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Condition monitoring in the smart factory

16 May 2017

Industry 4.0 is all about having better quality and quantity of data, more intelligently communicated and more effectively used, from shop floor right through to enterprise level. David Hannaby, SICK’s UK product manager for presence detection, considers what this means in terms of condition monitoring

When faced with the everyday realities of reacting to failures as they occur, the Smart Factory may seem like a far away ideal when it comes to optimising machine operation and maintenance. But, Industry 4.0 really does offer practical, tangible benefits right now for both manufacturers and machine integrators.  

Predictive maintenance

Instead of the false economy of 'if it ain’t broke don’t fix it', production lines are increasingly developing more robust approaches that can minimise those resource-intensive reactive responses and eliminate breakdowns as well as reducing the time taken for planned outages.  A planned and predictive approach to maintenance lowers the whole-life operating costs of the machinery and extends the life of the equipment.

Data is key

In order to predict how any machine, or component, is going to perform, you need data; data that is accurately and reliably collected, properly communicated and intelligently interpreted.  Routine approaches can then prevent or reduce the risk of faults, failures or excessive deterioration of equipment.  

That’s where sensors come in: sensors are on the front line of the fourth industrial revolution.  Without their fundamental ability to monitor processes and collect reliable, high-quality data at the field level in the first place, the Smart Factory simply would not exist.

Firstly, sensors are already well on the way to detecting and measuring any object, in virtually all industrial automation applications.  The accuracy and consistency now achievable means production tasks can be controlled reliably to higher performance levels than ever before, with resultant benefits in product quality control, low wastage and minimal line downtime.

Communications

Just as important as the sensors themselves, is the power of IO-Link. IO-Link has evolved into one of the most highly regarded two-way interfaces available today.  Before IO-Link, the information collected on machines was, in a sense, ‘trapped’ there, i.e. the data was not automatically visible and available to the control system.  Then came IO-Link – and sensors were given a voice.  

All the PLCs, computers, data networks and factory nets don’t mean a thing if they don’t have the quality data fed to them in real time from the sensors operating reliably on the shop floor.  

Condition monitoring

With the control system fully connected to 'see' the sensor, you have access to a full range of diagnostic capabilities that were not available previously. Is the sensor about to reach the end of its life?  Does it need cleaning?  Will it last until the next production changeover? 

Through the combination of this sensor self-monitoring – both during set-up and ongoing operation – and full transparency, production teams have much greater flexibility in monitoring their processes.  Pre-failure notifications can prevent failures before they happen and sensor diagnostics can be integrated into flexible, needs-based maintenance plans.

By continually monitoring process flow, faults can be displayed and detected quickly. If problems do arise, the cause can be easily determined with easy-to-use visualisation tools, either on the sensor itself or via an HMI, so machine downtime is minimised.

Diagnostics and sensor self-monitoring

Industry 4.0 is already here, so when it comes to condition monitoring, there are opportunities right now to take advantage of Industry 4.0 capability and achieve production efficiencies that reduce scrap, save downtime and energy, and optimise product stores.

In terms of sensor selection, that means opting for devices that offer an open gateway to system connectivity, and at the same time, provide the most reliable and intelligent response to the specific production task required.

 
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