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CONTENTS & COMMENT CONTENTS


Front Cover The publishers would like to thank Strainsense Limited for the use of their images on the front cover of DAQ, Sensors & Instrumentation 2017.


The source guide for engineers, scientists and technicians


It’s good to talk


2-3 Making sense of ADAS As vehicles become more connected and aware, with an expectation that they will react to their surroundings and eventually drive themselves, the automotive industry is facing an increasing challenge of ensuring its test regimes are robust enough to account for the escalating number of variables such sensor proliferation represents - including the human behind the wheel.


5-6 How to pinpoint bearing failure at an early stage Envelope signal processing is a relatively new technique used to pinpoint bearing failure at an early stage. This relatively unknown technique can minimise the risk of machine damage and failure.


9 & 12 Drivers under pressure How external tyre sensing aims to take the pressure off motorists to make regular checks of their tyres.


10-11 Technology events Important dates for your diary


15, 16 & 18 Company guide A guide to DAQ, Sensors & Instrumentation suppliers


20 Enhanced pyrometer alignment The SPOT Actuator was unveiled for the first time at the end of November at Aluminium 2016 in Dusseldorf, Germany. It provides enhanced target alignment for measuring temperatures at the die exit or quench exit on aluminium extrusion presses.


© Concorde Publishing Ltd 2017 I


t is ironic that as industry steams towards a connected future, our wider society is recoiling from the idea. Isolationist policies appear to be the order of the day: as Trump pulls away from a global markets and the UK Government prefers to go it alone in the world rather than in partnership with its closest neighbours.


What if anything can we learn from our automated systems?


Conversely, these seem determined to forge much closer links with their closest neighbours, as unified standards make it ever more feasible to speak the same language. As we scale to a world of billions of intelligent, connected sensors and other devices, mission-critical data must be transferred reliably, in real-time and in the right order of priority. Manufacturing operations require tight coordination of sensing and actuation to safely and efficiently perform closed loop control. Industrial automation approaches have traditionally been hampered by different incompatible and non-interoperable standards used for communication between devices – in effect, a barrier of language and culture. As a result, users have often found themselves locked into proprietary systems, while vendors have had to develop multiple versions of essentially the same product. Machines and their designers have learned that it’s good to talk, and how hard it becomes when everyone retreats into their own proprietary dialects. But what about all the data that these connected sensors generate?


The ability to generate, assimilate and interpret huge quantities of data and take appropriate action means that data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data - extremely large data sets to be analysed computationally to reveal patterns, trends and associations. And yet, while machines are heading in that direction, human society once again appears to be heading in the opposite direction. Divergent levels of trust in statistics has opened up in western liberal democracies. Shortly before the November presidential election, a study in the US discovered that 68% of Trump supporters distrusted the economic data published by the federal government. In the UK, a research project by Cambridge University and YouGov discovered that 55% of the population believes that the government “is hiding the truth about the number of immigrants living here”. Antipathy to statistics has become one of the hallmarks of the populist right, with statisticians and economists chief among the various “experts” that were ostensibly rejected by voters in 2016. Perhaps the answer lies in educating ourselves how to analyse data objectively, in the way that we have taught our machines to do for us? Has the age of the machine truly arrived?


Andy Pye, Editor


Concorde Publishing Ltd 100 Borough High Street, London SE1 1LB, UK Tel: +44 (0)20 7863 3079 Email: daqsensors@concordepublishing.com Web: www.EnvironmentalEngineering.org.uk


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