e-MCM is a powerful online condition monitoring,predictive maintenance and power meter tool intended for critical AC rotating equipment. The patented machine learning algorithm of e-MCM enables comprehensive fault detection up to 6 months in advance. Thanks to around the clock monitoring and real-time model-based voltage and current analysis, e-MCM can detect electrical, mechanical as well as process faults of fixed, variable speed motors and generators.
Predictive Condition Monitoring e-MCM By Artesis
Remote Vibration Analysis & Predictive Condition Monitoring Using Artesis e-MCM
Artesis (e-MCM) Motor Condition Monitoring System
Highlights
Features
Ease of Use
Automated fault diagnosis feature of e-MCM makes it very simple to use by the maintenance personnel. Rather than overwhelming the end user with raw signals and data, e-MCM provides processed data results in an actionable form. The system requires minimal operator intervention for set-up and operation and provides clear indication of the nature and severity of developing faults both locally to the monitored equipment and remotely.
Real Time Monitoring
e-MCM constantly takes measurements and compares them with its reference condition, in order to assess the severity and type of any developing fault. It is able to recognize abnormalities in a wide range of operating states, and is even able to extend its self-learning process when it recognizes that is has moved beyond its original learning limits. This allows e-MCM to achieve very sensitive detection of faults without false alarms.
Simple Installation
e-MCM installation requires only three-phase voltage and current connection via low cost current transformers (CT) and voltage transformers (VT) (if needed). It is usually located at the motor control cabinet, requiring very short cable runs and avoiding the need to install equipment in remote or hazardous areas. When first switched on, e-MCM carries out automatic self-learning process during which the normal operating condition of the equipment is established. Advance analysis techniques ensure that this training takes account of variables like speed and load, and the existing faults do not result in training errors.