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Modeling Condition And Performance Of Mining Equipment Tad S. Golosinski and Hui Hu

Modeling Condition And Performance Of Mining Equipment Tad S. Golosinski and Hui Hu. Mining Engineering University of Missouri-Rolla. Condition and Performance Monitoring Systems. Machine health monitoring Allows for quick diagnostics of problems Payload and productivity

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Modeling Condition And Performance Of Mining Equipment Tad S. Golosinski and Hui Hu

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  1. Modeling Condition And Performance Of Mining EquipmentTad S. Golosinski and Hui Hu Mining Engineering University of Missouri-Rolla

  2. Condition and Performance MonitoringSystems • Machine health monitoring • Allows for quick diagnostics of problems • Payload and productivity • Provides management with machine and fleet performance data • Warning system • Alerts operator of problems, reducing the risk of catastrophic failure

  3. Collects / processes information on major machine components Engine control Transmission/chassis control Braking control Payload measurement system Installed on… Off-highway trucks 785, 789, 793, 797 Hydraulic shovels 5130, 5230 Wheel loaders 994, 992G (optional) CAT’s VIMS (Vital Information Management System)

  4. Other, Similar Systems • Cummins • CENSE (Engine Module) • Euclid-Hitachi • Contronics & Haultronics • Komatsu • VHMS (Vehicle Health Monitoring System) • LeTourneau • LINCS (LeTourneau Integrated Network Control System)

  5. Round Mountain Gold Mine • Truck Fleet • 17 CAT 785 (150t) • 11 CAT 789B (190t) • PSA • (Product Support Agreement)CAT dealer guarantees 88% availability

  6. VIMS in RMG Mine • Average availability is 93% over 70,000 operating hours • VIMS used to help with preventive maintenance • Diagnostics after engine failure • Haul road condition assessment • Other Holmes Safety Association Bulletin 1998

  7. CAT MineStar • CAT MineStar - Integrates … • Machine Tracking System (GPS) • Computer Aided Earthmoving System (CAES) • Fleet scheduling System (FleetCommander) • VIMS

  8. Cummins Mining Gateway Modem MiningGateway.com Database CENSE Base Station Cummins Engine RF Receiver Modem

  9. VIMS Data & Information Flow VIMS Legacy Database Mine Site 1 VIMS Data Warehouse DataExtract DataCleanup DataLoad Mine Site 2 Mine Site 3 Information Extraction Information Apply Data Mining Tools

  10. Earlier Research: Data Mining of VIMS • Kaan Ataman tried modeling using: • Major Factor Analysis • Linear Regression Analysis • All this on datalogger data • Edwin Madiba tried modeling using: • Data formatting and transferring • VIMS events association • All this on datalogger and event data

  11. Research Objectives • Build the VIMS data warehouse to facilitate the data mining • Develop the data mining application for knowledge discovery • Build the predictive models for prediction of equipment condition and performance

  12. Interactions Data Acquisition Data Preparation Result Interpretation Data Mining

  13. Operator Download Sensors&Controls Monitor&Store Maintenance Event list • Event recorder • Data logger • Trends • Wireless Link Cumulative data • Histograms • Management Payloads • VIMS wireless VIMS Features

  14. Data Source

  15. Minimum Maximum Average Data Range Variance Regression Intercept Regression Slope Regression SYY Standard Deviation VIMS Statistical Data Warehouse 1-3 minute interval statistical data

  16. VIMS Data Description • Six CAT 789B trucks • 300 MB of VIMS data • 79 “High Engine Speed” events One-minute data statistics

  17. SPRINT -A Decision Tree AlgorithmIBM Almaden Research Center • GINI index for the split point • Strictly binary tree • Built-in v-fold cross validation

  18. High Engine Speed Snapshot Normal Engine Speed Normal Engine Speed VIMS Data 0 0 0 0 0 0 1 2 3 4 5 6 0 0 0 0 0 High Eng 767_2 767_1 Event_ID Other Other Eng_1 Eng_2 Other Other Predicted Label VIMS EVENT PREDICTION

