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Machine Learning for monitoring the condition of critical systems Ross W Dickie MacTaggart Scott | Heriot Watt University rwd2@hw.ac.uk, Ross.Dickie@mactag.com #UDT2019 Stand: A19 Project Outline Industry & Academic partnership

  1. Machine Learning for monitoring the condition of critical systems Ross W Dickie MacTaggart Scott | Heriot Watt University rwd2@hw.ac.uk, Ross.Dickie@mactag.com #UDT2019 Stand: A19

  2. Project Outline • Industry & Academic partnership project • Assessing the potential for use of Condition Monitoring (CM) data to improve asset performance • Provide decision support in the form of fault prediction and assessment tools • Primarily hydraulic assets and equipment #UDT2019 Stand: A19

  3. Aims & Motivations • Increase asset reliability & availability • Improve understanding of “real - world” asset usage • Improve information available for decision support • Improve quality of servicing and product support • Improve future designs #UDT2019 Stand: A19

  4. PROCESS #UDT2019 Stand: A19

  5. Process Outline #UDT2019 Stand: A19

  6. Learning Cases • Supervised learning requires historic data on fault and failures to learn data characteristics (data is labelled i.e. fault/no fault) • Unsupervised learning directly learns patterns in the data without apriori labelling • Anomaly Detection learning what is normal to provide information on deviation from the normal. #UDT2019 Stand: A19

  7. Learning Cases • In fault analysis the two cases are analogous to two broad cases in engineered equipment: 1. Low cost cheaply replaceable components/equipment can easily provide an extensive training set often through accelerated lifecycle testing or analytical modelling 2. Robust expensive equipment lacks fault/failure data due to costs in obtaining data and strict maintenance regimes mitigating faults #UDT2019 Stand: A19

  8. Learning from a Fault Dataset #UDT2019 Stand: A19

  9. Learning in Absence of Fault Dataset #UDT2019 Stand: A19

  10. Anomaly Detection • Identify common/ expected operational parameters • Extract data features from profiles using Expert Elicitation or Empirical Operating data • Anomaly detection uncovers deviations outside the expected operational space • Inclusion of classification also makes estimates of fault mode and/or mechanism #UDT2019 Stand: A19

  11. APPLICATION #UDT2019 Stand: A19

  12. Application to Machinery Vibration • Perform signal processing techniques Fast Fourier Transforms, Wavelets etc. • Monitor spectra across history of equipment • Automatically detect fault conditions • Improve historical record for specific asset to improve fault detection and diagnosis #UDT2019 Stand: A19

  13. Application to Machinery Vibration #UDT2019 Stand: A19

  14. Application to Machinery Vibration #UDT2019 Stand: A19

  15. Application to Machinery Vibration • Passively assess internal workings of machinery • Can be performed using relatively low cost accelerometers • Suited for regression, classification and anomaly detection • Central assumption increased vibration = decreased equipment quality #UDT2019 Stand: A19

  16. Application to Machinery Vibration Future Challenges • Adapt algorithm to account for operating conditions ( avoidance of false positives ) • Incorporation of advanced signal processing techniques for increased contextual inference (Wavelet transforms etc.) • Ensure robustness of algorithms to noise #UDT2019 Stand: A19

  17. BENEFITS #UDT2019 Stand: A19

  18. Operational Benefits • Live high resolution understanding of asset operation including reports of impending faults improves situational awareness • Leads to improved maintenance logistics by increasing maintenance horizon . • Improvements in operational planning based upon system state i.e. mission requirements can be compared with asset predicted capability • Reduction in manned maintenance inspections • Increased equipment availability #UDT2019 Stand: A19

  19. Operational Benefits • Increased “Mission Reliability” • Use of past data to understand how different scenarios affect reliability of assets • Use of data to model asset future operating scenarios • Safety improvements • Knowledge of impending faults impedes the development of safety critical failure situations • Provides visibility to hidden failures outside of routine inspection intervals #UDT2019 Stand: A19

  20. Manufacturer & Customer Benefits • High quality system state estimations enable improved contract and product support • Enhanced product support via asset data analysis • Improved product development and design driven by real world usage profiles and duty cycles • Enabling technology for Contracts for Availability (CfA) #UDT2019 Stand: A19

  21. Future Technical Benefits • Access to high resolution data history of equipment • Improved understanding of asset operational profiles i.e. system stresses under different operating modes • Incorporation with Integrated Platform Management Systems (IPMS) and Digital Twin technology • Use within automated & autonomous control systems to providing self-awareness element of mission planning/execution #UDT2019 Stand: A19

  22. Recapitulation • Many techniques exist to make use of large existing and potential datasets (sensor streams etc.) • Accessibility of techniques constantly improving. • Mission critical assets requires expert elicitation we cannot blindly trust “black - box” style prediction systems i.e. breaking down the walls of the black box. #UDT2019 Stand: A19

  23. Funding and Stakeholders #UDT2019 Stand: A19

  24. Questions? #UDT2019 Stand: A19

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