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Modeling Condition And Performance

Of Mining Equipment

Tad S. Golosinski and Hui Hu

Mining Engineering

University of

Missouri-Rolla

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Condition and Performance Monitoring

Systems

Machine health monitoring

Allows for quick diagnostics of problems Payload and productivity

Provides management with machine and fleetperformance data

Warning systemAlerts operator of problems, reducing the risk

of catastrophic failure

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CATs VIMS(Vital Information Management System)

Collects / processesinformation on majormachine components

Engine control Transmission/chassis

control

Braking control

Payload measurementsystem

Installed on Off-highway trucks

785, 789, 793, 797

Hydraulic shovels 5130, 5230 Wheel loaders

994, 992G (optional)

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Other, Similar Systems

Cummins CENSE (Engine Module)

Euclid-Hitachi

Contronics & Haultronics Komatsu

VHMS (Vehicle Health Monitoring System) LeTourneau

LINCS (LeTourneau Integrated Network ControlSystem)

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Round Mountain Gold Mine

Truck Fleet17 CAT 785 (150t)

11 CAT 789B (190t)PSA

(Product Support

Agreement)CATdealer guarantees88% availability

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VIMS in RMG Mine

Average availability is 93%

over 70,000 operating hours

VIMS used to help withpreventive maintenance

Diagnostics after engine failure

Haul road condition assessment Other

Holmes Safety Association Bulletin 1998

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CAT MineStar

CAT MineStar - Integrates

Machine Tracking System(GPS)

Computer Aided Earthmoving System(CAES)

Fleet scheduling System(FleetCommander)

VIMS

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Cummins Mining Gateway

CumminsEngine

Base

Station

RF Receiver Modem

Modem

CENSEDatabaseMiningGateway.com

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VIMS Data & Information Flow

VIMS Data

Warehouse

DataExtract

DataCleanupDataLoad

Data Mining

Tools

Information

Extraction

InformationApply

MineSite 1

Mine

Site 2

MineSite 3

VIMSLegacyDatabase

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Earlier Research:Data Mining of VIMS

Kaan Atamantried modeling using: Major Factor Analysis

Linear Regression AnalysisAll this on datalogger data Edwin Madibatried modeling using:

Data formatting and transferring

VIMS events associationAll this on datalogger and event data

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Research Objectives

Build the VIMS data warehouse to

facilitate the data mining

Develop the data mining application forknowledge discovery

Build the predictive models for prediction

of equipment condition and performance

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Data Mining

Interactions

ResultInterpretation

Data

Preparation

Data

Acquisition

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VIMS Features

Sensors&ControlsMonitor&Store

Event list Event recorder Data logger Trends Cumulative data Histograms Payloads

Wireless Link

Maintenance

Management

Download

Operator

VIMS wireless

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Data Source

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VIMS Statistical Data Warehouse

Minimum Maximum Average Data Range Variance

Regression Intercept Regression Slope Regression SYY Standard Deviation

EVENT ID TC_OUT_TEMP_AVG TC_OUT_TEMP_MAX TC_OUT_TEMP_MIN TC_OUT_TEMP_RANGE

0_6 70.35 73 65 8

0_7 64.95 66 64 2

0_8 65.67 66 65 1

0_9 66.30 67 66 1

767_1 80.00 80 80 0

767_2 80.37 81 80 1

767_3 80.95 81 80 1

767_4 81.32 82 81 1

767_5 81.83 82 81 1

767_6 83.43 87 82 5

1-3 minute interval statistical data

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VIMS Data Description

Six CAT 789B trucks

300 MB of VIMS data

79 High Engine Speed events

One-minute data statistics

Dataset Count of Record

Training Set 1870 86.4%Test set 1 (#1) 98

Test set 2 (#2) 19613.6%

Total 2164

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SPRINT -A Decision Tree Algorithm

IBM Almaden Research Center

GINI index for the split point

Strictly binary tree

Built-in v-fold cross validation

21)( jpsgini

)()()( 22

11 sgini

n

nsgini

n

nsginisplit

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00000654321000000

High Engine Speed

Snapshot

Normal Engine Speed Normal Engine Speed

High Eng

767_1 767_2

Eng_1 Eng_2Other Other OtherOther

VIMS

Data

Predicted

Label

Event_ID

VIMS EVENT PREDICTION

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One-Minute

decision tree

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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

Decision Tree: Training on One-Minute Data

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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

Decision Tree: Test#1 on One-Minute Data

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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

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Two-Minute

decision tree

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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

Decision TreeTraining on Two-Minute Data Sets

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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

Decision TreeTest #1 on Two-Minute Data

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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

Decision TreeTest #2 on Two-Minute Data

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Three-Minute

decision tree

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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

Decision TreeTraining on Three-Minute Data

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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

Decision TreeTest #1 on Three-Minute Data

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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

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Decision Tree Summary

One-Minute model needs more complex treestructure

One-Minute model gives low accuracy of

predictions Three-Minute decision tree model gives

reasonable accuracy of predictions Based on test #1

Other - 13% error rate Eng1 - 50% error rate Eng2 0 error rate

Other approach?

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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.

f(z)

x1

x2

x3w3

w2

w1

Node Detail

z = Siwixi

Node

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mk kk

ytE1

2)(

2

1min

mk

kk ytE1

2)(

2

1

yk (output) is a function ofthe weights wj,k.tk is the true value.

SSQ Error Function

Freeman & Skapura, Neural Networks,Addison Wesley, 1992

Minimize the Sum of Squares

kj ,,

,

,for W0solveand

kjW

kj

kjWE

W

EE

In the graph:

Ep is the sum ofsquares error

Ep is the gradient,(direction of maximumfunction increase)

More

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Neural Network Modeling Results

Three-Minute training set

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Neural Network Modeling Result

Three-Minute set: test #1 and #2

Test #1

Test #2

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NN Summary

Insufficient data for one-minute and two-

minute prediction models

Three-minute network shows betterperformance than the decision tree

model: Other - 17% error rate Eng1 - 28% error rate Eng2 - 20% error rate

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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 themodels

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References

Failure Pattern Recognition of a MiningTruck with a Decision Tree Algorithm Tad Golosinski & Hui Hu, Mineral Resources

Engineering, 2002 (?)

Intelligent Miner-Data Mining Applicationfor Modeling VIMS Condition MonitoringData Tad Golosinski and Hui Hu, ANNIE, 2001, St. Louis

Data Mining VIMS Data for Information onTruck Condition Tad Golosinski and Hui Hu, APCOM 2001, Beijing,

P.R. China

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