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The e-MCM detects developing equipment faults in advance without the need for installing sensors on the machinery, it achieves this by continuously monitoring motor voltage and current, providing early fault diagnosis and actionable maintenance insights.
The e‑MCM is an AI-powered, sensorless condition monitoring system for the continuous monitoring of AC motors and connected equipment such as pumps, fans, compressors and gearboxes. Installed at the motor’s electrical supply, it analyses three-phase current and voltage data without requiring sensors to be mounted directly on the machinery.
Utilising a self-learning digital twin and a database of more than 10 million fault signatures, the e‑MCM identifies developing mechanical, electrical, and process-related faults and presents the results as clear, actionable maintenance insights. The system can also monitor pump performance, analyse energy efficiency and estimate the operational costs associated with inefficient equipment. It also detects abnormalities early, helping maintenance teams plan corrective work, reduce unplanned downtime, and improve the reliability and efficiency of motor-driven equipment.
Through continuous equipment monitoring and data analysis, the e-MCM system enables maintenance teams to identify emerging problems and intervene before they lead to failure.
The e‑MCM follows a continuous, model-based monitoring process that learns how the connected machinery normally operates across different loads and speeds. It then compares real-time electrical data with this established reference to identify abnormal behaviour, diagnose developing faults, and present the findings as practical maintenance information.
The e-MCM is connected to the motor’s electrical supply, where it obtains three-phase voltage and current information while the motor-driven equipment continues operating.
During the self-learning phase, system identification algorithms analyse the relationship between voltage and current to create a mathematical model of the machine’s normal behaviour across different speeds and loads.
Once the learning phase is complete, the system continuously measures incoming voltage and the current drawn by the motor. These electrical signals reflect changes occurring within the motor and its connected machine train.
The collected measurements are continuously compared with the digital twin. Deviations that cannot be explained by normal operating changes are evaluated to determine whether a mechanical, electrical or process-related fault is developing.
Detected conditions are translated into clear diagnostic results, including fault type, severity, estimated time to failure and recommended corrective action. For pump applications, the system can also track flow, head and duty-point performance.
Intelligent Fault Diagnosis
Automatically detects and classifies mechanical, electrical and process-related faults using a model base containing more than 10 million fault signatures.
AI-Driven Digital Twin
Creates a self-learning mathematical model that represents normal machine behaviour across different operating speeds and loads.
Complete Machine-Train Coverage
Uses the motor as a sensor to monitor the motor, drivetrain and connected equipment from a single electrical measurement point.
Pump Performance Monitoring
Estimates flow and pressure from motor power data, tracks the pump’s duty point and identifies deviations from its best efficiency point.
Energy Efficiency and Cost Analysis
Identifies inefficient operating conditions, calculates energy-saving opportunities and provides motor-sizing and efficiency-class recommendations.
Electrical and Power Monitoring
Measures and tracks motor voltage, current and power information to support equipment-condition, energy-consumption and performance analysis.
Automated Diagnostic Interface
Displays equipment condition through traffic-light status indicators alongside fault severity, estimated time to failure and recommended corrective actions.
Flexible System Integration
Supports on-premise or cloud deployment and connects with SCADA, DCS and other plant monitoring systems through standard industrial communication protocols.
The e‑MCM system evaluates motor voltage and current patterns to detect abnormalities originating from the motor, connected equipment, and operating process. Its diagnostic coverage extends across three principal categories, enabling maintenance teams to distinguish between mechanical deterioration, electrical problems, and process-related conditions that may produce similar operating symptoms.
As sensors do not need to be mounted directly on the machinery, the e-MCM can monitor both readily accessible assets and equipment located in submerged, hazardous, hygienically controlled or hard-to-reach environments. Below are a range of compatible motor-driven machinery and equipment:
AC motors operating across different loads, speeds, sizes and voltage levels.
Centrifugal pumps, positive displacement pumps, process pumps and submerged pumping equipment.
Axial fans, centrifugal fans, ventilation fans and industrial blowers.
Motor-driven compressors used in compressed-air, refrigeration and industrial process systems.
Gearboxes, belt-driven systems, couplings and other motor-driven transmission components.
Belt, screw and chain-driven conveyors used in production and material-handling systems.
Motor-driven mixers, agitators and other equipment used for blending or process circulation.
Motor-driven aeration and clarification equipment used in water and wastewater treatment operations.
Motor-driven ventilation and air-handling systems used in industrial, pharmaceutical and controlled environments.
| Specification | Capability |
|---|---|
| Supported Equipment | Supports three-phase AC motors and generators, including fixed-speed and variable-speed applications. |
| Supported Starting Methods | Compatible with direct-on-line, star-delta, soft-starter and variable-frequency-drive configurations. |
| Operating Power Supply | Operates from a 100–240 V AC or 120–370 V DC power supply, with a power consumption of 5 W. |
| Voltage Measurement Input | Supports direct measurement up to 690 V AC line-to-line. Higher-voltage systems can be monitored using suitable voltage transformers. |
| Current Measurement Input | Measures currents up to 2,500 A using three CAT III current transformers, with a stated accuracy of 0.5%. |
| Frequency | Supports a rated frequency of 50/60 Hz and a stated measurement-frequency range of 20–120 Hz. |
| Communications | Supports RS-485 Modbus RTU for power monitoring and Ethernet communication using TCP/IP Modbus TCP. |
| Physical and Environmental Rating | Measures 94 × 64 × 110 mm and weighs approximately 450 g. Designed for indoor front-panel mounting with an IP40 rating, an operating temperature of −10°C to 50°C and up to 80% relative humidity, non-condensing. |
The following video explains how the e‑MCM works, the equipment conditions it can detect, and how its diagnostic insights support maintenance planning, energy efficiency and motor reliability.
The e-MCM can be applied to industries that relies heavily on motor-driven equipment and often contain machinery that is critical, widely distributed, inaccessible, hazardous or difficult to monitor using directly mounted sensors. Unexpected failure in these equipment can interrupt production, compromise safety, increase energy consumption, or affect production quality. Below are some of those industries:
Water and Wastewater
Oil and Gas
Automotive
Energy and Power
Logistics and Transportation
Iron and Steel
Food and Beverage
Pharmaceutical
The e-MCM sensorless condition monitoring system is developed by Artesis Technology Systems. Vibtech Genesis Pte Ltd is the official Singapore distributor for Artesis.
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