solution for the last 50 to 100m. Bluetooth mesh can provide site-wide connectivity.

Initially implement a few key improvements that offer good payback for the effort (ROI). The improvements in efficiency, productivity, reduced costs and even customer experience should be enough to convince stakeholders to expand and better plan the digital transformation. This involves replacement of inefficient equipment and inefficient processes using, for example, robotics and 3D printing. It also involves analysing higher order information combined from multiple sources and using more advanced techniques such as AI machine learning to recognise and detect patterns to detect, classify and predict. This solves problem complexity beyond that able to be solved by the human mind or algorithmic program created by a programmer.

Get Help Determining Feasibility

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Logistics and Manufacturing Pallet Tracking with Beacons

Tracking loaded pallets saves a significant amount of time, effort and hence cost in logistics and manufacturing. Pallets might contain stock, components or sub-assemblies. The top usecase is finding a particular pallet quickly. However, sensing need not be limited to pallets. Extending tracking to forklifts, racking and even people provides additional business benefits. For example, we have previously been asked to provide for the following scenarios:

“I need to ensure best use of my forklifts. Tell me when one hasn’t moved for 2 minutes.”

“I need to know if a forklift has crashed into racking.”

“I run a warehouse containing hazardous materials. If evacuation is needed, I need to know everyone has evacuated the building even if someone has temporarily gone off site”

These situations, and more, are solvable using Bluetooth beacons (In terms of asset tracking, beacons are sometimes called tags).

RFID and barcodes can also be used for tracking. However, the information is only as good as the last scan that might have been a long time ago after which the asset might have moved without being scanned. The advantage of beacons is that they repeatedly advertise so the information is always up to date.

Further advantages of Bluetooth tags are:

  • Being commodity items rather than specialist devices, tags and readers are relatively inexpensive compared to other proprietary technologies such as ultra wideband (UWB).
  • They consume very low power and battery lifetimes of 5 to 10 years are possible.
  • They are readable from handheld devices such as smartphones and tablets, providing for additional possibilities within the warehouse.

Software such as our BeaconServer™ and BeaconRTLS™ can be used to track what’s where over time, create notifications and integrate with your existing systems.

Once you have some current and historical data you can also start to do clever things such as automatically detecting and predicting over or under utilisation, finding pinch points or detecting valuable equipment about to be stolen.

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SensorCognition™ – Machine Learning Sensor Data at the Edge

The traditional IoT strategy of sending all data up to the cloud for analysis doesn’t work well for some sensing scenarios. The combination of lots of sensors and/or frequent updates leads to lots of data being sent to the server, sometimes needlessly. The server and onward systems usually only need to now about abnormal situations. The data burden manifests itself as lots of traffic, lots of stored data, lots of complex processing and significant, unnecessary costs.

The processing of data and creating of ongoing alerts by a server can also imply longer delays that can be too long or unreliable for some time-critical scenarios. The opposite, doing all or the majority of processing near the sensing is called ‘Edge’ computing. Some people think that edge computing might one day become more normal as it’s realised that the cloud paradigm doesn’t scale technically or financially. We have been working with edge devices for a while now and can now formally announce a new edge device with some unique features.

Another problem with IoT is every scenario is different, with different inputs and outputs. Most organisations start by looking for a packaged, ready-made solution to their IoT problem that usually doesn’t exist. They tend to end up creating a custom coded solution. Instead, with SensorCognition™ we use pre-created modules that we ‘wire’ together, using data, to create your solution. We configure rather than code. This speeds up solution creation, providing greater adaptability to requirements changes and ultimately allows us to spend more time on your solution and less time solving programming problems.

