The customer operates various drives throughout the plant and have chosen the mill's vertical fan drive, a vital component in the cement production process, for this pilot project. Recognizing its importance, a thorough criticality assessment was conducted to understand the impact of potential shutdowns due to drive failures. To establish optimal performance and proactive maintenance, predictive analytics has been performed, paving the way for a more resilient and efficient operation.
The necessary data has been efficiently collected from the selected inverter on the technological device. This data will be thoroughly evaluated to establish optimal performance. We efficiently processes low-frequency and high-frequency data from the monitored control unit of the Siemens SINAMICS CU 320-2 DP, demonstrating its robust capabilities. During the pilot period, Siemens service experts successfully collected data from the equipment, and the predictive analytics consistently and precisely assessed its condition and operation. Our predictive analytics modules operated flawlessly, and the evaluated results aligned perfectly with reality. By leveraging predictive analytics and its advanced capabilities for transparency and AI-driven predictive maintenance, Siemens service experts confidently arrived at the following findings:
1. AI for Converter
There is no overload during the operation of the device and the components are correctly dimensioned.
There is no significant and frequent overheating of the switching elements (IGBT) of the frequency converter.
2. AI for Applicaton
The module correctly evaluated various situations when the individual monitored parameters deviated from the learned values and in the given cases the application reported a warning or an alarm.
3. AI for Grid
This module proved that the quality of the power supply network of the monitored device is stable and doesn't have undesirable high higher harmonic components in the frequency spectrum.
Our predictive analytics pilot installation demonstrated its modules' robust functionality and critical purposes. With its impressive transparency capabilities, the application verified the accuracy of the monitored device settings, and the predictive maintenance effectively provided warnings whenever the device's operation strayed from the usual.
“With predictive analytics Siemens solution we found several critical situations on our device:
The alarm reported by the mains AI module has been evaluated for a possible mains shutdown.
Reported warning and alarm due to change in speed.”
Jan Sadloň, Electrical Maintenance Master
Thanks to predictive analytics it is possible to predict malfunctions that could happen in the future on the selected monitored device.
The cement plant in Mokra is one of the largest and most modern cement production plants in the Czech Republic. In the Czech Republic, Heidelberg Materials local subsidiary, Heidelberg Materials CZ, a.s. (formerly Českomoravský cement, a.s.) is the market leader in high-quality cement, aggregates, and ready-mixed concrete.They have been active in the Czech market since 1991. The company operates two cement plants that produce various types of cement, over 25 aggregates plants and over 80 ready-mix concrete plants. Regarding cement plants, the Radotin plant is located near the capital city of Prague, and the Mokra plant is near the second-largest city, Brno. The customer focus' on high-quality sustainable production that is achieved by operational excellence supported with digitalization tools and environmental driven spirit.
Heidelberg Materials, CZ - Optimal performance with predictive analytics
Pilot implementation of the predictive analytics - EDGE application
Heidelberg Materials, CZ - Optimal performance with predictive analytics
Pilot implementation of the predictive analytics - EDGE application
Completion: 2024
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