MCSA – motor condition monitoring beyond vibration analysis

The latest motor condition monitoring technology for motors and rotating equipment offers advantages over previous methods. It is simpler to install, adapts faster to more applications, and delivers improved detection rates with earlier failure detection. This helps lower total-cost-of-ownership by up to 50 percent and shortens the payback period compared to other monitoring methods.

To improve productivity, there is a need for industrial organisations to put in place predictive maintenance strategies for managing their stock of motors. This approach will help reduce downtime, improve efficiency, reduce costs, and get more life from rotating equipment. Yet, it also benefits from the latest motor condition monitoring technologies like Motor Current Signature Analysis (MCSA)

Motor condition monitoring

The emergence of MCSA for motor condition monitoring has many advantages over the vibration, oil, acoustic, and thermal monitoring techniques. MCSA measures minor fluctuations in both the current draw and supply voltage of the power lines feeding a motor or other rotating equipment.

These electrical signatures can provide early indications of upcoming failures with increased sensitivity and accuracy over other methods. The technology can diagnose specific failure modes or their initiating root causes, whether they are mechanical or electrical.

One of the biggest advantages of MCSA is the option to place the sensors away from the motors. Installation can be in the motor control cabinet (MCC) that is a clean, dry location to protect sensors from dirt, moisture, and wear. This helps improve the reliability of the entire monitoring system. An MCC is easy and safe to access, often containing the power lines for many motors. This, along with the ability to upload data to the cloud using cellular communications, further reduces the time and cost to install MCSA sensors for a complete production line, while providing easier scalability.

Sensor data aggregation to a central, cloud-based asset management system, can also include the support of expert services. A set of machine learning algorithms first makes a model of ‘normal’ motor behaviour with an associated normal motor current signature. If a motor operating behaviour begins to drift out of the normal range, any anomalies detected and classified through recognized variations in the motor current signature. These sensing technologies and analytical methods detect and diagnose a broad range of potential failure modes including stator shorts, bearing degradation, loose rotor bars, coupling misalignment, mechanical or electrical imbalance and more.

Accurate failure prediction

These methods allow faster prediction of both known and unknown failure patterns with over 90 percent accuracy. The detection of failure modes or causes can be from weeks to up to five months in advance, depending on the type of potential failure. This enables maintenance teams to order spare parts and schedule a repair when it will least impact operations. MCSA failure analysis also provides clues to electrical conditions upstream of the motor that may be causing the issue. This can include power quality anomalies.

Over 95 percent of the total cost of ownership of an electric motor is the cost of the electricity it consumes. Issues like voltage imbalance reduce efficiency, whilst well maintained motors consume up to 15 percent less electricity. As MCSA monitors both current and voltage, it can provide metrics on energy consumption of individual motors and power factor. This insight can help facility teams make decisions to lower energy usage and reduce environmental footprint.

In summary, MCSA delivers much higher performance and a greater scope of application than other motor monitoring technologies. An asset management system that includes MCSA capability will monitor all motors 24/7. It will send notifications via mobile device or desktop when detecting an upcoming failure. This creates a complete, dependable, accurate, and easy-to-use condition-based monitoring solution. It can also help reduce the number of regular inspections needed by enabling a predictive maintenance strategy.

EcoStruxure Asset Advisor for Electrical Distribution now includes the condition-based monitoring of rotating equipment assets using MCSA technology.



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