  19. “One-Minute” decision tree

  20. Decision Tree: Training on One-Minute Data Total Errors = 120 (6.734%) Predicted Class --> | Other | Eng1 | Eng3 | Eng2 | Eng4 | Eng6 | Eng5 | ---------------------------------------------------------------------------------------------------------------- Other | 1331 | 18 | 9 | 5 | 16 | 6 | 1 | total = 1386 Eng1 | 0 | 62 | 1 | 3 | 0 | 0 | 0 | total = 66 Eng3 | 0 | 11 | 51 | 2 | 2 | 1 | 0 | total = 67 Eng2 | 0 | 12 | 8 | 38 | 7 | 0 | 0 | total = 65 Eng4 | 0 | 3 | 7 | 2 | 55 | 0 | 1 | total = 68 Eng6 | 0 | 0 | 0 | 1 | 0 | 61 | 4 | total = 66 Eng5 | 0 | 0 | 0 | 0 | 0 | 0 | 64 | total = 64 -------------------------------------------------------------------------------------------------------------- 1331 | 106 | 76 | 51 | 80 | 68 | 70 | total = 1782

  21. Decision Tree: Test#1 on One-Minute Data Total Errors = 24 (24%) Predicted Class --> | Other | Eng1 | Eng3 | Eng2 | Eng4 | Eng6 | Eng5 | ----------------------------------------------------------------------------------------------------------- Other | 59 | 3 | 0 | 2 | 3 | 0 | 1 | total = 68 Eng1 | 4 | 1 | 0 | 1 | 0 | 0 | 0 | total = 6 Eng3 | 0 | 3 | 1 | 0 | 1 | 0 | 0 | total = 5 Eng2 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | total = 4 Eng4 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | total = 4 Eng6 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | total = 7 Eng5 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | total = 6 ----------------------------------------------------------------------------------------------------------- 65 | 9 | 2 | 5 | 5 | 7 | 7 | total = 100

  22. Decision Tree: Test#2 on One-Minute Data TotalErrors = 35 (17.86%) Predicted Class --> | Other | Eng1 | Eng3 | Eng2 | Eng4 | Eng6 | Eng5 | -------------------------------------------------------------------------------------------------------- Other | 141 | 9 | 2 | 4 | 4 | 0 | 0 | total = 160 Eng1 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | total = 6 Eng3 | 2 | 1 | 2 | 0 | 1 | 0 | 0 | total = 6 Eng2 | 2 | 1 | 2 | 1 | 0 | 0 | 0 | total = 6 Eng4 | 1 | 0 | 1 | 1 | 3 | 0 | 0 | total = 6 Eng6 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | total = 6 Eng5 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | total = 6 --------------------------------------------------------------------------------------------------------- 148 | 13 | 8 | 7 | 8 | 6 | 6 | total = 196

  23. “Two-Minute” decision tree

  24. Decision TreeTraining on Two-Minute Data Sets Total Errors = 51 (5.743%) Predicted Class --> | OTHER | ENG1 | ENG2 | ENG3 | --------------------------------------------------------------------- OTHER | 657 | 6 | 19 | 3 | total = 685 ENG1 | 0 | 62 | 10 | 0 | total = 72 ENG2 | 0 | 13 | 54 | 0 | total = 67 ENG3 | 0 | 0 | 0 | 64 | total = 64 --------------------------------------------------------------------- 657 | 81 | 83 | 67 | total = 888

  25. Decision TreeTest #1 on Two-Minute Data Total Errors = 14 (29.79%) Predicted Class --> | OTHER | ENG1 | ENG2 | ENG3 | --------------------------------------------------------------------- OTHER | 28 | 5 | 4 | 1 | total = 38 ENG1 | 1 | 0 | 0 | 0 | total = 1 ENG2 | 2 | 1 | 1 | 0 | total = 4 ENG3 | 0 | 0 | 0 | 4 | total = 4 --------------------------------------------------------------------- 31 | 6 | 5 | 5 | total = 47

