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Safety and Health 


RI _9_6.;.....;...5_8~____---:RE~PO~R::-:::T~O~F-=INVE"'=='s=T~IG~A~T~IO~N~s/2~O~02 

Drill Monitor With Strata Strength 


Classification in Near-Real Time 


Department of Health and Human Services 

Centers for Disease Control and Prevention 

National Institute for Occupational Safety and Health m,OSH
Report of Investigations 9658 

Drill Monitor With Strata Strength Classification 

in Near-Real Time 

Walter K. Utt, Gregory G. Miller, Wayne L. Howie, 

and Chelsea C. Woodward 

u.s. DEPARTMENT OF HEALTH AND HUMAN SERVICES 


Public Health Service 


Centers for Disease Control and Prevention 


National Institute for Occupational Safety and Health 


Spokane Research Laboratory 


Spokane, WA 


July 2002 
ORDERING INFORMATION 


Copies ofNational Institute for Occupational Safety and Health (NIOSH) 

documents and information 

about occupational safety and health are available from 

NIOSH-Publications Dissemination 


4676 Columbia Parkway 


Cincinnati, OH 45226-1998 


FAX: 513-533-8573 

Telephone: 1-800-35-NIOSH 

(1-800-356-4674) 

E-mail: pubstaft@cdc.gov 

Web site: www.cdc.gov/niosh 

This document is the public domain and may be freely copied or reprinted 

Disclaimer: Mention of any company or product does not constitute endorsement by NIOSH. 

DHHS (NIOSH) Publication No. 2002-141 
CONTENTS 


Page 

Abstract ...................................................................................... 1 


Introduction ................................................................................... 2 


Theoretical background .......................................................................... 2 


Description of drill . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 


Data acquisition instruments ...................................................................... 4 


Program organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 


Neural networks ..... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 


Neural network packages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 


Neural network training. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 


Rock strength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 


Laboratory tests ................................................................................ 10 


Applications and extensions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 


Conclusions ................................................................................... 13 


References .................................................................................... 14 


ILLUSTRATIONS 

1. Accidents during roof bolting ............................................................... 2 


2. Drilling data flow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 


3. Laboratory roof drill ...................................................................... 3 


4. Overview of data collection process .......................................................... 5 


5. Preprocessing of individual channel signals .................................................... 6 


6. LabView graphics display screen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 


7. Example of artificial neuron ................................................................ 9 


8. Neural network training. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 


9. Neural network classification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 


10. Mine roofstrata characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 


11. Strength index versus depth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 


12. Test block 7 in form box ................................................................... 11 


13. Block with layers of differing strengths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 


14. Detection ofinclusion from change in strength. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 


15. Specific energy versus depth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 


16. SED with detrending ...................................................................... 12 


17. Penetration rate versus depth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 


18. Tele-robotic roof drilling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 


19. Tele-robotic core drilling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 


20. Supervisory remote control ................................................................. 13 


TABLES 

1. Calibration coefficients 6 


2. Training data set ........................................................................ . 8 


3. Training parameters for network ............................................................ . 8 


4. Alternative parameter sets ................................................................. . 10 

UNIT OF MEASURE ABBREVIATIONS USED IN THIS REPORT 


cm centimeter rnA milliampere 

Hz hertz ms millisecond 

kbyte kilobyte N newton 

kg kilogram N·m newton-meter 

kPa kilopascal Pa pascal 

m meter sec second 

m2 square meter V volt 

mlmin meter per minute dc direct current 

MHz megahertz rpm revolution per minute 
DRILL MONITOR WITH STRATA STRENGTH CLASSIFICATION 


IN NEAR-REAL TIME 


By Walter K. Utt,1 

'Electrical engineer. 

Gregory G. Miller,2 

'Mechanical engineer. 

Wayne L. Howie,3

JElectronics technician. 

and Chelsea C. Woodward4 

4Engineering technician, Spokane Research Laboratory, National Institute for Occupational Safety and Health, Spokane, W A. 

ABSTRACT 

The process of drilling and bolting the roof is currently one of the most dangerous jobs in underground 

mining, resulting in about 1,000 accidents with injuries each year in the United States. Researchers from the 

Spokane Research Laboratory ofthe National Institute for Occupational Safety and Health are studying the use 

of a drill monitoring system to estimate the strength of successive layers of rock and assess the integrity of a 

mine roof so that roof drill operators can be warned when a weak layer is being drilled. Measurements taken 

during drilling can be converted to suitably scaled features so that a neural network can classify mine roofstrata 

in terms of relative strength. The feasibility of this concept has been demonstrated in the laboratory. The 

research project was undertaken in order to increase the safety ofunderground miners, especially those involved 

in roofbolting. The system should be applicable to the mobile drills used in underground mining and would 

likely find wider application as well. 
INTRODUCTION 

Rooffalls in underground mines have caused many fatalities 

in the past. To reduce the risk of deaths and injuries from roof 

falls, roof bolts are used to reinforce the rock. However, the 

process of drilling and bolting the roof is one of the most dangerous jobs in underground mining, resulting in about 1,000 

accidents with injuries each year in the United States (figure 1), 

according to data compiled from Mine Safety and Health 

Administration (MSHA) statistics. 

(!) 1,400 

Z

0::: ~ 1,200 

:;:) i= 1,000 

0:....1 

C/) 0 800 

I- CO 600 ZLL Wo 0 400 0 

o 0::: 200 

o 

« 0 

r -

-

84 85 86 87 88 89 90 91 92 93 94 

YEAR 

Figure 1.-Accidents during roof bolting. 

By using a monitoring system on a roof drill to assess the integrity of a mine roof, a roof 

drill operator could be warned when a weak stratum is encountered while drilling. Such a warning could make the difference between life and death for the operator. 

The Spokane Research Laboratory (SRL) of the National 

Institute for Occupational Safety and Health (NIOSH) conducts 

research to improve the safety of miners. Building on prior 

research atthe former U.S. Bureau ofMines' Spokane Research 

Center, the feasibility ofa prototype strata strength classification 

and warning system was demonstrated on a full-scale commercial drill customized in the laboratory. 

It is necessary to measure basic drilling parameters-torque, 

rotation rate, thrust, penetration rate, and depth ofpenetration of 

the drill tip--to establish the strength of the rock layer being 

drilled. To obtain maximum benefit from the information and 

,provide a timely warning to a bolting machine operator,)t is 

desirable that the computation of strength be completed within 

a few seconds ofthe measurement. The earlier the warning, the 

more time the operator has to move to safety. 

Neural network technology was developed to classify strata 

according to their estimated strength. In this system, torque, 

rotation rate, thrust, penetration rate, and depth of the drill tip 

are measured and converted to electrical signals by transducers. 

