At Hannover 2013, SKF unveiled a new and innovative technology that promised to revolutionise the way in which engineers use and interface with bearings. Called SKF Insight, this new technology integrated a miniature, self-powered and intelligent wireless sensor at the heart of the bearing. For the first time, this allowed engineers to monitor the operating conditions of a bearing from within a machine, in real time. As a result, bearing wear and potential failure could be planned and prevented, rather than simply being predicted, as was previously the case.
Now, 18 months later, the technology is under validation and is finding practical applications in a number of different market sectors. Before considering these in greater detail, let’s first look back at what makes SKF Insight so revolutionary.
The Insight project was born from the fact that few bearings fail in service as a result of normal operating conditions. Indeed, most in-service failures result from misuse, neglect, lubrication problems or operating conditions that were unforeseen when the machine was first designed or the bearing originally specified.
Traditionally, condition monitoring looks for early signs of failure by measuring levels of vibration. Vibration signals are normally produced when the first small fragments of steel begin to spall from the raceway surface of the rings or the rolling elements. By the time this damage reaches the stage where it can be detected using conventional sensors it is already too late, as the bearing has already suffered damage that affects its operating performance and life.
SKF engineers wondered what would happen if instead of using external monitoring devices, a bearing could detect the critical parameters affecting its immediate operating environment; and then to make this information instantly available via a wireless connection to the plant operator or machine manufacturer.
If operating conditions can be monitored in this way then potential damage can be prevented, or at least identified before it has an impact, with corrective actions being taken while machinery is working. This would ensure that expensive and disruptive failures are avoided, thereby reducing total cost of asset ownership and giving a much longer machine operating life.
The solution that has been developed uses miniature sensors and intelligent wireless components that are embedded in the bearing and draw their power from the application environment as the bearing rotates. The package is therefore completely self-contained; there are no cables required for power or sensor output, and once installed the device operates autonomously.
The intelligent wireless communication technology inside the bearing enables it to be used in environments where traditional Wi-Fi cannot function properly. It also allows bearings to be configured in smart networks, which communicate via wireless gateways.
An SKF Insight bearing can monitor the applied load, the quality of lubrication, operating speed, temperature and vibration, and detect changes in the microstructure of the bearing steel, giving early warning signs before damage occurs at a macro-structural level. This data can then be broadcast via cloud servers either to a local operator, who can use a specialised app on a smart phone or tablet, or to a remote monitoring centre. In each case, diagnostic tools interpret the data to establish fluctuations from optimum operating conditions, including excessive loads, duty excursions and lubricant contamination, so that modifications can immediately be made to the operating conditions by adding lubricant, mitigating transient overloads and so on.
As the bearings are self-contained they can be used in applications where it has previously been impossible to embed sensors within the heart of a machine. Not only does this represent an important step forward in real time condition based maintenance, it also provides a far better understanding of the operating environment so that it may be possible, for example, for a machine to be uprated to extend its life or power rating beyond the initial specification.
One of the purposes of SKF Insight technology is to make condition monitoring more widely applicable and accessible, particularly in applications where it has been previously been considered impossible or impractical. This is one reason why the technology is in testing in challenging industries such as wind power, rail and steel manufacturing.
Wind farms can be remote and difficult to access. In some offshore applications, the cost of changing a wind turbine main bearing can be so high that it undermines the business case for building the turbine in the first place. It therefore makes business sense to record loads and lubrication conditions in service and to take action to eliminate damaging conditions.
SKF is now working with customers to integrate SKF Insight technology and develop smart bearings for wind turbine monitoring. This allows dynamic bearing information to be measured in the true operating state and to be wirelessly communicated to remote monitoring centres, or to local maintenance crews. The solution currently under consideration can monitor bearing speed, vibration, temperature and lubrication and can be retro-fitted, thereby instantly enhancing the operational potential of tens of thousands of turbines around the world.
A similar solution is being developed for wheel end bearings used in the rail sector. These are safety-critical components and are normally changed at set intervals regardless of condition. By fitting SKF Insight it becomes possible to create an extremely cost effective method of collecting condition monitoring data, so that bearing life, and thus change-out intervals, can be accurately determined based on actual rather than predicted operating conditions.
Intelligent bearing technology is opening up new dimensions, both in the field of condition based monitoring and in machine design, operation and life. Innovations such as SKF Insight are now providing for the first time the critical tools and data that engineers and business managers need to maximise the efficiency, productivity and profitability of their machine assets.
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