Bently Nevada  AnomAlert TM Motor Anomaly Detector
Motor Failure modes Typical distribution of motor failure modes. Motors up to 4Kv <ul><li>MECHANICAL FAULTS </li></ul><ul>...
Condition Monitoring Methodologies <ul><li>Vibration </li></ul><ul><li>Temperature </li></ul><ul><li>Motor circuit analysi...
Condition Monitoring Methodologies <ul><li>Vibration </li></ul><ul><li>Temperature </li></ul><ul><li>Motor circuit analysi...
Technology – Model Based Fault Detection Compares ACTUAL motor behavior with PREDICTED behavior to detect Anomalies and di...
The inputs and outputs of the system are treated as complex dynamic signals Technology – Model Based Fault Detection MODEL...
Technology – Model Based Fault Detection Frequency  (Hz) Fault Identification MODEL Frequency  (Hz) INPUT OUTPUT Measured ...
<ul><li>Three assessments are made: </li></ul><ul><li>Inputs (Line voltage analysis) </li></ul><ul><li>Outputs (Motor curr...
Fault type is identified from frequency content Technology – Model Based Fault Detection Frequency  (Hz) Fault Identificat...
Extensive motor database is used to set threshold envelope for Current PSD at 8 standard deviations. Technology – Model Ba...
Technology – Model Based Fault Detection Frequency  (Hz) Fault Identification MODEL Frequency  (Hz) INPUT OUTPUT Measured ...
PSD Analysis <ul><li>In motor current spectral analysis, faults which cause dynamic change in air-gap create a frequency m...
PSD Analysis <ul><li>AnomAlert uses the residual PSD spectrum for high resolution detection of potential problems. </li></...
PSD Analysis <ul><li>Frequency bands are automatically generated to match known common fault characteristics, with thresho...
MONITOR OK WATCH LINE  (  Supply voltage problem  ) Temporary changes in supply voltage cause this alarm. If alarm is pers...
How AnomAlert Works 1. Install & Commission 2. Train – 10 days 3. Run Motor acts  as a sensor AnomAlert automatically buil...
AnomAlert  Clustering Algorithm Power   Factor Frequency C1 Motor Operating Curve Gain ( A/V ) <ul><li>During the learning...
AnomAlert  Clustering Algorithm Power   Factor Frequency C1 C2 C3 Motor Operating Curve Gain ( A/V ) <ul><li>During the le...
AnomAlert  Clustering Algorithm Power   Factor Frequency C1 C2 C3 C4 Motor Operating Curve Gain ( A/V ) <ul><li>During the...
AnomAlert  Clustering Algorithm Power   Factor Frequency C1 C2 C3 C4 Motor Operating Curve Gain ( A/V ) <ul><li>AnomAlert ...
The P-F Interval – Motor  Mechanical  Failures  Audible noise 1-4 weeks Heat by touch 1-5 days P1 P2 P5 P6 F Lube Analysis...
Value of AnomAlert ? Maintenance Planning Typical 75kW motor uses over US$50,000 in electricity annually, of which up to 5...
Good-fit Applications for AnomAlert Inaccessible Machines While large motors are typically already well instrumented, medi...
Medium Voltage – above 500V Low Voltage – up to 500V Inverter – Low Voltage AnomAlert Model Types Inverter – Medium Voltag...
AnomAlert  Connection Diagram
AnomAlert Architecture RS 485 RS 485 Ethernet Media Converter Typical arrangement is RS485 multidrop with media converter ...
AnomAlert  Communication Terminating Resistors
Conclusion <ul><li>AnomAlert enables maintenance planning to manage motor faults as well as the driven machine. </li></ul>...