This information flows through interface boards to a computer 

with a custom data acquisition program that includes a graphics 

display (see the section on "Neural Networks"). The data are 

smoothed by averaging or other means, and the specific energy 

of drilling (SED) is computed. Teale discovered a useful, approximate relationship between SED and the compressive 

strength of a layer of rock (Teale 1965). This relationship allows the use ofa neural network to provide a satisfactory classification ofmine roofstrata according to relative strength. Using 

this relationship is a new development in mining, as is achieving 

such a classification in near-real time. 

THEORETICAL BACKGROUND 

SED is calculated as energy input during drilling or the work 

done per unit volume of rock excavated (Teale 1965), as indicated in the following equation. 

F 2n:(WT) e=-+--­ A Au (1) 

where e specific energy, N/m2 or Pa, 

F thrust, N, 

A area of drill hole, m2 

, 

W rotation rate, rpm, 

T torque, N'm 

and u penetration rate, m/min. 

SED includes both translational and rotational energy. Rotational energy input is usually much greater than translational 

energy. However, if thrust is zero, there will be no significant 

penetration of the rock, even if rotational energy input is high. 

During normal drilling operations, SED is usually greater than the 

compressive strength ofthe material being drilled and is a useful 

feature for strength classification, assuming drilling parameters are 

within a normal operating range. Consequently, at each individual 

mine, it would be advisable to monitor drilling parameters to be 

certain they are within the normal range of operation. 

SED can be used in combination with penetration rate to provide a minimum set offeatures for a classifier. SED represents 

the energy input to drill the rock, and penetration rate represents 

the resulting output. The other measurements can be used as 

supplementary features, ifdesired. The full set ofsix parameters 

(that is, the five measurements plus calculated SED) may yield 

a more robust classifier, but the neural network would be significantly larger. The data processing program consists ofthree 

major parts: data acquisition, conversion to features, and the 

classifier, as shown in figure 2. 

Since strength is to be evaluated while drilling is still underway, it is necessary to process a subset of data corresponding to 

each layer. Such a subset can be converted to suitably scaled 
features for a neural network classifier. A pipeline processing 

system is an appropriate concept for processing the data while 

drilling through successive layers because the data can be 

regarded as flowing past a window, with the computations being 

performed on the subarray ofdata in the window. The maximum 

processing speed would be obtained with each successive stage 

processing the sub array in its own window, but this is yet to be 

done. However, the laboratory version was designed to be consistent with anticipated pipeline processing. Graphics displays, 

especially the display of estimated strength class versus depth, 

result in a significant delay that will require attention when 

designing a prototype suitable for field use. 

Conversion 

to 

features 

Figure 2.-Drilling data flow. 

DESCRIPTION OF DRILL 

A full-scale roof drill (figure 3) was customized in the 

laboratory at SRL to test intelligent drilling systems that could 

automatically determine the condition of the anchorage strata 

and optimize drilling efficiency. 

Figure 3.-Laboratory roof drill 

To accomplish this, the basic 

design of the roof drill was extensively modified to satisfY 

several objectives: the capability to sense drilling parameters, 

allow for remote control, and hold specific parameters constant 

to reduce drilling variability, as well as drill linearly and reduce 

machine vibration. The ability to flush cuttings with either 

water or a vacuum was also desired. 

Features ofthe customized drill are listed below. 

1. Sensors were installed to monitor torque, thrust, rotation 

rate, penetration rate, and position. 

2. The drill could be put under automatic control using an 

industry-standard programmable logic controller (PLC) to hold 

operator-selected drilling parameters constant. 

3. The drill was operated remotely by manual electronic 

joysticks or by the PLC. A remote-control pendant housed the 

joysticks and an electronic operator display of drilling 

parameters. 

4. Machine noise was reduced by removing the gear-style 

drill head and gear pumps and replacing them with a smoothly 

operating rotor-vane drill head and hydrostatic pump. The 

hydrostatic pump gave exceptionally good drill head and mast 

control and had the additional benefit of not building up 

excessive heat. 

5. A mast-type drill was selected to reduce the energy lost 

when the drill steel rubbed on the sides of the drill hole. (In 

contrast, arm-type drills result in high energy losses because of 

rubbing.) 

6. The drill was built with intrinsically safe sensors, proportional valves, and wire routing with the expectation that it 

could be approved by MSHA with just the addition of 

explosion-proof boxes. 

7. A combination drill head allowed rock cuttings produced 

during drilling to be removed from the hole using either standard water flushin g or vacuuming. This allowed the selection and 

simulation ofthe different flushing procedures used in the field. 

The drill was designed with the mast in a horizontal position 

so that a 2-m-Iong concrete block could be drilled by just 

elevating the block a half-meter or so off the ground into the 

fixture. (See the section on "Test Results.") Thus, the operator 

and laboratory equipment were not exposed to the dangers of 

spalling concrete associated with traditional overhead drilling. 

Once the drill was developed, the sensors were calibrated 

with specially designed linear and rotary dynamometers. When 

completed, the laboratory drill (figure 3) was used as the testbed 

for the neural net strata characterization techniques discussed in 

the section on "Neural Networks." 

Standard 2.54- or 3.49-cm-diameter, fluted, vacuum or 

water-flushed carbide bits can be used, as well as 2.54-cmdiameter, rounded, water-flushed diamond bits. Carbide bits are 

thought to cut rock by a breaking action, whereas diamond bits 

cut rock by a smoother grinding action. Diamond bits produce 

less vibration and have significantly increased bit life and should 

be selected if vibration and long bit life are concerns. Some 

research has been conducted earlier on diamond bit types by 

Sundae et al. (1995). 

Carbide bits were chosen because they are the most popular bit 

used in the field. In the laboratory tests, a standard 2.54-cmdiameter, water-flushed carbide drill bit was mounted on a 1.52­

m-Iong drill rod. To alleviate concerns about decreasing bit 

sharpness during drilling, a new bit was used for each hole drilled. 
A combination locking chuck and drill steel was selected to 

aid in removing a stuck drill steel. Rod rubbing, although 

reduced by the linear feed of the mast drill, was not totally 

eliminated. Incremental errors caused by rod rubbing as the 

drill steel penetrated the concrete block were further reduced by 

a detrending algorithm discussed in the section on "Test 

Results." 

DATA ACQUISITION INSTRUMENTS 

A laboratory test system was developed to test the strata 

classification concept. The data flow in through the transducers 

to the computerized data acquisition system. Some custom routines have been programmed to convert the data into appropriate 

groups of features for use in the neural network classifier. The 

data acquisition system will record raw data and data that have 

been scaled to the desired units. A second mode of operation 

converts the data to features, which can then be used to evaluate 

some ofthe alternative neural network classification techniques. 

The system was designed to collect and process sensor data 

as a test block was being drilled. The raw sensor signals were 

preprocessed, then delivered to a differential amplifier card. The 

backplane that hosted the amplifier card was connected to a 

National Instruments5

,6 

SMention of specific products and manufacturers does not imply endorsement by the National Institute for Occupational Safety and Health. 