However, the main reason for creating SensorCognition™ has been to provide for easier machine learning of sensor data. Machine learning is a two stage process. First data is collected, cleaned and fed into the ‘learning’ stage to create models. Crudely speaking, these models represent patterns that have been detected in the data to DETECT, CLASSIFY, PREDICT. During the production or ‘inference’ stage, new data is fed through the models to gain real-time insights. It’s important to clean the new data in exactly the same way as was done with the learning stage otherwise the models don’t work. The traditional method of data scientists manually cleaning data prior to creating models isn’t easily transferable to using those same models in production. SensorCognition™ provides a way of collecting sensor data for learning and inference with a common way of cleaning it, all without using a cloud server.

Sensor data and machine learning isn’t much use unless your solution can communicate with the outside world. SensorCognition™ modules allow us to combine inputs such as MQTT, HTTP, WebSocket, TCP, UDP, Twitter, email, files and RSS. SensorCognition™ can also have a web user interface, accessible on the same local network, with buttons, charts, colour pickers, date pickers, dropdowns, forms, gauges, notifications, sliders, switches, labels (text), play audio or text to speech and use arbitrary HTML/Javascript to view data from other places. SensorCognition™ processes the above inputs and provides output to files, MQTT, HTTP(S), Websocket, TCP, UDP, Email, Twitter, FTP, Slack, Kafka. It can also run external processes and Javascript if needed.

With SensorCognition™ we have created a general purpose device that can process sensor data using machine learning to provide for business-changing Internet of Things (IoT) and ‘Industry 4.0’ machine learning applications. This technology is available as a component of BeaconZone Solutions.

Industry 4.0 Platform

The February 2019 edition of ComputingEdge magazine from IEEE has an article on From Raw Data to Smart Manufacturing (pdf – current and back issues freely available).

The article describes what they call a ‘Semantic Web of Things for Industry 4.0 (SWeTI) platform’. Although it’s very useful, it’s less of a platform in the software sense and more of an ecosystem or model.

The platform describes usecases, tools and techniques for smart applications. Using this model, BeaconZone operates in the Device, Edge and Data Analytic layers. We provide for smart devices and tools, gateways, storage, machine learning (ML) and analytics.

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Reducing Costs with Predictive Maintenance

The Nordic blog has an informative post on How IoT-Based Predictive Maintenance Can Reduce Costs. It explains how connected sensors can save maintenance costs through reduced downtime. The post provides some examples from the power industry and explains how the same techniques can be used in the tools, retail, distribution and physical infrastructure industries.

As the post mentions, the challenge is how to scale this up. We are told IoT is the solution. Here at BeaconZone, we don’t believe IoT is always the solution, especially where there’s a requirement for higher sensor sampling frequencies. There’s too much data, too much data transfer and too much server processing. It really doesn’t scale. Apart from the waste and cost of these resources, the latency of triggering events based on the data is too high. Instead, look to so called ‘edge’ or ‘fog’ computing where more processing is done nearer the sensors and only pertinent data is sent to other systems.

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Bluetooth LE on the Factory Floor

Connected factory implementations require a large number of connected assets for condition-based monitoring, asset tracking, inventory (stock) management or for building automation. Bluetooth is a secure, low cost, low power and reliable solution suitable for use in connected factories. In this post, we examine the reasoning behind some out-of-date thinking on industrial wireless, uncover the real problems in factories and provide some explanations how Bluetooth overcomes these challenges.

Operations teams are usually very sceptical about industrial wireless. They have usually tried proprietary industry solutions using wireless with mixed results. They might have experienced how connections, such as WiFi, can become unreliable in the more electrically noisy areas of factories. The usual approach is to use cable. However, this can become expensive and time consuming. Using cable isn’t possible when assets are moving and becomes impractical when the number of connected items becomes large as in the case of connected factories. As we shall explain, Bluetooth is intrinsically more reliable than WiFi even through they share the same 2.4GHz frequency band.

There’s usually lots of electrical noise in an industrial environment that tends to be one of two types:

  • Electromagnetic radiation emitted by equipment. This typically includes engines, charging devices, frequency converters, power converters and welding. It also includes transmissions from other radio equipment such as DECT phones and mobile telephones.
  • Multipath propagation which is reflection of radio signals off, usually metallic, surfaces and received again slightly later.