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AnomAlert Motor Monitoring

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Model-based motor monitoring from Bently Nevada

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AnomAlert Motor Monitoring

  1. 1. Bently Nevada AnomAlert TM Motor Anomaly Detector
  2. 2. Motor Failure modes Typical distribution of motor failure modes. Motors up to 4Kv <ul><li>MECHANICAL FAULTS </li></ul><ul><li>Bearings </li></ul><ul><ul><ul><li>Contamination </li></ul></ul></ul><ul><ul><ul><li>Stress, Load, Fatigue </li></ul></ul></ul><ul><ul><ul><li>Vibration </li></ul></ul></ul><ul><ul><ul><li>Misalignment </li></ul></ul></ul><ul><ul><ul><li>Heat </li></ul></ul></ul><ul><ul><ul><li>Lubrication </li></ul></ul></ul><ul><ul><ul><li>Electrical discharge </li></ul></ul></ul><ul><li>Rotor </li></ul><ul><ul><ul><li>Mass unbalance </li></ul></ul></ul><ul><ul><ul><li>Rotor bow </li></ul></ul></ul><ul><ul><ul><li>Uneven cooling </li></ul></ul></ul><ul><li>External Misalignment </li></ul><ul><ul><ul><li>Foundation crack </li></ul></ul></ul><ul><ul><ul><li>Grouting degradation </li></ul></ul></ul><ul><ul><ul><li>Wrong thermal offset </li></ul></ul></ul><ul><li>ELECTRICAL FAULTS </li></ul><ul><li>Electrical Unbalance </li></ul><ul><ul><ul><li>Voltage unbalance </li></ul></ul></ul><ul><ul><ul><li>Rotor bar failure </li></ul></ul></ul><ul><li>Stator Problems: </li></ul><ul><ul><ul><li>Loose Iron </li></ul></ul></ul><ul><ul><ul><li>Stator Eccentricity </li></ul></ul></ul><ul><ul><ul><li>Shorted Turns </li></ul></ul></ul><ul><li>Windings </li></ul><ul><ul><ul><li>Heat </li></ul></ul></ul><ul><ul><ul><li>Inverters </li></ul></ul></ul><ul><ul><ul><li>Supply Voltage problems </li></ul></ul></ul><ul><ul><ul><li>Load </li></ul></ul></ul><ul><ul><ul><li>Contamination </li></ul></ul></ul><ul><li>Rotor Problems: </li></ul><ul><ul><ul><li>Broken/Cracked Rotor Bars </li></ul></ul></ul><ul><ul><ul><li>Loose Rotor Bars </li></ul></ul></ul><ul><ul><ul><li>Eccentric Rotor </li></ul></ul></ul>Roller Bearing Failures 51% Stator Failures 25% Rotor Failures 6% Others 18%
  3. 3. Condition Monitoring Methodologies <ul><li>Vibration </li></ul><ul><li>Temperature </li></ul><ul><li>Motor circuit analysis </li></ul><ul><ul><li>current </li></ul></ul><ul><ul><li>voltage </li></ul></ul><ul><li>Thermography </li></ul><ul><li>Ultrasound </li></ul><ul><li>Partial Discharge </li></ul><ul><li>Lubrication analysis </li></ul><ul><li>Insulation Resistance Testing </li></ul>
  4. 4. Condition Monitoring Methodologies <ul><li>Vibration </li></ul><ul><li>Temperature </li></ul><ul><li>Motor circuit analysis </li></ul><ul><ul><li>current </li></ul></ul><ul><ul><li>voltage </li></ul></ul><ul><li>Thermography </li></ul><ul><li>Ultrasound </li></ul><ul><li>Partial Discharge </li></ul><ul><li>Lubrication analysis </li></ul><ul><li>Insulation Resistance Testing </li></ul>AnomAlert provides one technology that detects anomalies and the cause.