"National Instruments, Inc., Austin, TX. 

AT -MIO-16E-l input/output (I/O) board 

mounted in an IBM-compatible computer. 

The computer was built with a ASUS7 

7AsusTek Computer. Inc., Newark, CA. 

P3V4X motherboard 

and an Intel Pentium III central processing unit (CPU) that 

operates at 866 MHz. The front-side bus speed of this motherboard is133 MHz. The I/O board uses the Industry Standard 

Architecture (lSA) interface to the motherboard and a SCXI 

(signal conditioning extension for instrumentation) bus connector 

to a National Instruments SCXI-1000 backplane chassis that hosts 

an eight-channel, simultaneously sampling differential amplifier. 

A Nationallnstruments SCXI-130 1 break-out box was used as the 

interface to the differential amplifier card (figure 4). 

The 110 board collects the following signals into eight 

channels: torque, thrust, revolutions per minute (rpm), rate of 

penetration, position, void detection, remote data acquisition 

pulse, and drill motor rotation pulse. Depending on the channel, 

the preprocessing flow and sensor type are specified differently. 

Each measurement channel specification is described below and 

is shown in figure 5. 

• Channel 0 was used for the torque input. From two 

NOSHOK8 

"NOSHOK, Inc., Berea, OH. 

(model 100.3000.2.1.2.7) pressure transducers 

positioned before and after the hydraulic drill motor, two signals 

ranging from 4 to 20 rnA were passed through a resistance and 

then subtracted from each other using a Burr-Brown INAl14 

integrated circuit. The output voltage approximated the torque 

on the drill motor. 

• Channell was used for the thrust signal. Initially, thrust was 

approximated with a pressure transducer in the hydraulic line of 

the thrust cylinder. The inaccuracy of this approximation 

suggested that another sensing method was needed. A 4,536-kg 

load cell was then installed in line with the hydraulic thrust 

cylinder to increase resolution. The current output from the 

Transducer Techniques9 

"Transducer Techniques, Inc., Temecula, CA. 

(model LBO-10K) load cell current 

output was converted to voltage, then passed to the differential 

amplifier card. 

Measurements of rpm were acquired using a BEllO 

"'BEl Industrial Encoder Division, Goleta, CA. 

(model 

H25) incremental encoder. The quadrature square-wave output 

signal was sent through a Red Lionll 

"Red Lion Controls, York, PA. 

bidirectional motion 

decoder (model BDMD 1000) then through a Red Lion 

frequency-to-analog voltage converter (model IFMA). The 

range used was 10 ms (100 Hz) for the minimum response time 

setting and 50 ms (20 Hz) for the maximum response time 

setting. The voltage output signals from the converter were then 

sent through a dual-pole, low-pass filter and voltage divider, 

then out to channel 2 of the SCXI 1140 110 board. The comer 

frequency ofthe filter was set to 10Hz and was adjustable to 20 

Hz. The I/O board was configured to reference the external 

ground for this channel and provide a direct current (dc) path for 

the input bias current to avoid signal drift. 

• The rate of penetration signal was delivered to channel 3 of 

the I/O board. Rate of penetration was determined using a 

Celesco '2 

12Celesco Transducer Products, Inc., Canoga Park, P A. 

(model PT9600) cable extension position encoder. 

The raw quadrature output signal was first sent to a Red Lion 

bidirectional motion decoder (BDMD 1000) to reduce signal 

jitter. The signal was then processed by a Red Lion frequencyto-analog voltage converter. The range used was 5 ms (200 Hz) 

for the minimum response time setting and 20 ms (50 Hz) for 

the maximum response time setting. The output signals from the 

converter were then passed through another two-pole, low-pass 

filter and voltage divider, then onto the SCXI 1140110 board. 

The comer frequency of this low-pass filter was also set to 10 

Hz. The SCXI -1140 board configuration was again modified for 

input from channel 3 after stray capacitances caused saturation, 

and the signal was lost. After board jumpers were changed to 

reference the ground, signal response improved. 
- - Model drill 

NOSHOK 


positive hydraulic 


pressure transducer 


NOSHOK 


negative hydraulic 


pressure transducer 


Transducer 


Techniques LBO-10K 


load cell 


elesco PT9600 


cable-extension 


~ position transducer 


MagneTek Rayelco 


p-aOa linear 


motion transducer 


Momentary switch 


+5VDC 


BEIH25 


incremental 


encoder 


- - ...... ----------...... f National Instruments 

National 

Instruments 

SCXI-1140 

simultaneously 

sampling 

differential 

a":lplifier module 

,...-------. 

ASUS P3V4X 

133 MHz FSB 

P-III 866 MHz 

CPU 

National 

Instruments 

AT-MIO-16E-1 

multifunction 

input/output 

board 

I 

I 

SCXI 1000 Backplane 

-- -- '-- -- -- ---­ Figure 4.-0verview of data collection process. 

• Channel 4 was reserved for the depth ofthe drilled hole. This 

position was attained by using the voltage output from a 

MagneTek Rayelco l3 

"MagneTec, Inc., Los Angeles, CA. 

(model P-80a) linear motion transducer. 

The cable was attached to the drill head and the instrument to 

the stationary frame. Output signal was proportional to the 

relative position of the drill head to the frame. 

• Channel 5 was used to indicate a void in the drilled material. 

If a void is encountered during drilling, the rate of penetration 

should suddenly increase as resistance to forward motion is 

removed. The output of the Red Lion bidirectional motion 

decoder was also routed through a Red Lion limit switch when 

a signal frequency above the threshold was detected. When 

normally closed (NC setting), the switch terminals were tied to 

the supply power "common" terminal and then to the SCXI1140, channel 5 positive input. This configuration prevents the 

SCXI-1140 input from floating. 

• Provisions were made to send a momentary +5-V dc signal 

through channel 6 to provide an optional remote trigger for the 

strata-classifying application software to start recording drilling 

data. When the "data" switch is in the NC setting, the terminals 

are tied to the supply power "common" and to the SCXI-1140 

channel 6 positive input. This configuration is similar to the 

"void detect" scheme and also prohibits the SCXI-1140 channel 

6 input from floating. 

• Channel 7 was used as an index reference to rotation of the 

drill motor. The BEl H25 incremental encoder used in measuring rpm produces an index signal that is gated at a half-cycle 

wide. This index signal is brought directly into the SCXI 1140 

board, positive channel 7. The encoder provides one +5-V dc, 

square-wave pulse for every rotation. 

Signal calibration of all channels involved an auxiliary test 

program that used the same data acquisition methods as the 

strata classifier application written in the graphics program 

LabView.14 

14National Instruments, Austin, TX. 

