Neural network application to mine-fire diesel-exhaust discrimination 

G.F. Friel & J.C. Edwards 

National Institute for Occupational Safety and Health, Pittsburgh Research Laboratory, Pittsburgh, PA, USA 

1 INTRODUCTION 

If diesel engines are present in underground mines, 

the presence of diesel exhaust can interfere with the 

early detection of hazardous combustion in flammable materials such as coal and conveyor belting. To 

discriminate the hazardous combustion from the diesel exhaust, it was decided that a collection of commercial sensors would be tested to determine what 

set of sensors would be most appropriate. These sensors included both fire and environmental sensors. A 

neural network algorithm was chosen as the function 

approximator in the analysis of the sensor data 

mainly because of the many possible independent 

and dependent variables involved. Earlier work included neural networks applied to the discrimination 

of coal combustion from water vapor and shot firing 

fumes (Brinn & Bott 1994) and to the discrimination 

of coal, diesel-fuel, conveyor-belting, electricalinsulation, and metal-cutting products of combustion 

(POC) from each other (Edwards et al. 2000). To determine what commercial sensors were appropriate 

and what neural network algorithm was best to process the data from the sensors, a series of seven underground fire experiments was undertaken in the 

Safety Research Coal Mine (SRCM) at the National 

Institute for Occupational Safety and Health 

(NIOSH), Pittsburgh Research Laboratory, Pittsburgh, PA, USA. 

 

2 EXPERIMENTAL METHODOLOGY 

Figure 1 shows a schematic diagram of the underground SRCM experimental entries. In each of four 

experiments, about 14 kg of coal was ignited in a

61-cm-square steel pan placed on the floor in the 

middle of Room 10. Five electrical strip heaters 

were spaced evenly inside the pan under a layer of 

coal lumps of size less than 5-cm equivalent diameter and of depth sufficient to cover the heaters. 

About a kilogram of coal fines was sprinkled on top 

of the coal to thermally insulate the coal and decrease the time to ignition. The larger specific surface area of the coal fines compared to the coal 

lumps also provided more CO during the smoldering 

combustion stage. The coal was heated for about 40 

min with power ranging from 1.3 kW to 2.4 kW in

ABSTRACT: A series of seven underground-coal-mine fire experiments was conducted in the Safety Research Coal Mine at the National Institute for Occupational Safety and Health, Pittsburgh Research Laboratory. Coal and styrene-butadiene-rubber conveyor belting were burned upwind of two sensor stations, 18 m 

and 148 m from the fire source. Exhaust from a diesel locomotive flowed over the fire sources in six of the 

tests. Metal-oxide-semiconductor (MOS), CO, and optical-path-smoke sensors were positioned at both stations and found to be an optimum set of sensors for the fire discriminations. A representative set of 7,679 

samples of CO data and data from the smoke and diesel-exhaust MOS sensors were used as inputs to train a 

neural network (NN). By testing 42,538 data samples from the seven experiments, all fires were detected by 

the NN within 9.67 min from the onset of significant changes in the MOS voltages without any false alarms. 

Figure 1. Schematic diagram of a portion of the SRCM. 
10-V increments from 130 V to 170 V every 10 min. 

This rate of heating was selected to generate copious 

quantities of CO during the coal smoldering combustion stage. Usually, intense smoking of the coal 

occurred near 30 min after the start of heating with 

flaming near 40 min. In another three experiments, a 

53-cm-square piece of styrene-butadiene-rubber, 

conveyor belting about 1.1 cm thick was clamped to 

the top of a steel plate. The belting was heated by 

the five strip heaters next to and spaced evenly below the steel plate. A nonflammable insulating board 

was placed under the heaters to level and protect the 

heaters from the damp mine floor. The belt was 

heated for about 40 min with power ranging from 

1.1 kW to 3.3 kW in 20-V increments from 120 V to 

200 V every 10 min. Usually, intense smoking of the 

belt occurred near 30 min after the start of heating 

with flaming after 40 min. In three of the coal and 

all of the conveyor belt experiments, an idling diesel 

locomotive was positioned in the entry, B-Butt. The 

air quantity through B-Butt was about 15 m3

/s. For 

the three coal combustion experiments with diesel 

exhaust, the diesel locomotive was located upwind 

of the Room-10 split from B-Butt. In the first coal 

experiment, diesel exhaust was absent. For the three 

belt combustion experiments, the exhaust pipe from 

the locomotive was attached to a 10-cm-ID, 4-mlong pipe that directed the exhaust into Room 10 and 

downwind toward the fire source. Room 10 had an 

average height and width of 2.0 m and 3.9 m, respectively. Collections of duplicate fire sensors at 

two locations, stations 1 and 2, were placed 18 m 

and 148 m, respectively, downwind from the fire 

source in six of the experiments and shown as S1 

and S2 in figure 1. In the first coal experiment, when 

diesel exhaust was absent, only the data from the 

sensors at station 2 were used in a subsequent neural 

network analysis. The location of station 2 was in FButt, which had an average height and width of 1.9 

m and 4.5 m, respectively. For the experiments conducted, the average air quantity near the fire source 

was 2.66 m3

/s and at the end of F-Butt was 4.73 

m3

/s. The increase in air quantity downwind of the 

fire source was caused by leakage into F-Butt 

around cloth brattices blocking crosscuts connecting 

F-Butt with parallel airways. 

