The innovative approach for higher efficiency and reliability

Operators of material handling and logistics systems need to ensure that their facilities are available when needed. Unplanned downtime and emergency maintenance are major challenges. By transitioning from time-based service models to predictive maintenance, companies can effectively optimize their processes as a whole.


SmartService – tested and proven portfolio

Siemens Logistics is continuously developing the solutions for predictive maintenance that have proven themselves in the field. The product range includes critical components of various systems – also from third-party providers.

The basis for predictive maintenance is a condition-based approach that uses artificial intelligence to recognize patterns in historic sensor data. The results are used among others to forecast the best time to perform maintenance, or to predict a wear part’s end of life. With Siemens Logistics’ SmartService portfolio, customers benefit from optimized maintenance planning and execution with just-in-time equipment servicing. Higher levels of operational availability are achieved along with increased efficiency, productivity and competitiveness.

 

Analytics and visualization

As a first step, condition data from various sensors is pre-processed on an edge device. The data are then transferred via secure channels to MindSphere, Siemens’ open IoT cloud-based operating system, for advanced analytics. Tools such as machine learning, pattern recognition and trend identification are used to analyze the condition data to determine the best time to perform maintenance.

Results from each supervised resource are displayed on dashboards in two views – asset and analytics. Based on those illustrated details service teams get information about recommended actions.

Benefits

  • Reliability and availability: Operational downtime is significantly reduced or eliminated, and operational risk is minimized through predictive analytics and transparency of the system condition
  • Efficiency: By planning maintenance activities according to the assets’ conditions, unnecessary work is reduced and/or eliminated
  • Effectiveness: SmartService enables predictive maintenance, material and resource planning as well as focused execution of maintenance activities in an optimized manner
  • Asset optimization: The useful lifetime of assets is extended through data-driven root-cause analysis and equipment optimization
  • Safety: Workplace safety is improved by reducing maintenance activities that have special safety requirements, such as working in heights
  • Versatile use: Sensors monitor different critical components of various systems – also from third party suppliers

Available products

The SmartService portfolio from Siemens Logistics’ provides innovative digital maintenance solutions.

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Electrical cross-belt sorter


Sorter 360:
The sorter’s rollers are monitored for wear and tear through stationary sensors. They determine imbalanced movements and deviations in carrier height.

SmartCarrier: Track vibration, evenness and the chain are surveyed. Sensors are located at a dedicated carrier without affecting its sorting function.

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Tilt-tray sorter


Sorter 360:
Stationary sensors monitor the sorter’s rollers for wear and tear, which is identified through imbalanced movements and deviations in carrier height.

SmartCarrier: Sensors are located at a carrier to monitor vibration and evenness of tracks and forces on chains. The carrier is still available for sorting.

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Carousel 360


Stationary sensors identify vibrations and thus mechanical issues, such as blocked or broken rollers and wear and tear of the carousel’s metal structure. Additionally, mobile sensors survey the track condition. An integrated load pin monitors the forces acting on the drive chain. It detects problems such as faulty tensioning force, worn-out friction belts or incorrect drive unit settings.

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Motor 360


Collection and analysis of data from the motor controls are done without separate sensors. Monitoring start/stop currents from drive motors for a longer period of time identifies increasing trends in nominal current that indicate an impending major breakdown.

Edge Technologies

Service 4.0 from Siemens Logistics enables airports to collect and evaluate data for the predictive maintenance of their baggage handling systems. To further improve operational processes, going forward Siemens will equip drives with innovative edge technologies from the company’s Digital Industries unit. Thanks to additional detailed information, this smart solution will enable wear to be detected earlier than before – such as on VarioTray TilterPlus. Unexpected downtime can be avoided.

The result is a considerably improved predictive maintenance approach. Customers benefit from optimal system availability and capacity, extended baggage handling system lifetime and lower operating costs.