  5. 5. Technology – Model Based Fault Detection Compares ACTUAL motor behavior with PREDICTED behavior to detect Anomalies and diagnose type of fault. Measured Current Voltage Σ Predicted Current Diff + - MOTOR AnomAlert
  6. 6. The inputs and outputs of the system are treated as complex dynamic signals Technology – Model Based Fault Detection MODEL Frequency (Hz) INPUT OUTPUT Measured Current Voltage Σ Predicted Current Diff + - MOTOR AnomAlert
  7. 7. Technology – Model Based Fault Detection Frequency (Hz) Fault Identification MODEL Frequency (Hz) INPUT OUTPUT Measured Current Voltage Σ Predicted Current Diff + - MOTOR AnomAlert
  8. 8. <ul><li>Three assessments are made: </li></ul><ul><li>Inputs (Line voltage analysis) </li></ul><ul><li>Outputs (Motor current, Power factor) </li></ul><ul><li>Power Spectral Density difference. </li></ul>Technology – Model Based Fault Detection Frequency (Hz) Fault Identification MODEL Frequency (Hz) INPUT OUTPUT Measured Current Voltage Σ Predicted Current Diff + - MOTOR AnomAlert
  9. 9. Fault type is identified from frequency content Technology – Model Based Fault Detection Frequency (Hz) Fault Identification MODEL Frequency (Hz) INPUT OUTPUT Measured Current Voltage Σ Predicted Current Diff + - MOTOR AnomAlert
  10. 10. Extensive motor database is used to set threshold envelope for Current PSD at 8 standard deviations. Technology – Model Based Fault Detection Frequency (Hz) Fault Identification MODEL Frequency (Hz) INPUT OUTPUT Measured Current Voltage Σ Predicted Current Diff + - MOTOR AnomAlert
  11. 11. Technology – Model Based Fault Detection Frequency (Hz) Fault Identification MODEL Frequency (Hz) INPUT OUTPUT Measured Current Voltage Σ Predicted Current Diff + - MOTOR AnomAlert Threshold Overlay on PSD Plot
  12. 12. PSD Analysis <ul><li>In motor current spectral analysis, faults which cause dynamic change in air-gap create a frequency modulation to the line frequency. Other faults generate unique frequency symptoms </li></ul>
  13. 13. PSD Analysis <ul><li>AnomAlert uses the residual PSD spectrum for high resolution detection of potential problems. </li></ul>Line Frequency
  14. 14. PSD Analysis <ul><li>Frequency bands are automatically generated to match known common fault characteristics, with threshold values in each band set by historical (empirical) database. </li></ul>M1 : Looseness M2 : Unbalance, Misalignment, Transmission Elements M3 : Rotor Fault M5 : Stator Fault M4 : Unbalance, Misalignment, Transmission Elements M6 : Bearing Fault M8 : Bearing Fault M10 : Bearing Fault M9 : Other Fault M7 : Other Fault M11 : Other Fault M12 : Other Fault Line Frequency
  15. 15. MONITOR OK WATCH LINE ( Supply voltage problem ) Temporary changes in supply voltage cause this alarm. If alarm is persistent check ; harmonic levels – capacitor - isolation of cables- motor connector or terminal slackness -contacts of the contactor WATCH LOAD ( Changes in process is observed ) If process is not altered deliberately, check; leakages – valve & vane misadjustments - Pressure gauge fault – Manometer – filters getting dirty (fans, compressors) Examine 1 ( First level alarm ) Maintenance should be scheduled. Check imbalance – misalignment – bearing/ bearing housing – motor shaft - broken rotor bar - isolation of stator windings- over lubrication and lubrication leakages through oil belt Driven equipment mechanical problems (gear box, compressor, fan blades, pump seals, conveyor chain tension problem -.....etc Examine 2 ( Second level alarm ) After this alarm, maintenance action is required. MONITOR Motor status display
  16. 16. How AnomAlert Works 1. Install & Commission 2. Train – 10 days 3. Run Motor acts as a sensor AnomAlert automatically build a mathematical models of the motor, which describe the electromechanical behavior of the motor-driven system. Electrical and Mechanical anomalies automatically detected
  17. 17. AnomAlert Clustering Algorithm Power Factor Frequency C1 Motor Operating Curve Gain ( A/V ) <ul><li>During the learning period AnomAlert treats each operating point of the motor as a cluster in the three dimensional space (powerfactor, gain, supply frequency). </li></ul>
  18. 18. AnomAlert Clustering Algorithm Power Factor Frequency C1 C2 C3 Motor Operating Curve Gain ( A/V ) <ul><li>During the learning period AnomAlert treats each operating point of the motor as a cluster in the three dimensional space (powerfactor, gain, supply frequency). </li></ul>