At each of the two stations, a point CO diffusionmode sensor was suspended from the roof with the 

inlet of the sensor 40 cm down from the roof. Concentration of CO was measured by this type of sensor in parts per million (ppm). A point, dieselengine-exhaust-gas, MOS sensor was also suspended from the roof with its inlet about the same 

distance down from the roof. It was found that the 

MOS sensor output voltage responded bi modally. 

The voltage increased above its clear-air value when 

oxidizable gases such as CO and organic compounds 

were present. When only diesel exhaust in air was 

present, the MOS sensor voltage decreased below its 

clear-air value. This decrease in voltage was in response to the nitrogen oxides in the diesel exhaust 

gas. In all seven experiments, the onset of smoldering combustion released enough oxidizable gas to 

cause the MOS voltages to increase above their 

clear-air average values. Two other types of MOS 

sensors, designed for hydrogen and carbon monoxide detection, were placed at both stations but were 

subsequently found to be inferior to the dieselexhaust MOS sensors in discriminating the dieselengine and flammable material POC. An opticalpath smoke-sensor assembly consisting of a pair of 

modules was placed approximately diagonally 

across the airway at each station with a path length 

between the modules of about 10 m. These modules 

consisted of an infrared emitter and an infrared collector mounted on two steel posts. The presence of 

infrared absorbing or scattering particles in the path 

of the infrared beam caused the sensor signal to decrease. An ionization smoke sensor placed at these 

two stations was also found to be inferior to the 

three sensors described above in discriminating the 

combustion of coal or conveyor belting from diesel 

exhaust. All the sensor data were collected from the 

sensors by a mine monitoring system above ground 

that polled the sensors every two seconds. These 

sensors and the mine monitoring system are more 

completely described in (Edwards et al. 2001). 

3 DESCRIPTION OF NEURAL-NETWORK 

 CLASSIFICATIONS 

A computer package of a collection of neural networks named NeuroSolutions, (reference to specific 

products does not imply endorsement by NIOSH), 

from NeuroDimension, Inc., was applied to sets of 

inputs derived from the sensor data. The multilayer, 

perceptron neural network (NN) from this package, 

described in some detail in (Principe et al. 2000), 

was found to yield the best discrimination between 

the diesel exhaust and hazardous combustion. This 

NN was comprised of a set of inputs, hidden layers 

of nodes called process elements (PEs), and an output layer of nodes representing the probabilities of 

the possible events. In order for the NN to discriminate between two combustion sources, the NN had 

to be trained on data similar to data that would be 

routinely collected from underground mine sensors. 

Two sets of experimental data at station one, one 

coal and the other belt smoldering in the presence of 

diesel-engine POC, were combined into one data file 

of 7,679 temporal samples that was used to train the 

NN. From trial-and-error calculations, it was determined that two hidden layers in the NN yielded the 

best discrimination with 10 PEs in the first hidden 

layer and 5 PEs in the second hidden layer. Each PE 
used a hyperbolic tangent (tanh) activation function 

that operated on the sum of inputs to the PE. The 

three output PEs used softmax activation functions–

functions that operated on the sum of inputs from 

the second hidden layer and classified outputs by assigning probabilities to each output. The output layer 

of the NN was divided into the three classifications–

clear air, diesel exhaust, and a combination of diesel-exhaust and hazardous-combustion products. A 

desired value for an output was a value of one for a 

correct classification and zero for each of the two 

other possibilities. A classification of the outputs 

from a sample of the inputs occurred when the softmax activation function applied to the NN outputs 

indicated that the probability of one of the outputs 

was greater than each of the probabilities of the two 

other outputs. During training 105 weights were varied by the NN. These weights were coefficients of 

the outputs from the PEs and were initialized randomly in a range that depended on the structure of 

the neural network. The weights were then varied to 

make the mean sum of the squares of the differences 

between desired outputs (zero or one) and predicted 

outputs, named the mean square error (MSE), approach a small value. Acceptable training, also determined by trial-and-error, occurred after as few as 

100 epochs or iterations through the entire training 

file. The exemplar (sample) weight changes were 

averaged over each epoch and the average weight 

changes were applied only after completion of the 

epoch. Changing the weights after each exemplar 

did not improve the classifications. The weight 

changes were calculated using a gradient descent 

method in the backpropagation-of-error part of the 

NN algorithm. A numerical term called momentum, 

having a default coefficient of 0.7, was added to the 

backpropagation expression for calculating each 

weight change. The momentum term used the value 

of the weight from the previous iteration to sometimes accelerate the convergence of the MSE. No 

compelling advantage was found for using other 

than the value of the default momentum coefficient. 

