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20181213 Fleet based analytics - industrial case 3 sirris

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20181213 Fleet based analytics - industrial case 3 sirris

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20181213 Fleet based analytics - industrial case 3 sirris

  1. 1. A portal for fleet-based data-driven analysis Dr. Henrique Cabral, Dr. Tom Tourwé
  2. 2. A lightweight portal that allows you to apply our toolbox of fleet-aware algorithms on your own datasets Any party that is operating a fleet of assets and that wants initial insights into its operational behaviour Time-series data covering a meaningful period of time for a meaningful number of assets An automatically-generated report containing a well- documenteded data analysis of your fleet data WHAT? FOR WHO? OUTPUT? REQUIREMENTS
  3. 3. Do my assets behave as expected? Is the fleet behaving consistently? How do individual assets behave? How do exogenous factors affect behaviour? How are assets related to each other? What is the quality of my fleet data? FLEET-BASED PORTAL
  4. 4. 1. What is the quality of my fleet data? § How complete is the data? § Does the data contain outliers? § How is the data distributed? Outliers can be treated as missing data and imputed in 2 ways Timeseries- based imputation Fleet-based imputation Number of outliers per asset per day Date
  5. 5. 2. How do individual assets behave? § Which assets have the highest output? § Do all assets contribute equally to fleet performance? Relative daily asset contribution to fleet output #ofdays Spot periods of asset down-time 0.4 0 Relativecontribution Date Identify assets that consistently have a higher share of fleet output (e.g. asset R80790)
  6. 6. 3. How are assets related to each other? § Are there groups of assets with similar behaviour? § How can we characterize the behaviour of the different groups? • Group covariant assets and identify functional clusters of assets in fleet • Use this clustering information to fine- tune advanced analysis, such as remaining useful lifetime estimation or operational optimization Asset Asset
  7. 7. 4. How do exogenous factors affect behaviour? § How can we characterize the behaviour in terms of exogenous factors? § Which exogenous factors have the strongest impact on fleet behaviour? § Which exogenous factors are most prevalent across the fleet? Hypercube ID Poweroutput#occurrences Scaledexogenousfactor value Hypercubes • Identify most occurring exogenous factors combination • Determine which factors have a largest impact on fleet output
  8. 8. 5. Is the fleet behaving consistently? § How are individual assets behaving with respect to overall fleet behaviour? § Can we identify cases requiring further analysis? Correlation Date Asset-to-fleet correlation • Best suited for co-located assets exposed to similar conditions • Identify days of abnormal behavior Asset output
  9. 9. 5. Is the fleet behaving consistently? § How are individual assets behaving with respect to overall fleet behaviour? § Can we identify cases requiring further analysis? Averageoutput Hypercube ID Well adapted to non-colocated assets, exposed to different conditions
  10. 10. 6. Do my assets behave as expected? § Can we identify anomalous behaviour? § What is the estimated performance loss due to underperformance? Assetoutput Date detected anomaly: asset R80790 was underperforming over a period of 1.2 hours • Detect anomalous behavior based on expected output • Tune detection to events of specific duration and/or magnitude Expected output range
  11. 11. Summary: a lightweight portal that § allows you to apply our toolbox of state-of-the-art fleet-aware algorithms on your own datasets § is domain agnostic, i.e. independent of the type of assets under consideration § provides insights into asset-level and fleet-level behaviour § is validated on different industrial datasets Launch: beginning of 2019, together with the EluciDATA community
  12. 12. henrique.cabral@sirris.be elucidatalab.sirris.be

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