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  • an intelligent predictive maintenance visualization system for your most critical machinery health and condition monitoring
  • an intelligent predictive maintenance visualization system for your most critical machinery health and condition monitoring
  • an intelligent predictive maintenance visualization system for your most critical machinery health and condition monitoring
  • an intelligent predictive maintenance visualization system for your most critical machinery health and condition monitoring
an intelligent predictive maintenance visualization system for your most critical machinery health and condition monitoring

Visualization: BKC critical machinery health monitoring system combines with the three-dimensional model of the plant and machine assets, present the real-time data and state changing visually. For instance, through shaft center line plots at all bearings,you can see the minimal oil film clearances, and you can also check if the shaft was lift correctly during the turning gear, etc. Moreover, the difficult-to-understand time-frequency analysis results are directly linked to the status of the unit to achievereal-time visual asset condition monitoring.

Analytics: Our system providing complete all tools of asset condition monitoring, include the alarming approaches with BKC many years’ experiences, so, we can quickly detect the meaningful anomaly changes,release manpower from the huge measured data. BKC developed an intelligent fault diagnosis knowledge base plug-in system with BKC proprietary algorithm, it can analysis the data and conduct the anomaly detection and identify potential malfunctions indetail, find out the fault locations, severity, root causes and action plans, generate the exception report, and deliver the messages to the right people, provide advanced decision supports for the machinery healthy operation.


  • Product Details

Key Technologies

Collect vibration data from TSI system, import into the intelligent fault diagnosis knowledge base system to automatically identify the fault vector features, and through the self-learning ability of the data feature matrix, and with the help of the specialist diagnosis engine, the operation status and the starting point of deterioration are automatically detected, analyzed and concluded, and then the maintenance and repair suggestions are automatically pushed to the right people, so as to avoid machine trips, which caused by the missing of regular detection, wrong analysis, incorrect conclusion, and deficiency of maintenance strategies. Three-dimensional dynamic display of the trends of the shafting, cylinder block, sliding pin system, water and steam intake models.

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Classic HMI example

The attached HMI plot presented the shaft center line plot, they demonstrated the real-time shaft center positions in every bearing. Reviewing  the historical cold startup data, we can check if the shaft was lift correctly while in turning gear;The upon plot showed the direct vibration amplitudes and bearing metal temperatures of every bearing; the bottom plot displayed the shaft speed, active power and reactive power, stator current of generator etc. Machinery malfunction causes automatic diagnostics, including imbalance, thermal bending, unstable oil film, etc. Once the plug-in fault diagnosis knowledge system was triggered, the exception report will deliver to the correct people.

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