  26. Decision TreeTest #2 on Two-Minute Data Total Errors = 15 (15.31%) Predicted Class --> | OTHER | ENG1 | ENG2 | ENG3 | --------------------------------------------------------------------- OTHER | 71 | 8 | 1 | 0 | total = 80 ENG1 | 3 | 3 | 0 | 0 | total = 6 ENG2 | 0 | 3 | 3 | 0 | total = 6 ENG3 | 0 | 0 | 0 | 6 | total = 6 --------------------------------------------------------------------- 74 | 14 | 4 | 6 | total = 98

  27. “Three-Minute” decision tree

  28. Decision TreeTraining on Three-Minute Data Total Errors = 28 (4.878%) Predicted Class --> | OTHER | ENG1 | ENG2 | ---------------------------------------------------- OTHER | 411 | 23 | 4 | total = 438 ENG1 | 1 | 65 | 0 | total = 66 ENG2 | 0 | 0 | 70 | total = 70 ---------------------------------------------------- 412 | 88 | 74 | total = 574

  29. Decision Tree Test #1 on Three-Minute Data Total Errors = 12 (19.05%) Predicted Class --> | OTHER | ENG1 | ENG2 | ---------------------------------------------------- OTHER | 42 | 9 | 0 | total = 51 ENG1 | 3 | 5 | 0 | total = 8 ENG2 | 0 | 0 | 4 | total = 4 ---------------------------------------------------- 45 | 14 | 4 | total = 63

  30. Decision TreeTest #2 on Three-Minute Data Total Errors = 9 (14.06%) Predicted Class --> | OTHER | ENG1 | ENG2 | ---------------------------------------------------- OTHER | 47 | 5 | 0 | total = 52 ENG1 | 4 | 2 | 0 | total = 6 ENG2 | 0 | 0 | 6 | total = 6 ---------------------------------------------------- 51 | 7 | 6 | total = 64

  31. Decision Tree Summary • “One-Minute model” needs more complex tree structure • “One-Minute model” gives low accuracy of predictions • “Three-Minute” decision tree model gives reasonable accuracy of predictions • Based on test #1 &#2 • Other - 13% error rate • Eng1 - 50% error rate • Eng2 – 0 error rate • Other approach?

  32. Node Detail x1 Node w1 w2 x2 f(z) w3 x3 z = Siwixi Backpropagation A Neural Network Classification Algorithm Input Hidden Layer Out Some choices for F(z): f(z) = 1 / [1+e-z] (sigmoid) f(z) = (1-e-2z) / (1+e-2z) (tanh) Characteristic: Each output corresponds to a possible classification.

  33. Freeman & Skapura, Neural Networks, Addison Wesley, 1992 Minimize the Sum of Squares SSQ Error Function yk (output) is a function of the weights wj,k. tk is the true value. • In the graph: • Ep is the sum of squares error • Ep is the gradient, (direction of maximum function increase) More

  34. Neural Network Modeling Results“Three-Minute training set”

  35. Neural Network Modeling Result“Three-Minute set”: test #1 and #2 Test #1 Test #2

  36. NN Summary • Insufficient data for one-minute and two-minute prediction models • Three-minute network shows better performance than the decision tree model: • Other - 17% error rate • Eng1 - 28% error rate • Eng2 - 20% error rate

  37. Conclusions • Predictive model can be built • Neural Network model is more accurate than the Decision Tree one • Based on all data • Overall accuracy is not sufficient for practical applications • More data is needed to train and test the models

  38. References • Failure Pattern Recognition of a Mining Truck with a Decision Tree Algorithm • Tad Golosinski & Hui Hu, Mineral Resources Engineering, 2002 (?) • Intelligent Miner-Data Mining Application for Modeling VIMS Condition Monitoring Data • Tad Golosinski and Hui Hu, ANNIE, 2001, St. Louis • Data Mining VIMS Data for Information on Truck Condition • Tad Golosinski and Hui Hu, APCOM 2001, Beijing, P.R. China

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