4 APPLICATION OF THE NEURAL NETWORK 

 TO DISCRIMINATIONS 

Criteria were established to determine boundaries 

between the clear air, the diesel exhaust, and the 

combined diesel exhaust and hazardous combustion 

data. Because the MOS voltage responded differently to diesel exhaust gas than to hazardous combustion gas with relatively little noise in its value, it 

was selected to separate the three periods. The mean 

and standard deviations of the output voltages from 

the MOS sensors were calculated for each of the periods of clear air at the sensors. When 10 standard 

deviations decreased from the mean of the MOSsensor voltage for more than two time-sampling increments, it was decided that the diesel exhaust had 

reached all the sensors at the station with the MOS 

sensor. If the noise superimposed on a constant signal is normally distributed, 10 standard deviations 

from the noise mean occurring within two consecutive samples of the signal would indicate almost certain confidence that the signal was being changed by 

something other than noise. 

 To determine the accuracy and responsiveness 

of the NN, the time interval between the time of arrival of hazardous POC at a station and the time the 

NN detected the hazardous POC was approximated. 

Since electrical heating of the flammable solids 

(coal or belting) occurred soon after the diesel locomotive was in position, the MOS sensors were affected by the thermal off gassing of volatile oxidizable compounds from the solids. This off gassing 

caused the MOS voltages to slowly increase before 

the start of combustion caused a more rapid rate of 

increase. This period of a slow rate of MOS voltage 

increase was often complicated by a slow decrease 

in the slope of the MOS voltage curve as the rate of 

emission of volatiles decreased. For these reasons, a 

visual decision was made on each set of MOS data 

when combustion was being sensed. The period between the time of visual indication of hazardous 

combustion in the MOS data and the time predicted 

by the NN computer algorithm from the 13 data sets 

ranged from 2.13 min to 9.67 min with the mean period being 5.57 min and the standard deviation of the 

periods being 2.51 min. 

After the boundaries were determined for the 

three temporal periods of each experiment, the MOS 

data were digitized to improve the NN classifications of the three periods. In the clear-air period, the 

MOS input to the NN was set to 0. In the dieselexhaust period or when the MOS voltage was less 

than ten standard deviations from the clear-air mean, 

the MOS input to the NN was set to -1. In the combination, diesel-exhaust and hazardous-combustion 

period or when the MOS voltage was more than ten 

standard deviations from the clear-air mean, the 

MOS input to the NN was set to 1. Only sensor data 

for the periods ranging from initial clear air to smoldering combustion were needed in the NN to discriminate the hazardous combustion from the diesel 

exhaust, since hazardous combustion detection was 

achieved in all of the data sets without considering 

the periods of flaming combustion. The total time 

intervals tested from each of the 13 data sets ranged 

from 1.36 hr to 2.21 hr with the mean time interval 

being 1.82 hr and the standard deviation of the time 

intervals being 0.32 hr. 

To remove the effect of the different initial values 

of the smoke sensors from experiment to experiment, the initial smoke sensor data was normalized 

to a mean value of one during the clear-air periods 
of each experiment yielding a nominal experimental 

signal range from zero to one. The clear-air, mean 

background concentration of CO was subtracted 

from the CO data to yield a nominal clear-air CO 

concentration of zero. The CO data, the processed 

data from two sensors (the digitized MOS and the

normalized, optical smoke sensors), and the product 

of the CO and the normalized smoke sensor data 

(COXSmoke in figures 3 to 7) were used as the NN 

input layer. The selection of the product of the CO 

and the normalized smoke sensor data was determined by trial-and-error from various combinations 

of the sensor data in order to minimize the time intervals from onset of combustion to hazardous combustion detection and the number of false alarms. 