Signal levels were monitored in the test program 

and compared graphically to data experimentally collected 

through each iteration. Polynomials were used to approximate 

curves in the active range. The polynomial coefficients were 

then entered into the program software to translate the conditioned signal levels to meaningful values. The calibration of 

torque, thrust, and rpm were determined by developing signal­
to-value curves. The reference values were read from a LeBow 

Productsl5 

l'LeBow Products, Troy, Ml. 

(model 6446-104) thrust torque sensor. The rate of 

penetration was calibrated by measuring signal outputs as indicated in the test program and comparing them to actual distance 

traveled per minute. The position signal was linear to the actual 

distance the drill head traveled. The void-detect frequency was 

adjusted to compensate for the hysteresis caused by the trip and 

release points set too close together. The data switch and rpm 

reference tick signals did not require calibration. Calibration 

curves for the basic measurements are presented in table 1. 

Table 1.-Calibration coefficients 

Torgue. N·m Thrust. N Rotation rate. rpm Penetration rate. mlmin Depth of bit. cm 

Intercept 10.27 -250.4 o 0.0057 7.3098 

Slope. 116 3569 125 0.8719 17.268 

I ------- ----------------------~ ~.- ............ _.... -----........ -_ ........ _...... _............................ _.. _.... --........ -........ -....... --.. _...... _......... ...
~ 

Sensor array Signal conditioning , I 

I NOSHOK Current Channel to voltage positive hydraulic circuit I number

Burr-Brown INA114 

differential 

pressure transducer 

I 0 

NOSHOK signal conditioner Current to voltage negative hydraulic circuit pressure transducer 

Transducer 


Techniques 
 circuit 

Red Lion bi-dieclional 

BDMD 1000 

motion decoder 

Current to voltage 1--------------------;.-1..... L.BO-1 OK load cell 

celesco PT9600 Red Lion IFMA 


Cable-extension 
 frequency-to-analog
 [ position encoder 
 converter 

MagneTek Rayelco 

+5VDC 

momentary switch 

BEl H25 

incremental encoder 

P-BOa linear 1-~~----+--------------------4--!-...L. 4 motion transducer 

Red Lion 

IFMR r-------------------------~~~5 limit switch 

r-~------------------------------------~~6 ~--------------------------------------~~~7 

Red Lion bi-diectional , Red Lion IFMA 

BDMD 1000 frequency-to-analog motion decoder converter 

............................. _.......... -...... --................. -_ .......................... _.. -........... _.. _................ -......... ~ 

~----------------------------_/ Figure 5.-Preprocessing of individual channel signals. 

PROGRAM ORGANIZATION 

An overview of the program structure, emphasizing data 

flow, is presented in figure 2. The software for the drilling application consisted of three parts. 

1. A program to control the data acquisition process and 

display information, written in LabView. 

2. A program to perform the necessary preliminary processing and convert the data into features, written in C language. 

3. The classifier, a neural network created from the Data 

Engine l6 

16MIT GmbH, Achen, Germany. 

software package, which was compatible with LabView. 

Some additional routines separate from the main program 

were created from Data Engine for training and labeling the 

neural network. A display panel from LabView is shown in 

figure 6. The data acquisition process is monitored by means 

of a graph displayed in the upper left portion of the panel. 

Eight channels of input data can be displayed as a function of 

time. The estimate of strength as a function of depth is presented in the lower graph. A few of the input parameter selections are depicted in the upper portion of the panel. A 

message box that informs the operator when a parameter is out 

ofthe normal bounds ofoperation is in the lower right portion 

of the panel. 
Figure 6.-LabView graphics display screen. 

That part of the program coded in the LabView graphics 

language is rather extensive, requiring over 620 kbyte of 

memory. The program for preliminary processing, written in 

C, requires only 68 kbyte in the dynamically linked library 

(dll) version, which is called from the data acquisition program 

as a set of call library functions. 

The integration ofthe classifier, based on Data Engine, with 

the data acquisition program, based on Lab View, was completed 

successfully. However, calling the C language functions from 

the data acquisition program turned out to be more complicated 

than expected. The C language functions had been checked out 

in a console version with a C language driver. However, it was 

necessary to use several global variables with different names in 

so-called "exportable wrapper functions" to integrate the C functions with the data acquisition calling program. The attempt to 

develop a multithreaded version of the program with critical 

sections to avoid conflicting calls encountered such difficulties 

that it was abandoned. A functional laboratory program that 

interfaced with the transducers on the laboratory drill was then 

used to evaluate and refine the program. 

NEURAL NETWORKS 

NEURAL NETWORK PACKAGES 

Two commercial neural network packages (EZ-l [Pryor 

Knowledge Systems 1995] and Data Engine [MIT GmbH 

1996a, I 996b]) were evaluated. The EZ-l is a package of supervised neural network techniques with an accelerator board. 

The package contains three alternative software programs: (1) 

a probabilistic neural network (Specht 1988); (2) the RCE 

neural network (Reilly et al. 1982; Reilly and Cooper 1990), 

patented as the Self Organizing General Pattern Class Separator 

and Identifier (Cooper et al. 1982); and (3) PRCE, which is a 

combination of the probabilistic and the RCE methods. 

Data Engine is a package of unsupervised neural network 

techniques that contains two alternative software programs: 

(4) Kohonen's self-organizing feature mapping algorithm 

(Kohonen 1995) and (5) fuzzy cluster means combined with 

Kohonen's algorithm (Tsao et al. 1994). 

All five alternatives were tested to classifY geologic strata and 

appeared to perform satisfactorily, which is an indication of the 

significant advances in neural network technology in recent years. 

The unsupervised learning algorithm of Kohonen (MIT 

GmbH 1996a; Kohonen 1995) was selected for the crisp 

classification oflayer strength in one of32 classes. The Data 

Engine software package was compatible with the LabView 

software. The competing EZ-l product, with the NESTOR accelerator board, processes data rapidly, but the software had 

not been upgraded for newer 32-bit address spaces now used 

in personal computers. 

NEURAL NETWORK TRAINING 

A supervised neural network architecture must be trained 

with known classifications prior to being used to classify new 

measurements. In the training or learning phase, classification 

output is compared to known classifications. The error in the 

output layer must be propagated back through the network in 

order to adjust weighting. The use ofback-propagation oferror 

in the manner ofa steepest descent as described in Rumelhart et 

al. (1986) was a major step forward in neural network technology. However, there have been several subsequentmodifications and variations on the iterative procedure, and this procedure can now be done off-line and need not affect the computational time required for classifYing in near-real time. Note 

that linear independence is not essential in most neural network 

techniques, which would be the case in other mathematical 

representations. 

The drill bit used in training must be ofthe same type as the 

bit used in subsequent drilling. Sharpness ofthe bit should also 

be monitored. Truth values should be obtained to compare 

measured parameters to known rock types and strengths. The 

training set of features required 32 cases for the 32 classes of 

strength, so interpolation and extrapolation were required. Although it is more common to scale to a specified range of 

values, the Kohonen method in Data Engine uses normalized 

input features. The input features, such as penetration rate and 

SED, are normalized using the following equation. 

