An Answer in Efficiency
FAQ
Centenelle™ ePMs continuously and comprehensively measure the quality and pattern of the electric power supply and usage of individual electric motors. We believe this is the richest source of data that can be used to identify pending failures and inefficiencies.
Our monitors combine the well-known benefits of voltage quality monitoring, advanced power trend monitoring, and power spectral analysis
Voltage Out of Range
Continuous and detailed monitoring of the power usage of an electric motor-driven system can reveal much about the health and efficiency of the system. Diagnostic indicators that may be derived from power monitoring include:
- Pump or fan flow blockage
- Mechanical obstruction or overload
- Process inefficiencies (mixture viscosity, feed rate, etc.)
- Control system degradation
- Degradation, inefficiency, or pending failure of other system components
Power/Amperage Distortion
Various forms of power/amperage spectral analysis are well-known diagnostic methods for identifying a range of mechanical wear, balance and alignment issues with electric motor-driven systems. These powerful techniques have been generally limited in application due to the cost of equipment and the complexity of required data analysis. Electrical pattern monitoring simplifies power/amperage spectral analysis, such that it can be made available for most electric motor-driven systems.
Deviation from Optimal Power Pattern Baseline
Centenelle electrical pattern monitoring continuously captures comprehensive statistical matrices of motor load power-related operating parameters, including:
- Maximum, average, minimum, and standard deviation for watt values for a configurable averaging interval;
- Maximum, average, minimum, and standard deviation for total harmonic amperage distortion for each stable operational power level;
- Maximum, average, minimum, and standard deviation values for the highest six amperage spectral components (RMS magnitude and frequency component) for each stable operational power level;
- Maximum, average, minimum, and standard deviation for multiple parameters associated with identifiable characteristics for each unique power transition type. Power transitions are automatically detected and identifying features are automatically categorized, including:
- Startup and shutdown power surges and sags
- Power ramp-ups and ramp-downs
Each transitional feature will have one or more associated parameter sets which might include values such as duration, maximum, minimum, beginning and ending values, etc.. Maximum, average, minimum, and standard deviation values are calculated for all parameters.
When these power-related parameters are known to be optimal, the power pattern matrices can be saved as a baseline. The baseline will then be continuously compared to the active operational matrices going forward. When power-related parameters deviate from the baseline by an amount that is statistically significant, alarm indications can be generated. This powerful capability provides maintenance personnel with the earliest possible indication that operation of the electric motor-driven system has changed to a relevant degree.