Condition Monitoring Architecture To Reduce Total Cost of Ownership
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The document proposes a condition monitoring architecture to reduce the total cost of ownership through lower hardware costs, improved software and support, and optimized IT infrastructure. It suggests using lower-cost MEMS accelerometers with embedded processing and digital signaling. Advanced signal processing like time synchronous averaging would extract fault indicators without spectra. A health indicator mapping would automate fusion of fault modes into a single metric requiring little interpretation. Cloud computing could replace local servers for simplified maintenance and data pooling across similar assets.
Condition Monitoring Architecture To Reduce Total Cost of Ownership
- 1. Condition Monitoring Architecture To Reduce Total Cost of Ownership Eric Bechhoefer, NRG Systems Brogan Morton, NRG Systems
- 2. Barriers to Sales of PHM Systems • CBM/PHM System are Proven to Work – Low Penetration into Commercial Markets – Example: 3% of Wind Turbines • Why? - Business Case is Hard to Make – Safety not the primary concern, cost avoidance is – Hard to Quantify Benefit • Change Architecture to Improve Value – Lower “Costs” and Better Information
- 3. Current System Architecture • System Hardware – 6 to 8 PZT Accelerometers • 5% Accuracy, .5 to 10,000 Hz – Tachometer – Signal Conditioning • 6 to 12 channels • Sample Rate: 60 to 80 KSPS • Support/Monitoring Services – Human in the loop to turn data into a diagnosis – $1,000 to $1,500 per year per turbine • IT Infrastructure – Data hosting on local server – Data also shipped to centralized analysis center
- 4. System Layout: Wind Turbines
- 5. From a System Perspective… • How to Lower Total Ownership Cost – Hardware Considerations • Costs driven by accelerometer – Software/Support Considerations • Costs driven by knowledge creation (data to diagnosis) – IT Infrastructure Considerations • Cost driven by local data storage and associated maintenance
- 6. Accelerometers – MEMS vs. PZT MEMS Advantages MEMS Disadvantages • Cost • Needs to be Packaged – $6 to $30 vs. $100’s – No Trivial Task • Bandwidth • Noisier – 0 to 32,000 Hz vs. 0.5 to – PDS is 2 to 40x higher 10,000 Hz System Issues • Accuracy • 4 Wire? – Typically 1% vs. 5% or 10% – Power/Signal Error • Local Conversion? • Self Test – ADC, then Microcontroller – Can Enable BIT vs. No BIT and RT
- 7. Sensor System Considerations • Low cost target – move to MEMS – Analog vs. Digital Sensing • If Digital – Local ADC, • EMI is Reduced – Microcontroller, RAM, Receiver/Transmitter • If Multi-Drop: RS-485 – If Microcontroller: Local Processing? • Many Smaller, Cheap Processors vs. One Larger Processor • Low cost packaging – Alternative to Stainless Steel or Titanium – Transfer Function – Has to Be Stiff/Light
- 8. MEMS: A Sensor Solutions • Noise Was Not An Issue – After Signal Processing, Noise was Negligible • Conductive Plastic Package – 40% Mass of Stainless – Similar Stiffness 1000 mv/g vs. 70 mv/g – 12% of Cost of Stainless MEMS Accel, 0.25 Hz – 6.5KHz Resonance, Flat Within 2% of Low G Accel Response to 17 KHz
- 9. Embedded PHM • Micro with FPU Support – 32MB RAM – 24 Bit ADC – Sample @ 300-100,000 kbps – R/T > 500 KB/S • Local Vibe Processing – Time Synchronous Average (TSA) – FFT/IFFT – Hilbert Transform • Total Cost: Similar to PZT Accel
- 10. Software & Support Considerations • Algorithmic – Digital signal processing of the vibration signals for fault detection • Knowledge Creation – Goal: Actionable information requiring little interpretation