(x- Ji) xp=--- (2) (J 

where xp normalized value, 

x input value, 

fJ. mean or expected value, 

and 0 standard deviation. 
The normalized training data set is presented in table 2. 

Table 2.-Training data set 

Strength class Class mean strength, Rate of penetration, SED, training 2, kPa Normalized rate Normalized 

kPa training 2 mlmin of !2enetration SED 

0 3,447.4 3.048 6,894.8 3.4000 -1.6393 

1 10,342.2 2.5908 20,684.4 2.6987 -1.5340 

2 17,237 2.1336 34,474 1.9973 -1.4286 

3 24,131.8 1.6764 48,263.6 1.2960 -1.3233 

4 31,026.6 1.3716 62,053.2 0.8285 -1.2180 

5 37,921.4 1.18872 75,842.8 0.5479 -1.1126 

6 44,816.2 0.9144 89,632.4 0.1271 -1.0073 

7 51,711 0.73152 103,422 -0.1534 -0.9019 

8 58,605.8 0.71628 117,211.6 -0.1768 -0.7966 

9 65,500.6 0.70104 131,001.2 -0.2002 -0.6913 

10 72,395.4 0.6858 144,790.8 -0.2235 -0.5859 

11 79,290.2 0.67056 158,580.4 -0.2469 -0.4806 

12 .......... 86,185 0.65532 172,370 -0.2703 -0.3753 

13 93,079.6 0.64008 186,159.6 -0.2937 -0.2699 

14 .......... 99,974.6 0.62484 199,949.2 -0.3170 -0.1646 

15 106,869.4 0.6096 213,738.8 -0.3404 -0.0593 

16 .......... 113,764.2 0.59436 227,528.4 -0.3638 0.0461 

17 .......... 120,659 0.57912 241,318 -0.3872 0.1514 

18 127,553.8 0.56388 255,107.6 -0.4106 0.2568 

19 134,448.6 0.54864 268,897.2 -0.4339 0.3621 

20 ......... 141,343.4 0.5334 282,686.8 -0.4573 0.4674 

21 148,238.2 0.51816 296,476.4 -0.4807 0.5728 

22 .......... 155,133 0.50292 310,266 -0.5041 0.6781 

23 162,027.8 0.48768 324,055.6 -0.5274 0.7834 

24 168,922.6 0.47244 337,845.2 -0.5508 0.8888 

25 175,817.4 0.4572 351,634.8 -0.5742 0.9941 

26 182,712.2 0.44196 365,424.4 -0.5976 1.0994 

27 189,607 0.42672 379,214 -0.6210 1.2048 

28 .......... 196,501.8 0.41148 393,003.6 -0.6443 1.3101 

29 203,396.6 0.39624 406,793.2 -0.6677 1.4155 

30 210,291.4 0.381 420,582.8 -0.6911 1.5208 

31 230,975.8 0.33528 461,951.6 -0.7612 1.8368 

Mean ... 0.8315 221,495 

Stand. dev. 0.6519 130910 

The input parameter values for training the Kohonen network 

on the field data are presented in table 3. 

Table 3.-Training parameters for network 

In!2ut !2arameter In!2ut value Comment 

Kohonen parameters: 

No. of features ....... . 2 

No. of dimensions .. 3 

Neurons, 1 st dimension 2 Initial, input layer 

Neurons, 1 st dimension 6 Initial, auto release 

Neurons, 3rd dimension 32 Initial, outer layer 

Training parameters 1: 

Initial learning rate ...... . 0.999 

Initial learning radius ... . 4 

Learning rate factor ..... . 0.99 

Learning radius factor . 0.995 

Presentation order ...... . o Sequential 

Training parameters 2: 

Cycles ........... . 200 

Presentation order. 1 Random 

Training mode ............ . o Initial new !2arameters 

A generic artificial 

neuron is shown in figure 7. Most of the neural network 

techniques use an activation function, usually sigmoidal, as 

well as a weighted sum of the several inputs. The Kohonen 

algorithms are based upon competitive learning and do not 

require the activation function. The weight for each input to 

a neuron in the artificial neural network is fixed at the 

conclusion of the training or learning phase, as indicated in 

figure 8. It is recommended that a validation test be performed 

to demonstrate that the classifications are correct. The neural 

network can then be used to classify the new input data 

according to the rules frozen into the network, as indicated in 

figure 9. Only the classification process contributes to the 

computational time of primary concern during drilling. 
Input 11 

Weights 

Neuronj 

~I l-----~jl Output Sigmoidal activation fUnction 

Figure 7.-Example of artificial neuron. 

--.. Rc(f(l!:.)) 

Xn --.. f(:n) / 

Features Labeled Mapping to 

data set network of 

neurons 

Figure B.-Neural network training. 

X1 ~f(lS.1)\ 

K2 f(K.2) r-----, 

Learned 

x ~ mapping C(x) 

Rc(f(lS.)) 

lS.~ ~f(:n)/ Features Unlabeled Classification 

data set 

Figure g.-Neural network classification. 

Input Neural network Output 

Drilling Classifier Class of rock 

measurements strength (0-31) 

Penetration ~-~~ Strong rock (B-31)

rate • ~~.".Ll~ Medium strength 

rock (4-7) 

Specific \~~@I-I:""") Weak rock (1-3)

energy • __."---~r-"1l -.Very weak seams, 

fractures, voids (0) 

Figure 1D.-Mine roof strata characterization. The conceptual artificial neural network for strata characterization is shown in figure 10. The actual network would have 

many more neurons, from 64 up to 384 or more, depending on 

the choice oftraining parameters. Very strong rock would require a different drilling technique, that is, percussive drilling 

rather than rotary drilling. Roller cone bits are regarded as percussive. Consequently, the "very strong" rock category is not 

depicted. For a warning, classifications forthe laboratory system were grouped into three color categories- red for weak, 

yellow for medium, and green for strong. 

A briefinvestigation ofalternate feature vectors was conducted 

early in the project using data from prior research at SRL and 

geological classes as described by King and Signer (1994) and 

King et al. (1993). After a neural network was trained on four of 

the existing data files, it was used to classify data from another file 

and was found to be successful in discriminating layers. The two 

features, SED and penetration rate, were found to be satisfactory 

for classifying different layers into the proper geologic classes. 

The SED input can vary from one operator to another, but 

penetration rate can help reduce the effect ofthat variation on the 

classification. The full set offeatures-SED, torque, rotation 

rate, thrust, and penetration rate, in addition to depth as an 

independent variable-gave comparable performances at 

discriminating layers to the set of SED and penetration rate. 

