FEATURE TEST & MAINTENANCE PREDICTING BIG THINGS ABOUT MACHINE LEARNING


correlating factors in data to not only flag up a problem but also the root cause of it. It sounds straightforward in principle, but the number of potential things to consider can be too high for a human to work through effectively. Within a single machine, there can be


dozens of sensors or other health signals. To get a clear picture of all the things that affect reliability, that data should be evaluated alongside things like maintenance records and a history of what the machine was running. Even ambient conditions and crew data can give clues as to what issues can crop up. The only effective way to navigate the


Sean Robinson, service leader at industrial analytics platform supplier Novotek UK & Ireland, explains how proactive maintenance can be even more effective with machine learning


I


n 2006, UK mathematician Clive Humby claimed that “data is the new oil”.


Whether you’re a food processing company or an automotive manufacturer, data from production processes is the cornerstone of better efficiency, effectiveness and overall performance. Over the past five years, the industrial


sector has begun to see the value in digitalisation and has invested more in adopting it. With this has come a cultural shift from reactive equipment maintenance to proactive maintenance that pre-empts problems. Plant managers that are familiar with


the industrial internet of things (IIoT) will know that one of the concept’s biggest selling points has been the insight it can provide into equipment performance and process effectiveness, which in turn creates benefits for the bottom-line.


This has changed the culture of


maintenance. Rather than responding to a breakage or conducting planned maintenance based on expected equipment lifespan, engineers can make informed decisions about when to maintain systems based on the equipment’s condition. Minimising unplanned downtime has


obvious benefits, but it’s the reduction in scheduled downtime that adds significant value in terms of increased overall throughput for no new capital outlay. However, achieving this is challenging due to the volume of data and subsequent analysis that is required to confidently change maintenance schedules. This is where an opportunity arises for


machine learning in industrial maintenance. With machine learning, algorithms can be trained to identify


ACCELEROMETER RANGE STARTS AT JUST £99


DJB Instruments (UK) has introduced a new design and build of its A/140 accelerometer range. Historically DJB has focused on the Test & Measurement vibration market, but with this launch the company enters the competitive ‘low cost’ machine health monitoring market, with what it says is an offering that will provide a new solution at a cost never seen before. The new range starts at just £99 for a single axis 100mV/g IEPE accelerometer (A/140). The combination


of low cost and Konic Shear means improvements in cross axis control without any cost penalty, providing a step up from compression and shear plate designs used by other manufacturers. The A/140 range is sealed to IP67 and manufactured in corrosion resistant stainless steel for use in


harsh environments. At only 80g, it is typically 25% lighter than its competitors and has a low noise floor. With a range of cabling solutions also available, it is said to offer unrivalled performance at low cost, in a package that is a direct replacement for other existing solutions. Gary Chadwick, DJB’s operations manager said of the design: “The Konic Shear design continues to


– A/140;


provide consistent cross axis control despite the inevitable degradation of polarisation that occurs in all piezoelectric materials. This unique feature means the potential 40% errors that can occur in cross axis are avoided over prolonged use, a critical aspect of accelerometer technology overlooked by 90% of users.” The A/140 IEPE accelerometer is available in several different versions: Mil-C-5015 2 pin top connector Integral top entry cable – A/140/C;


Integral side entry cable - A/140/SC; Waterproof integral


cable, top entry – A/140/W; and Waterproof integral cable, side entry – A/140/SW. DJB Instruments UK


T: 01638 712288 16 SEPTEMBER 2018 | PROCESS & CONTROL www.djbinstruments.com


Novotek UK & Ireland www.novotek.com/en/


/ PROCESS&CONTROL


Machine learning allows maintenance data analysis to become a more automated process


abundance of variables is with an IoT platform with machine learning, such as GE Digital’s Predix platform and Asset Performance Management (APM) suite. Connecting an IoT-enabled machine to the platform allows Predix’s machine learning algorithms to analyse it with the APM’s combination of standard measures and advanced analytics. This allows maintenance staff to spot when, and why, a machine needs maintenance. For example, a semiconductor


manufacturer might find that it rejects 10% of its output due to faults in the manufacturing process. Although all the machines may be IoT-connected, there is too much data for an engineer to reasonably analyse. With Predix’s machine learning algorithms, the APM could, for example, identify that a machine has elevated vibration levels, which is damaging the semiconductors. The algorithms can then assess this


against historic data to spot patterns in how often this occurs, identify the performance signs that precede it and — if integrated into a management system — send alerts to engineers as the machine requires maintenance. This makes it possible for the machine to receive maintenance only when its conditions indicate it should (condition-based predictive maintenance). In effect, machine learning allows


maintenance data analysis to become a more automated process. There are certain applications where the algorithms could be permitted to directly reconfigure a machine with the right settings. Whether you believe data is the new oil


or not, it’s indisputable that it’s a valuable resource that fuels overall operational improvement. The key to achieving this is to use industrial analytics intelligently and effectively to strike oil in industrial maintenance.


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