Improve Industrial Maintenance Efficiency with Electrical Signature Analysis
In recent years, with the rise of digitalization and sustainability, condition monitoring-based predictive maintenance has gained prominence. This strategy involves analyzing data obtained from machines, such as vibration, current, voltage, and temperature, to assess their health status and predict future malfunctions. One crucial technique in condition monitoring is electrical signature analysis, which plays a significant role in predictive maintenance.
Electrical signature analysis stands out in several aspects compared to other methods. It excels in detecting faults related to bearings, stators, and rotors in asynchronous motors. Although bearing failures can be detected through vibration analysis, electrical signature analysis also provides clear indications. Furthermore, electrical signature analysis is a powerful and cost-effective solution for detecting faults in stators and rotors, surpassing other techniques.
Detecting faults caused by mechanical misalignment, unbalance, air gap misalignment, motor supply, and insulation problems is possible through both electrical signature analysis and vibration analysis. However, electrical signature analysis outperforms other methods in detecting broken rotor bars and stator issues.
Unlike other methods, electrical signature analysis does not rely on machine operating conditions to collect data. Instead, sensors are attached to the cables in the motor control center (MCC) or the panel where the machine’s electrical connections are made. This setup allows for continuous measurements in a consistent ambient environment, facilitating uninterrupted data analysis.
If you want to learn the role and importance of electrical signature analysis in predictive maintenance, we invite you to read the full blog post on our website.
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