The future of condition monitoring

Dhe bearing manufacturer SKF has developed an integrated, self-powered sensor package called SKF Insight that can communicate a bearing's operating environment. Thanks to miniaturization of the technology, condition monitoring is autonomous: it supplies itself with power from the application environment, communicates via the Internet and can be used in applications where it was previously not possible.

An "intelligent" warehouse

The project arose from the fact that few bearings fail in service as a result of normal operating conditions. Most bearing failures occur due to misuse, negligence, lubrication problems or operating conditions that were not foreseen when a machine was designed or a bearing was selected. That's why SKF engineers wondered what would happen if a bearing could determine its own operating environment - the actual loads it's subjected to, the quality of lubrication, temperature, etc. - and relay that information to the equipment operator or machine manufacturer via the Internet. This is because in traditional condition monitoring, the first signs of bearing damage are detected by vibration measurements. A bearing starts to emit vibration signals when the first steel particles detach from the raceway surface of the rings or rolling elements. If such damage is detected, the bearing has almost reached the end of its service life. This means that it is already damaged and it is too late to take remedial action.

In contrast, SKF Insight uses sensors integrated into the bearing to monitor critical parameters such as lubricant contamination, excessive loads or temperatures (Figure 1) that can cause early failures. Bearing failure can be prevented by eliminating such anomalies in advance. The same technology can be used in a more positive way: By verifying the integrity of a bearing and due to a better understanding of the operating environment, a machine can be enabled to extend its life or increase its performance beyond what was originally intended.

Combined technology

SKF's algorithms and diagnostics interpret the data collected in terms of the severity or deviation of the operating conditions from the original design conditions. They can also detect overloads, operating deviations, lubricant contamination and lubrication problems so that changes can be made to prevent damage before it occurs. 

Bearings equipped with SKF Insight form intelligent networks that communicate with each other via a radio gateway and share their condition data for analysis purposes. The gateway can be used close to the machine or plant. System information is either forwarded to the customer for analysis with SKF@ptitude or sent to SKF remote diagnostic centers via the SKF Cloud. From here, data overviews and reports can be made available to the plant operator, machine manufacturer, SKF or other authorized parties with Internet access. Real-time condition monitoring data is accessible to all parties. With SKF Insight, customers can control the maintenance planning of their machines better than ever and optimize their workflows.

Improved availability

The SKF Insight technology is designed to make condition monitoring available for a larger number of applications, especially where it was previously not feasible or practical. This is one of the reasons why this technology has been tested in demanding applications in the wind (Fig. 3), rail vehicle and steel industries.

Wind farms, for example, can be remote and difficult to access. Together with customers, SKF is currently developing an intelligent bearing with integrated SKF Insight technology to monitor the condition of wind turbines. The idea is to transmit dynamic bearing data by radio to make available the load and lubrication conditions of the real operating environment. The cost of replacing the main bearing of a wind turbine can be so high that it destroys the original profitability calculation of the turbine. Therefore, it makes economic sense to record the load and lubrication conditions during operation and take measures to eliminate the harmful operating conditions. If the load and lubrication conditions are favourable, the wind turbine can then be operated outside its sizing range. It is often the case that the processes with the most serious consequences for the service life are based on operating conditions that were not included in the original specification.

For the rail industry, SKF integrates smart components and provides condition monitoring solutions for wheel heads that are powered by bearing rotation and communicate bearing condition wirelessly. In this application, SKF Insight provides a very cost-effective way to collect condition monitoring data where the risk of failure for each bearing can be determined over its entire service life, enabling better maintenance planning and scheduling.

Also for the tough and harsh environment in the steel industry, SKF has developed an intelligent bearing solution that can monitor and wirelessly transmit key parameters to control and optimize the steelmaking process. This solution successfully monitors the condition and operating environment of bearings in a continuous casting plant with integrated, self-powered wireless sensors - an environment where cables and external sensors simply have no "chance of survival".

Reduction of environmental impact and maintenance costs

The focus of SKF's technical development activities today is on reducing the environmental impact of a machine or plant during its service life in its own and the customer's processes and operations. With SKF Insight technology, condition monitoring will be more versatile. Maintenance can be scheduled before traditional condition monitoring signals the first signs of impending failure. Most importantly, harmful operating conditions can be eliminated through proactive measures and failures can be prevented instead of just detected.

By using an SKF bearing with integrated SKF Insight technology, the total cost of ownership can be radically reduced. This is especially true for applications where a large number of bearing positions in a large area need to be monitored.