A Lack of ‘Big Data’ Open Standards Is Undermining Industry 4.0 Ambitions
In 2017, the UK government announced the ‘Made Smarter Review’, a proposal for Britain to lead the world in a new industrial revolution. The ambitious review, which runs alongside the UK government’s Industrial Strategy that sets out a long term plan to boost productivity in British industry, proposed creating innovation hubs and a national organisation that could turn Britain into a global Industry 4.0 brand.
The opportunity here is immense; the introduction of automation within the manufacturing industry it could inject £455 billion into the UK economy, boost growth by 3 percent per annum and create a projected 175,000 skilled jobs.
It is predicted that this industrial digitization will lead to a shift in production and the creation of a digital industrial supply chain where products are developed, manufactured, and monitored in real-time through a single, integrated process. However, to achieve this, Industry 4.0 will require a revolution in industrial communications to create continuously ‘live’ data sharing capabilities between machines. This will enable vital data, from sensor measurements to control signals, to be continuously exchanged in real-time so that all critical industrial machinery, from robots to 3D printers, can be instantly accessed and controlled remotely by humans.
With autonomous machinery making critical components such as railway infrastructure or aircraft engines, engineers will need to be able to remotely monitor and maintain them to instantly detect and fix software faults. Continuous ‘live data’ will also be critical to enable ‘robot workers’ to operate in seamless synchronicity, continually choreographing their actions in response to live location or measurement data from other robots.
There are also plans to radically decentralise manufacturing through an ‘Internet of Thinking’ — based on a ‘DIY’ sensor network that can autonomously analyze information, rather than sending it for remote analysis. This will mean that if an industrial robot notices its own equipment malfunctioning, it will be able to recognise what it needs to do and self-correct any faults. However, such systems will need external human supervision in case of faults that they fail to detect.
Cybersecurity teams will also need to be able to remotely monitor for malicious interference and intervene in manufacturing machinery to avert industrial sabotageby cyberattack. For example, researchers have previously shown how to hack into 3D printer files to make a drone crash by altering the design specifications for the propellers.
