Predictive maintenance & condition monitoring


A staged approach to maintenance


Introducing a predictive maintenance regime has huge productivity benefits, but requires investment – which is why it makes sense to introduce it in three tiered stages, as Phil Burge, SKF, explains


it is more common than manufacturers like to admit: in a recent study of 450 IT and field service decision makers, GE Digital found that 82 per cent of respondents had experienced an average of two cases of unplanned downtime in the preceding three years. The outages lasted an average of four hours – and cost around $2million in each instance. In response to statistics like this, maintenance


U


teams have made rigorous changes to the way they service machines. Old style approaches such as corrective maintenance – where a machine is fixed after it has failed – or preventative maintenance (where it is serviced at fixed intervals) have begun to be replaced by a more


nplanned downtime can have serious consequences – from an inability to fulfil orders to a loss in turnover. In addition,


modern regime of predictive maintenance. Here, an array of sensors keeps a constant


watch on machine conditions – this is known as condition-based monitoring (CBM). Maintenance is then carried out when performance begins to dip, ensuring that production efficiency remains as high as possible. Of course, older maintenance regimes are still


prevalent in industry. However, companies that have embraced the idea of predictive maintenance are then faced with a choice: how far should they go? While predictive maintenance improves efficiency and avoids unplanned downtime, it requires more upfront investment than older methods. The answer is to adopt predictive maintenance at a level that makes sense – in terms of company size, criticality of assets, and maintenance budget.


SKF has developed


this kind of staged approach to condition-based asset care. Its tiered strategy offers three levels of condition-based asset care: basic, better or best. Users can then adopt the most appropriate level – mixing them up if necessary – depending on their budget and the criticality of individual assets.


BaSIc care For modest budgets – or to ensure that lower- value assets keep running – SKF offers its basic asset care package. In most cases, machinery will be monitored


manually using basic handheld devices, which in- house maintenance technicians use to perform walk-through machine data collection. An example of this type of device is SKF QuickCollect, which monitors both vibration and temperature. It then transmits the data wirelessly to a mobile device – where an entry-level app called SKF QuickCollect provides machine diagnostics and analysis. This approach can be applied widely – across a


range of industries – and is a perfect way of introducing a predictive maintenance regime. It may also be applicable if only certain assets on the factory floor are to be monitored. However, no monitoring can take place at all


without appropriate sensors. There are many types available to monitor machine conditions, but among the most popular and useful are vibration sensors (sometimes called accelerometers). These detect anomalous bearing vibrations – caused when the surface begins to wear – often far in advance of when a human operator can ‘hear’ any problems. The bearing can then be replaced before it causes damage to the machine – or even catastrophic failure. The relatively low cost of vibration sensors


means that basic CBM is within reach of even the smallest budget. However, it is vital to ensure that Continued on page 16...


Instrumentation Monthly October 2018 15


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56