It’s important to understand how Bluetooth and other competing technologies react to these types of interference. There’s a useful study (pdf) by Linköping University, Swedish Defence Research Agency (FOI) and the University of Gävle on noise industrial environments.

Noise in industrial environments tends to follow the following spectral pattern:

Electrical noise spectrum

There’s usually lots of electrical noise up to about 500MHz. This means wireless communication using lower frequencies, such as two way radio, exhibits a lot of noise. Pertinently, several wireless solutions for industrial applications use frequencies in the 30–80 MHz and 400–450 MHz bands. Bluetooth’s and WiFi’s 2.4GHz frequency is well above 500MHz so exhibits better reliability than some industrial wireless solutions. Incidentally, in the above charts, the peaks around 900 MHz and 1800 MHz mobile phone signals and 1880–1890 MHz come from DECT phones.

The magnitude of multipath propoagation depends on the environment. It’s greatest in buildings having highly reflective, usually metallic, floors, walls and roofs. If you imagine a radio signal wave being received and then received again nanoseconds later, you can imagine how both the amplitude (peaks) and the phase of the received signal becomes distorted over time. Bluetooth uses Adaptive Frequency Hopping (AFH) which means that packets transferred consecutively in time do not use the same frequency. Thus each packet acts like a single narrowband transmission and there’s less affect of one packet on the next one. However, what is more affected is amplitude which manifests itself as the received overall signal strength (RSSI). RSSI is often used by Bluetooth applications to infer distance from sender to receiver. We will mention mitigations for varying RSSI later.

It’s important to consider what happens when there is electrical noise. It turns out that technologies invented to ensure reliable transmission behave much less well in noisy situations. One such technique is carrier sense multiple access (CSMA), used by WLAN (WiFi), that listens to the channel before transmitting and waits until a free channel is observed. CSMA and automatic auto repeat (ARQ) also have re-transmission mechanisms. The retrying can also incur significant extra traffic that can overwhelm the communication in noisy environment. Bluetooth doesn’t use such schemes.

The previously mentioned research classifies different wireless technologies according to the delay when used in noisy environments:

Bluetooth (and WISA) is a good choice for noisier environments. It’s particularly suited for applications with lower data rates and sending at periodic intervals.

A final consideration is interference between Bluetooth and other technologies, such as WiFi, that use similar 2.4GHz frequencies. As mentioned in a previous post, there’s negligible overlap between Bluetooth and WiFi channel frequencies.

In summary, Bluetooth is more suited to electrically noisy environments and also offers low cost, low power and secure wireless communication.

These conclusions correlate well with our own empirical observations. We have found that Bluetooth advertising is still received in environments we have measured, using a RF spectrum analyser, to be electrically noisy around 2.4GHz . We believe this is because Bluetooth advertising hops across three frequencies such that there’s less likelihood of noise on all three. Advertising is also very short, typically taking 1 or 2 ms, making the coincidence of the noise and the advertising less likely than would be the case of a longer transmission.

It has been our experience that solutions using Bluetooth advertising are more reliable than those using Bluetooth (GATT) connections, especially in noisy environments when it’s difficult to maintain the chatty protocol of a connection over a long time period. In noisy situations, advertising is usually seen on a future transmit/scan if the first advertising is lost. By coincidence or design, Bluetooth Mesh is built on communication via advertising rather than connection and for this reason is also reliable on the factory floor.

However, using Bluetooth isn’t a silver bullet. There are situations where factories, or more usually parts of factories, have reflective surfaces or unusual radio frequency (RF) characteristics stretching into unforeseen frequencies. Poorer performing WiFi also needs to be considered in context of choosing between Ethernet and WiFi gateways and Bluetooth mesh.

It’s important to do a site survey including RF spectral analysis. This will uncover nuances of particular critical locations or coverage that can drive subsequent hardware planning. It can also feed into requirements for software processing, for example particular settings for processing within a real time locating system (RTLS) to cater for more varying RSSI.

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