The Kohonen approach combined with fuzzy clustering 

algorithm (alternative 5) automatically identified a start-in 

class that corresponded to observations made of the drill 

entering the rock, as indicated in table 4. The rock layers were 

arbitrarily labeled to compare the patterns of layer 

classifications in the preliminary analysis. When a drill bit 

first enters the rock, a lot of visible and audible chatter is 

observed, and noise in the data is high. When the drill tip is at 

a depth sufficient to quell the start-in chatter, it is said to have 

established a collar. In fact, the data obtained prior to reaching the collar depth should not be used in the strength classification, since these data would be misleading. Although the 

class must still be labeled, that the algorithm could automatically select an appropriate class is impressive. 

ROCK STRENGTH 

Naturally occurring rock varies considerably in both composition and strength. Rock strength is often classified in 32 

classes (Carmichael 1982). Field strength values tend to be 

lower than laboratory values (Heuze 1980) because a larger 

volume of rock is likely to have more fractures or joint sets 

than a laboratory sample (Brady and Brown 1985:86-90). The 

presence ofmoisture also can degrade the strength ofthe rock. 

Bit geometry and bit sharpness have a significant influence on 

the strength estimate. Thus, an approximation ofrock strength 

is the best that one can do under these circumstances. 

The network was trained on data for which strength was 

known and labeled. Data from a typical borehole were placed 
into one of 32 classes of compressive strength. The resultant 

strength index, or class, is presented as a function of depth in 

figure 11. 

oo 16 

oo

« 14 --l 

() 12 

><W 10 

o 

~ 8 

I

I­ 6 

~ 4 

W 

~ 2 

l­

(/) 0 

-------~ weak. 

o 20 40 60 80 100 120 140 160 

DEPTH, em 

Figure 11.-5trength index versus depth. 

There are three layers where the strength index drops 

below 4, indicating that these layers are weak and not suitable for 

anchoring. The deeper layers have a strength index greater than 8, 


which means they are strong enough to provide a good anchor. 


. If an estimate of compressive strength is required, it can be 


obtained. However, the strength class was adequate for the 

purposes of this project. The strength classification is both 

feasible and useful. 

Computational time is a significant consideration for practical 

implementation. An autoregressive integrated moving average 

(ARIMA) process (Gelb 1974:92-96) with full overlap of the 

data window was used in the preliminary smoothing or noise 

reduction phase of computation, producing a relatively smooth 

plot. Although the ARIMA computation worked rather well, as 

indicated in the smooth plot of strength index versus depth in 

figure 11, it was too slow, requiring about 40 sec to process each 

window of data. Therefore, it was necessary to replace that approach with a much simpler averaging computation in a preliminary filter. The optional trend evaluation from the first to 

the current data window was retained. 

An autoregressive moving average (ARMA) process could 

also be used ifthe trend were removed so that the statistical data 

would be stationary. The ARMA process was retained as an 

option, with no overlap of the data window, so that computational speed could be maintained. The software changes increased the speed ofthe computation. An additional speed increase by a factor of 4 was obtained by an upgrade of the 

computer hardware, resulting in a net increase in speed by 

approximately a factor of 10. 

Table 4.-Alternative parameter sets 

Parameter set From line To line Cluster output Layer label Classification Partition Proportion 

0 2 3 Start in 

entropy 

0.6 

coefficient 

0.68 

exponent 

6, all scaled, 4 clusters, 337.4 

exponent =2 3 34 1 A 


35 49 2 B 


50 51 4 C 


52 60 2 B 


61 79 4 C 


3, depth not scaled, 4 clusters, 0 19 2 Start in 0.43 0.77 556 

exponent = 2 20 40 4 A 

41 64 1 B 

65 79 3 C 

3, all scaled, 4 clusters, 0 9 3 Start in 0.6 0.69 359.2 

exponent = 2 10 34 2 A 

35 49 1 B 

50 51 4 C 

52 60 1 B 

61 79 4 C 

NOTE: Six-parameter set = depth, torque, thrust, rotation rate, penetration rate, SED. Three-parameter set - depth, 

penetration rate, SED. 

LABORATORY TESTS 

Concrete blocks containing various types ofrock inclusions 

were constructed to simulate changing mine roof conditions. 

Plywood form boxes were built in the desired shape, and rebar 

racks were placed inside to elevate the rock inclusions to the 

center ofthe box (figure 12). Each rock inclusion was attached 

to the rebar with metal bands to hold it in place while the 

concrete was being poured. A brass rod was inserted along 

each side ofthe inclusion in the box to mark the position ofthe 

inclusion. Rebar was placed from each corner ofthe end ofthe 

box to the corner at the other end to give the block additional 

strength. Wire ropes were also placed at each end ofthe block 

to enable the block to be picked up easily. Six bags of cement 

(335 kg) per cubic meter of concrete were used. Samples of 

the concrete were taken for subsequent strength tests, and 

slump tests were conducted as well. A sketch of a block with 

layers of differing strength is shown in figure 13. 
Figure 12.-Test block 7 in form box. 

4.· 

Figure 13.-Block with layers of differing strengths. 

A laboratory test demonstrated the capability to detect an 

inclusion. The results ofthe test are presented in figure 14. 

20 


18 


~ 16 


::s 14 


t) 12 


i= 10 


C) 8 


Z 

W 6 


0::: 

I- 4 


C/J 2 


o 


o 10 20 30 40 50 60 70 80 


DEPTH, em 

Figure 14.-Detection of inclusion from change in strength. 

A 

small change in strength is indicated at a depth of about 20 cm 

and a larger change at a depth of about 42 cm. The detection of 

an inclusion from a change in the strength was, to the best ofour 

knowledge, the first successful demonstration ofthis capability. 

Typical drilling data from a borehole were processed. Figure 15 presents SED as a function of the depth of the drill tip. 

LL 

o 70 


G~ 60 


ffi ~_ 50 


Z<.9 


W Z 40 


~:3 30 


~(2

() 0 20


W 


Q.. 10 


C/) 

... .c: 
 I = 

CIl_ 

c.
 0 (J) 

(,,)"0 

1
 

o 20 40 60 80 100 120 140 


DEPTH, em 

Figure 1S.-Specific energy versus depth. 

The spurious peak would not be used in estimating rock 

strength, nor would the data collected before the collar depth 

is reached (10 cm). A linear upward trend in SED is probably 

caused by friction as the steel drill shaft bends under thrust and 

rubs in the borehole. Such trends should be removed from the 

data before classification (Masters 1993), and that has been 

done. The effect of removing the trend is shown in figure 16. 

The control limit lines shown enable a researcher to distinguish 

random noise between the control lines from significant 

differences in rock strength. 
Penetration rate is presented as a function of depth in figure 17. 

