Maintenance Matters
The future of predictive maintenance arrives
The future of predictive maintenance arrives

What sounded like science fiction just a short time ago is now a reality in the new predictive maintenance systems becoming available from members of the global Schaeffler Group such as Schaeffler Australia.

The company’s automated rolling bearing diagnostics and the calculation of the remaining useful life of bearings are important components of Industry 4.0, which refers to a fourth industrial revolution (following water/steam power, mass production and automation through IT and robotics). Industry 4.0   introduces the concept of ‘cyber-physical systems’ to differentiate this new evolutionary phase from the electronic automation that has gone before.

“All machinery – all motors, drives, shafts, conveyors, wheels – depend critically on bearings, whether you’re in an outback mine, city factory or an urban water or energy plant supplying the population,” says Mark Ciechanowicz, Industrial Services Manager, Schaeffler Australia and also responsible for advanced technologies such as the early warning system FAG SmartQB.
Plant maintenance and operational staff were given an insight to the future at this year’s Hannover Messe 2016, the world’s biggest industrial trade fair, where Schaeffler presented to the 250,000 visitors its new predictive maintenance solutions that provide machine operators with information about the future condition of their machines.  

Schaeffler technologies flowing on into Australia in time for the Christmas New Year maintenance season included the FAG SmartCheck diagnostic system, which transfers data to the cloud. “The cloud provides greater processing power and a wider range of analysis options than existing purely local calculation systems”, says Ciechanowicz.  “This type of predictive maintenance allows not only the capacity utilisation of factories, processing plants and utilities to be optimised, but also makes it possible to plan maintenance intervals. This user-friendly technology comprises a FAG SmartQB sensor unit (a variant of the existing FAG SmartCheck), a cubic housing with a touch panel, and a cable for power and data transmission. The system was specially developed for detecting irregularities in electric motors, pumps, fans, and their rolling bearings, and is supplied with a ready-to-use configuration suitable for Australasian conditions.

“An important prerequisite for predictive maintenance is automated rolling bearing diagnostics, a function that is used in motor gearbox units, for example. These units are used not only in machine tools, but also in belt conveyors, presses, water and energy infrastructure, paper and cement plants and resources, processing and manufacturing and bulk handling processes, as well as steel mill rollers.

“Because such machine drives may be operated virtually without interruption, they require intensive maintenance in order to prevent production downtimes. This is why it is so important for operators to know the condition of the drive components at all times, and why the bearings are becoming particularly important as a central machine element.”
The new generation of the FAG SmartCheck diagnostic system represents a further step forward for Schaeffler which, with 85,000 employees worldwide, is a global leader in bearing manufacture and maintenance. In addition to identifying the threat of bearing damage, wear, and irregularities such as imbalance and misalignments based on vibration pattern changes, FAG SmartCheck’s cloud connection can be used to create an automated diagnosis in the cloud. This uses raw data supplied by the FAG SmartCheck and from additional data, e.g. from the machine control system.

From condition monitoring to predictive maintenance
Data stored in the cloud from condition monitoring systems such as FAG SmartCheck also allows the information to be used for other calculations, such as drive train and rolling bearing simulations relating to their static and dynamic strength.
“Using the real load spectra gathered during operation, Schaeffler can continuously calculate the bearings’ remaining useful life on the customer’s behalf at freely definable time intervals,” says Ciechanowicz.
Schaeffler’s BEARINX calculation tool retrieves the data from the cloud. The customer can then view the remaining useful life of every bearing in the machine using an internet-capable end device.
Schaeffler’s solution is based on three central elements:

  • A suitable system of sensors gathers reliable load data for the machine and its bearings.
  • Simulation models calculate the remaining useful life based on the dimensions of the machine and the actual loads.
  • A software platform through which the customer can access the calculations and retrieve information about his or her machine individually.

Schaeffler’s BEARINX calculation tool retrieves the data from the cloud and calculates the nominal rating life of the individual bearings. The calculation is based on loads such as speeds, torques, temperatures, and vibrations.
To back the system, Schaeffler Australia offers a full range of maintenance tools and condition monitoring equipment and services from a single source. Included in the range are hand-held data collectors for patrol monitoring, to modular online fixed vibration monitoring systems and easy-to-use acoustic emission monitoring devices. Services include vibration analysis, thermographic imaging, oil analysis and complete after-sales service by fully qualified engineers and for any application in any industry.

Optimum Maintenance Interval Planning
While condition monitoring provides information about a machine’s current condition, predictive maintenance also looks into the future and allows the optimum time for maintenance to be predicted.  
Dr Hans-Willi Keßler, Vice President of Service Products at Schaeffler, says this allows factories’ production planning to be optimised.

“For example, production can be increased if this is required to meet increased order and sufficient machine capacity is available. Alternatively, production can be reduced when there are fewer orders pending in order to match a specific maintenance interval.”  

In this way, production machine maintenance can be carried out based on the load conditions and according to requirements instead of specific time intervals and acute malfunctions, he says. This provides numerous benefits, including increased productivity through correlation between the level of capacity utilisation in production and the condition of the machine elements. What is more, replacement parts can be ordered on a just-in-time basis, which reduces warehousing costs. Last but not least, the overall operating costs can be lowered through optimum utilisation of maintenance intervals. Even in the event of incipient bearing damage, predictive maintenance can still help: if the operator knows how long a bearing will continue to function, he or she can make a valid decision as to whether or not production should be continued.
By carrying out maintenance work in accordance with the actual operating conditions, Schaeffler is ushering in a paradigm shift in mechanical engineering. In a second step, the measured load spectra can be used to completely redesign and reconfigure both individual components and entire machines to match the relevant load conditions.

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