Why Current Signature Analysis(CSA) Monitoring
Motor Current Signature Analysis (CSA) is an intrusive method to monitor the operational condition of induction motors by inspecting current signals without interrupting production. CSA identifies faults at an early stage, including misalignment, bearing faults, rotor faults, and eccentricities. By detecting frequency spectrum deviations, CSA provides accurate and low-cost fault diagnosis. Hidden faults could grow without monitoring, causing catastrophic motor failure, downtime, and excessive maintenance.
CSA monitoring is Condition-Based Service (CBS) monitoring, where sensors provide real-time data to predict and prevent failure. CBS maximizes maintenance scheduling, downtime, and the life of rotating equipment, providing enhanced reliability and operating efficiency.
Why Rugged Monitoring-
Current Signature Analysis (CSA) is a non-intrusive condition monitoring method that examines current waveforms and harmonic patterns to identify faults in induction motors at early stages. It supports real-time and remote monitoring without disrupting operations, making it the best option for detecting rotor faults, stator faults, wedge looseness, power quality problems, misalignment, and bearing-related issues. However, it does not cover insulation-specific deterioration.
Rugged Monitoring provides a high-performance CSA-based solution featuring precision IoT sensors, edge devices, and advanced analytics software. Our customized algorithms deliver in-depth asset condition insight, enabling online and offline diagnostics with alarms and intelligent reporting.Sensors are embedded directly into motor control centers (MCCs), making CSA easy to install and scalable for industrial facilities. Supported by data-driven diagnostics and rapid data transfer, our solution enables minimized downtime, reduced maintenance expenses, and predictive maintenance for assured long-term motor reliability.
Invest in Efficiency & Sustainability
Non-intrusive and EMI-resistant, capturing precise waveforms from Motor Control Centers(MCCs) for fault detection in rotating machines.
Processes harmonic patterns and current deviations locally to support fast diagnostics and predictive maintenance.
Transforms CSA data into actionable insights through AI-powered analytics, enabling real-time fault detection and long-term motor reliability.
What We Monitor-