- 11. Typical Drivetrain Configuration Generator High Speed Shaft 3-stage Gearbox Main Bearing Int. Speed Shaft Main Shaft Low Speed Shaft •17 Bearings •9 Gears •8 Shaft
- 12. Algorithmic • Process vibration signal into indications of faults – Data reduction without loss of information • No Spectrums/Order Analysis – Configurable Analysis for Shafts, Gears and Bearings, – Several Condition Indicators for Each Component • Use Time Synchronous Average (TSA)
- 13. Why This Approach • Large Variation in Wind Speeds Cause Large Changes in Rotor Speed • 3/Rev Torque/Speed Ripple From Tower Shadow/Wind Shear • Gearbox has many gear meshes; isolate gears of interest
- 14. Example of Spectrum Vs. TSA • Due to Changes in Rotor Speed, Order Analysis or the PSD Cause Smearing of Frequency Content • Example Main Rotor Shaft 1st, 2nd, and 3rd Harmonics of Ring Gear Frequency
- 15. The TSA •Use Tachometer as Phase Reference on Shaft •Reduces Non-Synchronous Noise 1/sqrt(revolutions) •For Each Revolution (From Tach) •Resample length m = 2^Ceiling(log2(number of points in Rev))
- 16. Gear Fault Indicators • No Single CI Works With All Fault Modes – Surface Disturbance, Scuffing, Deformation, Surface Fatigue, Cracks, Tooth Breakage, Eccentricity • Use a Number of Analysis to Cover All Fault Modes – Residual Analysis, Energy Operator, Narrow Band Analysis, Amplitude Modulation Analysis, Frequency Modulation Analysis.
- 17. Gear Analysis
- 18. Knowledge Creation • Recall Goal: Create actionable information requiring little interpretation – Convey what to fix and when • Single Health Indicator for Each Component – Fusion of different condition indicators – Common scale for every component (0-1)
- 19. Health as a Function of Distributions • HI Paradigm: Map the CIs into an HI – HI Ranges from 0 to 1, Where the Probability of the HI exceeding 0.5 is the PFA – HI in Warning when between 0.75 and 1 – HI is Alarm when Greater than 1.0 – Continued Operations with HI > 1 could Cause Collateral Damage
- 20. Controlling Correlation Between CIs • All CIs have PDFs CI 1 CI 2 CI 3 CI 4 CI 5 CI 6 • Any Operation on the CI to ij CI 1 1 0.84 0.79 0.66 -0.47 0.74 form an HI is a Function of CI 2 1 0.46 0.27 -0.59 0.36 Distributions CI 3 1 0.96 -0.03 0.97 – Max of n CI (an Order Statistics) CI 4 1 0.11 0.98 – Sum of n CI CI 5 1 0.05 – Norm of n CI CI 6 1 • Function of Distribution – PFA Correct if Distribution are IID – Need to Whiten
- 21. CI to HI Mapping • Six CIs used in HI Calculation – Residual RMS – Energy Operator RMS – FM0 – Narrowband Kurtosis – AM Kurtosis – FM RMS • Statistics Generated from 4 test articles: 100 samples prior to fault propagation
- 22. IT Infrastructure Alternate to Local Server: Cloud Computing For Owner/Operator For CMS Developer • No Seat License of the CMS • Simplifies Software Maintenance Database Cost • No Local Servers to Host – Only one Platform to Develop and Test to, Data – Only one Platform to Deploy • Management of Software Software Updates/Patches to, Maintenance. • Reduces the Cost of Certification • Allows Pooling of Dataset of – Configuration Management is Similar Type/Model Greatly Simplified Turbines without Risk of • Scalability Exposing Proprietary Information
- 23. Conclusion • Significant value can be created by redesigning system architecture – Vibration sensing • Non-traditional sensor, new packaging and design methods – Advanced signal processing techniques • Increased sensitivity to faults under dynamic conditions – Knowledge Creation • Automated fusion of fault modes • Actionable information with diagnostic support – IT Infrastructure • Economy of scale using cloud services