Datamining Presmmmmmmmmmmmmmmmmm

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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 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

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CAT’s VIMS (Vital Information Management System)

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)

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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 Control System)

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Round Mountain Gold Mine

Truck Fleet17 CAT 785 (150t)11 CAT 789B (190t)

PSA (Product Support Agreement) CAT dealer guarantees 88% availability

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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

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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

Cummins Engine

Base

Station

RF Receiver

Modem

Modem

CENSEDatabaseMiningGateway.com

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VIMS Data & Information Flow

VIMS Data Warehouse

Data ExtractData CleanupData Load

Data Mining

Tools

Information Extraction

Information Apply

Mine Site 1

Mine Site 2

Mine Site 3

VIMS Legacy Database

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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

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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

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Data Mining

Interactions

Result Interpretation

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

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

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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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19

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%)


-----------------------------------------------------------------------------------------------------------

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 Total Errors = 35 (17.86%)


--------------------------------------------------------------------------------------------------------

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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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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---------------------------------------------------------------------

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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---------------------------------------------------------------------

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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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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----------------------------------------------------

OTHER | 42 | 9 | 0 | total = 51

ENG1 | 3 | 5 | 0 | total = 8

ENG2 | 0 | 0 | 4 | total = 4

----------------------------------------------------

45 | 14 | 4 | total = 63

Decision Tree Test #1 on Three-Minute Data

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Decision TreeTest #2 on Three-Minute Data



----------------------------------------------------

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 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?

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Backpropagation A Neural Network Classification Algorithm

Input HiddenLayer

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

x3

w3

w2

w1

Node Detail

z = iwixi

Node

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m

kkk ytE

1

2)(21

min

m

kkk ytE

1

2)(21

yk (output) is a function of the 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 W 0 solve and

kjWkj

kjW EW

EE

In the graph:

• Ep is the sum of squares error

Ep is the gradient, (direction of maximum function 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 better performance than the decision tree model:

• Other - 17% 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 the models

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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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Datamining Presmmmmmmmmmmmmmmmmm