25r-~~~--~--~--~--__--~ 

ro 

~ ...:: 

O~ 

W 20 

Cl 

Z 

w 

0::: 

tu 15 

o 

o 

w 

(J) 

I.... .c Significant I~a. 8~ change in 

Random strength 

~~--~~~~~~__~UCL 

~~--+-~~~~~~LCL 

10~~--~--~--~~~~__~ 

o 20 40 60 80 100 120 140 

DEPTH, em 

Figure 16.-8ED with detrending. 

~ 4.0 

5 3.5 

WI- 3.0 

« 2.5 

Z 

c::: 

2.0 0 

i= 1.5 

~ 1.0 IW 0.5 Z 

W 

c.... 0 

Figure 17.-Penetration rate versus depth. 

0 20 40 60 80 100 120 140 

DEPTH, em 

Penetration rate indicates results ofdrilling, while specific energy represents the work put into the rock. The specific 

energy input can vary according to the manner of drill 

operation, but penetration rate should reduce the influence of 

that variability on estimated strength. Neither feature is 

without shortcomings, buttogetherthey provide a reliable basis 

for estimating the strength of the rock. 

The presence of cracks or voids would indicate a weak 

layer, which would be dangerous in a mine roof A strata depth 

resolution of 2.5 mm is desired to detect fractures. 

A record of the strength estimate for rock layers as a 

function of depth should be retained for viewing after a hole 

has been drilled. Such a strength estimate will be useful in 

selecting appropriate bolt lengths and patterns for roofbolting 

and may also be useful for a preliminary description of the 

layer being drilled. Informing the operator when one of the 

measured variables is out of the normal band of operation 

would also be desirable, since variations would affect the 

validity ofthe estimate of the strength of the layer. 

There are two problems to be addressed in a subsequent 

phase ofdevelopment. The first is the time required to plot the 

graph of strength class versus depth of drill tip. The integration ofthe buffered results into the graphing software proved 

awkward, with the graphing software waiting for data from the 

buffer. This delayed output of the graph. 

The other problem is noise. The drilling process introduces 

significant amounts of vibrational noise in the frequency band 

from 1 to 20 Hz. The use of a differential pressure transducer 

for torque measurement also contributes some oscillatory 

noise. One partial solution is to reduce the corner frequency of 

the low-pass filter, which is used to prevent aliasing, to reduce 

higher frequency noise. In the past, corner frequency has been 

reduced from 10 to 1 Hz. However, such a reduction could 

adversely affect performance in terms of response rate and 

detection ofthin layers. The trade-off would be between noise 

reduction and execution speed and should probably be 

revisited during the design of a field prototype. 

We expect the subsequent development of a field prototype 

to include the use of a real-time operating system and an 

appropriate set ofaccurate, reliable, and robust transducers. The 

software would be rewritten for a real-time operating system. A 

second microprocessor could be used to process the buffered 

output for the graph, which would free the main microprocessor 

for the rest ofthe calculations and speed up overall processing. 

In the future, a flat-faced, wear-resistant, diamond bit should 

improve detection of the interface between rock types, especially ifthat interface is perpendicular to the bit. In the field, 

the interface is unlikely to be perfectly horizontal. 

APPLICATIONS AND EXTENSIONS 

The technology described above has other drilling applications and could be used in other engineering research projects. 

Drilling holes for blasting in mining and construction is a possible application, since rock strength is an important consideration in efficient blasting (Hemphill 1981 ). The benefits ofusing 

microprocessor-based systems to monitor blast hole drilling 

have been described by Peck et al. (1988). Drilling exploration 

core is another likely area of application. 

Drilling for oil is a feasible application, although additional 

measurements, such as resistivity, may be needed for 

geological analysis (McCormick 1991). A roller cone bit is 

used in drilling for oil. The vibrations ofthe drill string have 

been utilized, via a wireless transmission system, to refine 

control of the drilling process (Henneuse 1992). 

In the future, it is expected that a remote-control system will 

be developed that would allow a drill operator to be positioned 

in a safer location (figure 18) and less likely to be under a roof 

fall. Tele-robotic core drilling is expected to develop along a 

parallel path (figure 19). Both remote-control tasks could 

serve as research projects. 
Figure 18.-Tele-robotic roof drilling. 

Figure 19.-Tele-robotic core drilling. 

Detecting drill steel chatter would be very helpful because the 

chatter could warn oftrouble. The chatter associated with a misaligned or damaged drill bit is of particular interest to a drill 

operator, especially a remote operator. An accelerometer at the 

chuck or the base ofthe drill could be used to measure vibrational accelerations, and either statistical or neural network techniques could be used to distinguish between tolerable and 

intolerable chatter. Geologists may be interested in measuring 

resistivity and electric potential to aid in the identification of 

strata. The temperature of the drill bit and drill bit wear might 

also be monitored (Choi and Ly 1992). 

Local 

loops } 

Better measurements of the ultimate compressive strength of 

rock in the field should enable researchers to retrain the classifier 

for better performance. Type ofdrill, bit geometry, bit sharpness, 

rock stiffness, and the amount of fracturing in the rock are also 

important factors. The borehole penetrometer, described in Unrug 

et al. (2001), appears to be appropriate for testing rock strength in 

a mine. Rock material under the indenter ofthe penetrometer fails 

in shear because oftriaxial conditions ofloading. Some rock mass 

index properties may be useful. The number offractures or joint 

sets in a drill hole could be used to determine rock quality 

(Franklin et al. 1971). A preliminary calculation of rock quality 

was included in this drill monitoring program, but was not 

validated before the project ended. 

Supervisory remote control might be ofinterest where continuous 

remote control is not entirely satisfactory (Sheridan 1992:86-90). In 

the supervisory remote control paradigm, some necessary and routine control functions could be automated at a remote drill, but the 

capability to modify the task schedule, monitor progress, and interrupt in an emergency would be retained by the operator (figure 20). 

Figure 20.-Supervisory remote control. 

A group of university researchers has expressed an interest in 

continuing the development ofthe drill monitoring system for use 

in mining, while another university research group has expressed 

interest in the feasibility ofthe system for extraterrestrial remote 

control applications using percussive drilling (Eustes et al. 1999). 

CONCLUSIONS 

A prototype drill monitoring system with a strata strength 

classifier was developed and demonstrated at SRL. The goal 

was to characterize the strength ofthe strata in a timely fashion 

so that the information can be used in the field, preferably while 

drilling is still underway. Drilling measurements for each roof 

bolt hole can be processed and the essential information displayed for the operator to monitor in near-real time. To do this, 

it is necessary to measure drilling parameters-torque, rotation 

rate, thrust, penetration rate, and the depth of drill tip 

penetration-and estimate the strength of the layers being 

drilled. Neural network technology can classify strata according 

to its estimated strength. 

