reduced maintenance costs*
and increased operating life up to 30%*
*Based on public studies; individual results may vary
Machinery Protection is fully redundant, scalable and is thus designed to provide extremely high fault tolerance.
Vibration Monitor (formerly known as VIBROCAM 5000) is the diagnostic monitoring solution for the analysis and diagnostics of all power plant machinery.
Anomaly Monitor is a system for continuous monitoring of one power plant or a whole fleet in steady-state and transient operating condition.
Machinery Protection is fully redundant, scalable and is thus designed to provide extremely high fault tolerance. Safety levels for the system are extremely high thanks to its three fully implemented redundancy nodes. The system can be integrated into the Siemens I&C and supports configuration from the plant DCS. PROFIBUS and MODBUS interfaces are available and can replace 0/4…20mA interfaces. The technology implemented into Machinery Protection also features digitalization throughout the system, “voting” within the system (on-board-voting) and short response time.
The main objective of the Siemens Machinery system (also called VIB3000) is to fulfill safety and availability targets without compromise by protecting the machine and the environment from the consequences of machine failure.
To consistently implement condition-based maintenance strategies, in-depth knowledge of the power plant assets’ condition is required. Siemens Vibration Monitor offers condition monitoring for any kind for turbines (Siemens or other manufacturers), any kind of fan (induced-draft, forced-draft), any kind of pump (feed water, condensate, cooling water), compressors, hydro-generators, gearboxes, electric motors, conveyor drives, etc. as well as for any kind of DCS (Siemens or third-party).
Siemens Anomaly Monitor, also part of the Omnivise Fleet Management solution is a system for continuous monitoring of one power plant or a whole fleet in steady-state and transient operating condition. Early detection of faults prevents machine failures and avoids high repair costs. Through asset diagnostic technology, Anomaly Monitor provides deep insights into the conditions of plant assets and consolidates relevant information on fleet-wide monitoring screens. Anomaly Monitor uses machine learning algorithms to train the normal behavior of an asset or process from historical data of related measurements. For each asset to be monitored, one or more databased models can be created and trained. After models have been trained and learned the normal behavior of the respective asset, new measured data gets continuously compared with what is expected based on the training data. This happens within reasonable cycle times, normally in the seconds range. Consequently, the user is informed extremely fast in the case of any deviations from the normal values.
Anomaly Monitor supports the following core functions that support the workflow from model creation, data handling to continuous monitoring:
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