Modeling Condition And Performance
Of Mining Equipment
Tad S. Golosinski and Hui Hu
Mining Engineering
University of
Missouri-Rolla
1
Condition and Performance Monitoring
Systems

Machine health monitoring

Payload and productivity
• Allows for quick diagnostics of problems
• Provides management with machine and fleet
performance data

Warning system
• Alerts operator of problems, reducing the risk
of catastrophic failure
2
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
•
Hydraulic shovels
•
Wheel loaders
• 785, 789, 793, 797
• 5130, 5230
• 994, 992G (optional)
3
Other, Similar Systems




Cummins
•
CENSE (Engine Module)
Euclid-Hitachi
•
Contronics & Haultronics
Komatsu
•
VHMS (Vehicle Health Monitoring System)
LeTourneau
•
LINCS (LeTourneau Integrated Network Control System)
4
Round Mountain Gold Mine
Truck Fleet
17 CAT 785 (150t)
11 CAT 789B (190t)
PSA
(Product Support
Agreement) CAT
dealer guarantees
88% availability
5
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
6
CAT MineStar

CAT MineStar - Integrates …
• Machine Tracking System
•
•
•
(GPS)
Computer Aided Earthmoving System
(CAES)
Fleet scheduling System
(FleetCommander)
VIMS
7
Cummins Mining Gateway
Modem
MiningGateway.com
Cummins
Engine
Database
CENSE
Base
Station
RF Receiver
Modem
8
VIMS Data & Information Flow
VIMS
Legacy
Database
Mine
Site 1
Data Extract
Data Cleanup
Data Load
Mine
Site 2
VIMS Data
Warehouse
Mine
Site 3
Information
Extraction
Information
Apply
Data Mining
Tools
9
Earlier Research:
Data Mining of VIMS

Kaan Ataman tried modeling using:

Edwin Madiba tried modeling using:
• Major Factor Analysis
• Linear Regression Analysis
• All this on datalogger data
• Data formatting and transferring
• VIMS events association
• All this on datalogger and event data
10
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
11
Interactions
Data
Acquisition
Result
Interpretation
Data
Preparation
Data Mining
12
VIMS Features
Operator
Download
Sensors & Controls
Monitor & Store
•
•
•
•
•
•
•
Event list
Event recorder
Data logger
Trends
Cumulative data
Histograms
Payloads
Maintenance
Wireless Link
VIMS wireless
Management
13
Data Source
14
VIMS Statistical Data Warehouse
1-3 minute interval statistical data
•
•
•
•
•
•
•
•
•
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
15
VIMS Data Description



Six CAT 789B trucks
300 MB of VIMS data
79 “High Engine Speed” events
One-minute data statistics
Dataset
Training Set
Test set 1 (#1)
Test set 2 (#2)
Total
Count of Record
1870
86.4%
98
13.6%
196
2164
16
SPRINT -A Decision Tree Algorithm
IBM Almaden Research Center

GINI index for the split point
gini (s)  1   p
2
j
n1
n2
ginisplit ( s )  gini ( s1 )  gini ( s2 )
n
n


Strictly binary tree
Built-in v-fold cross validation
17
18
VIMS EVENT PREDICTION
Normal Engine Speed
High Engine Speed
Normal Engine Speed
Snapshot
VIMS
Data
0 0 0 0 0 0 1 2 3 4 5 6 0 0 0 0 0
High Eng
Event_ID
Predicted
Label
Other
Other
767_1
767_2
Eng_1
Eng_2
Other
Other
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
Total Errors = 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 Tree
Training 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 Tree
Test #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 Tree
Test #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 Tree
Training 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 Tree
Test #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
Backpropagation
A Neural Network Classification Algorithm
Node
Node Detail
x1
x2
x3
Input
Hidden
Layer
Out
Characteristic: Each output
corresponds to a possible classification.
w1
w2
f(z)
w3
z = S iw ix i
Some choices for F(z):
f(z) = 1 / [1+e-z] (sigmoid)
f(z) = (1-e-2z) / (1+e-2z) (tanh)
33
Minimize the Sum of Squares
SSQ Error Function
1 m
E   ( t k  yk ) 2
2 k 1
1 m
min E   ( t k  yk )2
2 k 1
yk (output) is a function of
the weights wj,k.
tk is the true value.
EW j ,k
Freeman & Skapura, Neural Networks,
Addison Wesley, 1992
E

and solve EW j ,k  0 for W j,k
W j ,k
In the graph:
• Ep is the sum of
squares error
• Ep is the gradient,
(direction of maximum
function increase)
34
More
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 twominute 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
•

Intelligent Miner-Data Mining Application
for Modeling VIMS Condition Monitoring
Data
•

Tad Golosinski & Hui Hu, Mineral Resources
Engineering, 2002 (?)
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
39
40