PD & MCSA Permanent Monitoring
One platform. Two complimentary technologies.
Squirrel-cage induction motors are critical to modern industry, and a lack of condition visibility can lead to unexpected failures, catastrophic downtime and surging operational costs. Stator insulation and rotor faults account for a significant proportion of induction motor failures.
Stator winding insulation is susceptible to ageing, deterioration and manufacturing defects, which can ultimately result in catastrophic failure. Electrical failures impose substantially greater business interruption costs because of the complexity and duration of repairs. Published EPRI data demonstrates that these failures caused approximately five to six times lost production compared to bearing failures.
Squirrel-cage induction motor rotors are susceptible to defects including broken rotor bars, end-ring damage and high-resistance connections. Motors subjected to frequent start-stop cycles are particularly vulnerable due to the high transient forces generated during start-up.
The Kronos Monitor combines continuous Partial Discharge (PD) and Motor Current Signature Analysis (MCSA) in an integrated platform, providing comprehensive stator and rotor condition assessment, enabling early fault detection and proactive maintenance planning.
On-line PD monitoring provides early warning of developing stator insulation defects by continuously detecting and analysing high-frequency discharge activity originating from localized dielectric breakdown within the stator insulation during operation. Trending and Phase Resolved PD (PRPD) pattern analysis informs defect diagnosis.
MCSA monitoring assesses squirrel-cage induction motor rotor health by analysing the motor's electrical current signature. Broken rotor bars, end-ring damage and high-resistance connections produce distinctive sidebands at twice the slip frequency on either side of the electrical supply frequency. The relative amplitude of these sidebands indicates fault severity.
The Kronos Monitor continuously acquires and processes PD and MCSA data from sensors installed within the main terminal box of the motor or within its associated switchgear cubicle.
Using Monitra’s ML-42 advanced machine learning technology to identify and trend concerning signals in real time, providing early warning of catastrophic failure in both the stator and the rotor.
Designed to support CBM strategies, Atlas unifies fleet-wide PD and MCSA monitoring data, trends, asset reports, inspection records and expert recommendations in one intuitive data management platform.
Providing real-time visibility of monitored assets across individual sites or entire fleets, Atlas enables users to access and evaluate the latest condition information to support informed maintenance decisions. By facilitating the identification of developing insulation defects and deterioration, Atlas supports proactive maintenance planning and helps reduce the risk of costly unplanned outages.
Available for on-premises and secure cloud deployment, with API integration for seamless system connectivity.
Monitra’s TAG is an in-house panel of experts decades of hands-on field diagnostic experience with PhD-level expertise in HV asset condition monitoring to deliver key actionable insights, enabling operators to implement targeted preventative maintenance with confidence and achieve operational excellence.