  19. 19. AnomAlert Clustering Algorithm Power Factor Frequency C1 C2 C3 C4 Motor Operating Curve Gain ( A/V ) <ul><li>During the learning period AnomAlert treats each operating point of the motor as a cluster in the three dimensional space (powerfactor, gain, supply frequency). </li></ul>
  20. 20. AnomAlert Clustering Algorithm Power Factor Frequency C1 C2 C3 C4 Motor Operating Curve Gain ( A/V ) <ul><li>AnomAlert continuously compare real data with the clusters already defined during learning, any value out of the cluster will drive an error event. </li></ul>
  21. 21. The P-F Interval – Motor Mechanical Failures Audible noise 1-4 weeks Heat by touch 1-5 days P1 P2 P5 P6 F Lube Analysis 1-6 months P3 P = Potential Failure First indication that a functional failure is occurring, or is about to. F = Functional Failure The point at which the asset fails to deliver to it’s intended purpose Motor portable CM technology 4-8 weeks P4 P7 Condition Time (not linear scale) P Protection Relays Vibration 1-9 months Electrical / Mechanical Anomaly Modeling. 2 – 3 months AnomAlert – Motor Anomaly Detection IR Thermography 6-8 weeks
  22. 22. Value of AnomAlert ? Maintenance Planning Typical 75kW motor uses over US$50,000 in electricity annually, of which up to 5% may be saved by correcting motor defects, unbalance, misalignment, etc.. AnomAlert identifies motor and mechanical load anomalies, supporting an efficiency entitlement analysis where power efficiency improvements can be tracked. Efficiency Optimization AnomAlert can replace some PM inspection tasks and minimize the need for intrusive inspections, increasing availability. Unplanned downtime is reduced with accurate detection and monitoring of motor anomalies not well addressed with conventional PdM techniques.
  23. 23. Good-fit Applications for AnomAlert Inaccessible Machines While large motors are typically already well instrumented, medium and smaller motors are very well matched to the classes of anomalies detected by AnomAlert. Motors below 4kV AnomAlert uses the motor as a “transducer”, responding to anything that causes dynamic changes in the air-gap, including both motor problems and problems with the driven machine. For Submerged pumps and Cryogenic pump applications, which are inaccessible and hostile to instrumentation, AnomAlert is an ideal monitoring solution. The failure modes typically seen on belt-driven, step-down gearbox or directly coupled medium and smaller motor driven machines are well matched to the detection techniques used by AnomAlert. Belt-driven machines, and step-down gearboxes
  24. 24. Medium Voltage – above 500V Low Voltage – up to 500V Inverter – Low Voltage AnomAlert Model Types Inverter – Medium Voltage Measurement CTs required, but voltage can be a direct connection to the monitor Measurement CTs and Voltage PT are usually already installed. Connect to the extra secondary winding. Hall Effect Current sensors need to be fitted. Voltage can be directly connected to the monitor. Hall Effect Current sensors and Voltage PT need to be fitted. 3 X 3 X Power supply CT Hall Effect Sensor 3 X CT PT 3 X PT Hall Effect Sensor
  25. 25. AnomAlert Connection Diagram
  26. 26. AnomAlert Architecture RS 485 RS 485 Ethernet Media Converter Typical arrangement is RS485 multidrop with media converter connection to monitoring software.
  27. 27. AnomAlert Communication Terminating Resistors
  28. 28. Conclusion <ul><li>AnomAlert enables maintenance planning to manage motor faults as well as the driven machine. </li></ul><ul><li>Energy Efficiency entitlement, and change in driven load can be identified and tracked as a key deliverable of this solution </li></ul><ul><li>It is a complement to other CM technologies to monitor the status of the motor using it as a sensor, with no added instrumentation. It can be installed in the MCC or near to it. </li></ul><ul><li>Traffic light display for alert in the field and different levels of notification in the System 1 to provide a quick overview of the motor status. </li></ul><ul><li>All the information can be use in ruledesk to have automatic diagnostic capability as any other system integrated to System1 </li></ul>
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