Will DAQ be a casualty of Industry 4.0?

Machine sensors will generate vast quantities of data

Jonathan Newell finds out how Industry 4.0 is set to revolutionise manufacturing and whether DAQ as we currently know it could be a casualty.

The Industrial Internet of Things (IIoT) – or, more broadly, Industry 4.0 – is being described as the next industrial revolution and, like all revolutions, there will certainly be casualties. Traditional ways of doing things and technologies that engineers feel comfortable with will be turned on their heads and replaced by the unfamiliar, challenging and exciting world of big data connectivity.

Massive connectivity

Industry 4.0 is more than the IoT. It is a system of operating complex processes using intelligent connected devices. It is defined by high levels of computational power, big data and widespread connectivity. It crosses the boundary between closed industrial networks and the use of Internet Protocol (IP) wide area networks.

This enables parameters to be measured, machines to be monitored and components to be counted, with all this data being processed and analysed so that something meaningful can be done with it –  either automatically or with some intervention.

However, achieving this utopian world of actionable information always being at your fingertips requires some fundamental changes in the way the data is collected and managed.

Data that matters – The need to control network traffic, bandwidth and storage costs means that the indiscriminate collection of all data, particularly analogue data from measuring instruments, isn’t sustainable. And so one thing that characterises the way Industry 4.0 operates is the notion of “edge intelligence”, where data is processed at the point it is produced and only results that are of interest are transmitted across the network for analysis and action.

An example of this is video analytics packages. Instead of constantly streaming bandwidth consuming images across a network, analysis software embedded in the camera is programmed to recognise an alarm condition which triggers streaming so that operators can monitor what is happening.

Data mining – It isn’t always possible for analysis to be processed at the edge of the network and so there is still a considerable amount of “big data” analytics that need to be carried out at central servers using data mining techniques to discover trends and patterns that can reveal conditions that require some intervention.

Managing traffic – With the massive increase in connectivity characterised by the Internet of Things, industrial networks will become huge and sprawling and therefore require insight and management regarding how they operate and the distribution of traffic on them.

Developing methods of visualising network traffic can help to discover patterns in the huge amounts of data being generated.

Value of data

The reason Industry 4.0 is getting so much attention – and why Angela Merkel is sinking some 200 million Euros into its development – is the value of the data that currently isn’t being effectively exploited in industry and which will be teased out of thousands of processes using the IIoT.

By measuring, controlling and monitoring processes, industry becomes more efficient, less wasteful and more profitable. But capturing that data isn’t enough, it needs to be processed, analysed and acted upon.

This is where the value of DAQ comes into the equation and why the technology certainly won’t become a casualty of the revolution because the whole basis of Industry 4.0 is on data acquisition and processing and to this end, a new generation of DAQ controllers is on the market.

Embedded DAQ

On-board DAQ System-on-Module (SOM) devices such as the DT7816 from Data Translation is an Industry 4.0-enabled device with intelligence built into the board to enable processing at the point of data collection.

This example is equipped with eight parallel analogue inputs connected to an embedded Cortex-A8 1GHz ARM processor for real-time processing and analysis of DAQ measurements. Output is through USB or Ethernet clients or to on-board storage.

Flexible DAQ controllers

Modern DAQ controllers such as the CompactDAQ range from National Instruments provide the flexibility needed to be able to make the best use of Industry 4.0 connectivity variants including wireless, Ethernet and USB for desktop measurements.

With on-board processing and instant connectivity to the Internet of Things, the acquired data gains much more leverage in how it can be used.

The big picture

Data Translation and National Instruments are just two DAQ equipment suppliers who are already geared up for the IIoT. But the whole concept of Industry 4.0 has a scope that presents opportunities for industry to take advantage of what it offers and for suppliers to get a slice of the massive market opportunities.

Manufacturers in particular will see a transformation in the way business is conducted and the set of skills needed in order to be able to operate effectively as they go through their digital transformation. Some manufacturers have already begun to embrace Industry 4.0 wholeheartedly, particularly in Germany where Government backing is prominent. One such manufacturer is Bosch, a company that is at the forefront of Industry 4.0 development due to its high level of involvement in the sensor and connected technology markets. The company has set up the “Connected Industry Innovation Cluster” to explore new business models for manufacturing industry based on Industry 4.0 principles.

Dr Werner Struth of BoschAccording to Bosch board member, Dr Werner Struth, “We are pooling our Industry 4.0 expertise in the connected industry innovation cluster, which will make us large enough to provide our customers and the 15 Bosch divisions with the flexible and agile support they need to implement Industry 4.0.”

Projects include connected sensors for monitoring shipping cases in transit and even torque loading on screwdrivers on manufacturing lines to the widespread use of Radio Frequency IDentification (RFID) technology linked it into its digital network to make significant improvements in storage management and inventory control.

Using RFID transponders for asset management at its Suzhou facility in China, the company has already achieved a 97% reduction in the time taken to perform asset management audits and now it is linking the transponders to sensors connected to the company network, so that manual audits are eliminated entirely, with asset checks being able to be done at any time at the press of a button.

By using connected sensors to measure component inventories at point of use and other storage locations, Bosch now has enough confidence in its inventory accuracy to switch on automatic parts ordering when storage bins fall below threshold levels.

Similar inventory control at various steps of the assembly process is preventing overstocking and allowing orders to be placed in a timely manner. This reduces Work In Process (WIP) inventory by almost a third at Bosch’s diesel engine injection nozzle factory at Homburg.

Meeting the skills requirement

A significant detractor to the early adoption of Industry 4.0 isn’t the technology, it’s the available skills which are needed as manufacturers go through the necessary changes.

According to Struth, the industry needs a very well matched balance between digital expertise and engineering excellence.

At the “IT Summit” that took place in Berlin in November, Struth told delegates, “We are in urgent need of specialists who are not only familiar with products and production lines, but who can look beyond the borders of their area of expertise and also be able to analyze huge quantities of data.”

Jonathan Newell

Jonathan Newell

Jonathan Newell is a graduate of Loughborough University and has three decades of experience in engineering as well as broadcast and technical journalism.
Jonathan Newell

Latest posts by Jonathan Newell (see all)

About Jonathan Newell

Jonathan Newell is a graduate of Loughborough University and has three decades of experience in engineering as well as broadcast and technical journalism.

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