The Committee on Advanced Drilling Technologies of the 

National Research Council regards sensing and evaluating rock 
properties while drilling as a revolutionary improvement 

(National Research Council 1994). Detecting a different layer 

by the change in rock strength was, to the best ofour knowledge, 

the first successful demonstration of this capability. The 

application of neural network technology to strength classification ofthe material being drilled is new, as is estimating the 

strength ofrock layers in near-real time. The concept has been 

proven in principle, and a patent is pending (Utt 2000). 

REFERENCES 


Brady, B.H.G., and Brown, E.T. 1985. Rock Mechanicsfor Underground 

Mining. Boston: George Allen & Unwin, 527 pp. 

Carmichael, R.S. 1982. Handbook ofPhysical Properties ofRocks. Boca 

Raton, FL: CRC Press, vol. 2, 306 pp. 

Choi, J.J., and Ly, S. 1992. Drill wear monitoring with neural networks and 

fuzzy logic. In Northcon 1992, ed. by H. Juj and A.T. Moser (Seattle, W A, Oct. 

19-21, 1992). Piscataway, NJ: Institute ofElectrical and Electronics Engineers, 

pp. 284-292. 

Cooper, L.N., Elbaum, C., and Reilly, D.L. 1982. Self-Organizing General 

Pattern Class Separator and Identifier, U.S. Patent no. 4,326,259. 

Eustes, A., Rostami, J., and Blair, B. 1999. New technology: Review of 

issues related to extraterrestrial drilling. Geo Drilling international, vol. 7, no. 

8, pp. 32-39. 

Franklin, J.A., Broach, E., and Walton, G. 1971. Logging the mechanical 

character of rock. Transactions: Mining 1ndustry, Bull. 770, Section A, vol. 

80, pp. AI-A9. 

Gelb, A. 1974. Applied Optimal Estimation. Cambridge, MA: MIT Press, 

374pp. 

Hemphill, G.B. 1981. Blasting Operations. New York: McGraw-Hili, 258 pp. 

Henneuse, H. 1992. Surface detection ofvibrations and drilling optimization: Field experience. In Proceedings: Drilling Conference (New Orleans, 

LA, Feb. 18-21, 1992). Richardson, TX: Society of Petroleum Engineers, 

pp.409-423. 

Heuze, F.E. 1980. Scale effects in the determination of rock mass strength 

and deformability. Rock Mechanics, vol. 12, pp. 167-192. 

King, R.L., Hicks, M.A., and Signer, S.P. 1993. Using unsupervised 

learning for feature detection in a coal mine roof. EngineeringApplication of 

Artificial Intelligence, vol. 6, no. 6, pp. 565-573. 

King, R.L., and Signer, S.P. 1994. Using artificial neural networks for 

feature detection in coal mine roofs. In Computer Methods and Advances in 

Geomechanics. Proceedings of the Eighth International Conference on 

Computer Methods and Advances in Geomechanics, ed. by H.J. Siriwardane 

and M.M. Zaman (Morgantown, WV, May 22-28, 1994). Rotterdam: Balkema, 

vol. 3, pp. 1853-1857. 

Kohonen, T. 1995. Se{fOrganizingMaps. New York: Springer-Verlag,362pp. 

Masters, T. 1993. Practical Neural Network Recipes in C++. Boston: 

Academic Press, 493 pp. 

McCormick, M.D. 1991. Neural computing in geophysics. Geophysics, the 

Leading Edge ofExploration, vol. 10, pp. 11-15. 

MIT GmbH, Aachen, Germany. 1996a. Data Engine V.i. Data mining by 

intelligent data analysis. Part I: Basics. Pp.40-66. 

MIT GmbH, Aachen, Germany. 1996b. Data Engine V.L Data mining by 

intelligent data analysis. Part II: User manual, function reference, and tutorials. 

Pp.5-1-5-29. 

National Research Council, Committee on Advanced Drilling Technologies. 

1994. Drilling and Excavation Technologies for the Future. Washington, 

D.C.: National Academy Press, 161 pp. 

Peck, J, Scobie, M.J., and Hendricks, C. 1988. Monitoring while drilling 

production blastholes: Applications in surface mining. In Explosives and 

Blasting Research, 4th Mini-Symposium, ed. by R.D. Boddorff(Anaheim, CA, 

Feb. 1-5, 1988). Montville,OH: Society of Explosive Engineers, 145 pp. 

Pryor Knowledge Systems, Inc., Bloomfield Hills, MI.1995. EZ-l neural 

network system-basic. 74 pp. 

Reilly, D.L., and Cooper, L.N. 1990. An overview of neural networks: 

Early models to real world systems. Ch. 11 in An Introduction to Neural and 

Electronic Networks. San Diego: Academic Press, pp. 227-248. 

Reilly, D.L., Cooper, L.N., and Elbaum, C. 1982. A neural model for 

category learning. Biological Cybernetics, vol. 45, pp. 35-41. 

Rumelhart, D., Hinton, G.E., and Williams, R.J. 1986. Learning internal 

representations by error propagation. In Parallel Distributed Processing: 

Explorations in the Microstructure ofCognition. Vo!. 1: Foundations, ed. by 

D. Rumelhart, J. McClelland, and the PDP Research Group. Cambridge, MA: 

MIT Press, pp. 318-362. 

Sheridan, T.B. 1992. Telerobotics, Automation and Human Supervisory 

Control. Cambridge, MA: MIT Press, 396 pp. 

Specht, D.F. 1988. Probabilistic neural networks for classification, 

mapping, or associative memory. In Proceedings, IEEE Second International 

Conference on Neural Networks (San Diego, CA, July 24-27, 1988). 

Piscataway, NJ: Institute of Electrical and Electronics Engineers, vol. 1, pp. 

525-532. 

Sundae, L.S., Smelser, T.W. and Howie, W.L. 1995. Investigation of optimum thrust, cutting speed, and water pressure for tungsten carbide and polycrystalline diamond compact roof-bolt drill bits. U.S. Bureau ofMines Report 

ofinvestigations 9574, 20 pp. 

Teale, R. 1965. The concept ofspecific energy in rock drilling. Int. J Rock 

Mech. Min. Sci. & Geo. Abs., vol. 2, pp. 57-73. 

Tsao, E.C-K., Bezdek, J.C., and Pal, N.R. 1994. Fuzzy Kohonen clustering 

networks. Pattern Recognition, vol. 27, no. 5, pp. 757-764. 

Unrug, K.F., Thompson, E., and Altizer, D. 2001. New development in the 

testing of rocks. Mining Engineering, vol. 53, no. 1, pp. 43-49. 

Utt, W.K. 2000. Method for characterization of rock strata in drilling 

operations. U.S. Patent Application no. 09/613,963. 
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Through research and prevention 


For information about occupational safety and health topics contact NIOSH at: 

1-800-35-NIOSH (1-800-356-4674) 

Fax: 513-533-8573 

E-mail: pubstaft@cdc.gov 

www.cdc.gov/niosh 

DHHS (NIOSH) Publication No. 2002-141