The robustness of the classifications was demonstrated in one of the belt experiments when a mistaken increase of about 50% in the ventilation velocity occurred during the diesel idling period and just 

before combustion of the conveyor belt began. The 

only difference between this experiment and the 

other belt experiments was a smaller percentage of 

samples (56%) during the belt combustion period 

from station-2 data that were detected by the NN as

being belt combustion. Even with this relatively low 

percentage, when the belt combustion was detected, 

the probability was much above 0.5, the minimum 

certain-detection probability, and remained well 

above 0.5 for the rest of the test period. In other 

words, when the alarm for hazardous combustion

started, it remained in alarm until the test was terminated. This pattern of the NN alarm remaining in an 

alarm state during the rest of the test period occurred 

in all of the 13 tests. Also, since the probabilities 

were consistently less than a third, the minimum 

probability for a false alarm, during the initial periods of the experiments before hazardous combustion

began, no samples were detected as being hazardous

combustion during these periods or there were no 

false alarms. No false alarms occurred even though 

the diesel exhaust was not cleaned and during one of 

the tests the diesel exhaust produced a peak concentration of 38 ppm of CO at the station closest to the

diesel locomotive. Figure 2 shows a typical plot of 

the probabilities of detection of hazardous combustion during a coal experiment, the second coal experiment from station-1 data, and during a belt experiment, the third belt experiment from station-2 

data. The probabilities in figure 2 are representative 

of the probabilities generated from the other 11 test 

data sets–meaning the values ranged from less than 

0.2 initially to greater than 0.8 when the hazardous 

combustion was detected. 

The NN input variables from the coal experiment 

without diesel exhaust are shown in figure 3 as a 

function of time from the start of the experiment. 

The plot of the continuous MOS data, which was not 

used as an input, is shown only for comparison with 

the digitized MOS (dMOS) data. Sets of inputs from 

stations 1 and 2 for the third and second coal experiments with diesel exhaust are shown in figures 4 

and 5, respectively. The sets of inputs from stations 

1 and 2 for the third belt experiment are shown in 

figures 6 and 7, respectively. The times of the NN 

0

0.4

0.8

1.2

1.6

0 1000 2000 3000 4000 5000

Seconds

Signal

0

2

4

6

8

CO, ppm

MOS

S moke

COXS moke

CO

Dig itized

MOS NN Alarm

Figure 3. Inputs to the NN from station-2 data from the first

coal fire without diesel exhaust. 

-1

0

1

2

3

4

5

6

0 1000 2000 3000 4000 5000 6000 7000

Seconds

Signal

-4

0

4

8

12

16

20

24

CO, ppm

MOS

CO

COXSmoke

S moke

NN Ala rm

Digitize d MOS

0

0.2

0.4

0.6

0.8

1

0 1000 2000 3000 4000 5000 6000 7000 8000

Seconds

Probability

S econ d Coal

Fire at S tation 1

Th ird Belt 

Fire at

S tation 2

Figure 4. Inputs to the NN from station-1 data from the third

coal fire with diesel exhaust. 

Figure 2. Probability of hazardous combustion from second

coal fire at station 1 and third belt fire at station 2. 
predicted fire alarm are indicated as vertical lines on 

the figures. When the dMOS data responded to the 

hazardous combustion, a temporary plateau formed

as the data returned to within the range of ten standard deviations from the clear-air mean. Even with 

these temporary, clear-air, digitized-MOS values of 

0, the responses of the other sensors together were 

sufficient to prevent the NN from classifying as 

clear air these small time intervals. 

All of the 13 sets of data collected were tested by 

the trained neural network–which also included the 

two data sets constituting the training file. A combined total of 10,359 temporal samples was collected from the sensors for the periods when clear 

air occurred. During these periods, all samples 

within the data sets were correctly classified as being from clear air. A combined total of 17,185 temporal samples was collected from the sensors for the 

12 periods when only diesel exhaust and air were 

flowing over the sensors. During these periods, all 

samples within the data sets were correctly classified 

as being from a diesel-exhaust-air mixture. The main 

reason that no diesel exhaust data were classified as 

clear air in all 12 data sets was because the change 

in the dMOS values dominated the other sensor 

changes at the start of the detection of the diesel exhaust. A somewhat similar effect occurred later in

the experiments. At the time the dMOS values temporarily returned to a value of 0, the NN signaled 

hazardous combustion in all 12 of the diesel tests. 

The period between the first temporary dMOS value

of 0 and the value of 1 when the MOS sensor detected hazardous combustion from these 12 data sets 

ranged from 32 s to 210 s with the mean period being 88 s and the standard deviation of the periods 

being 53 s. If the heating rate had been slower, these

periods would have been larger. More volatile oxidizable gas would have been dissipated over a longer 

heating period causing the time interval from smoldering combustion to flaming to also increase. 

Without the CO and smoke sensors, the MOS sensor 

would not have detected the smoldering combustion 

until later with the lag in detection depending on the 

rate of heating of the flammable material. In the first 

coal test, without diesel exhaust, only at the time the 

dMOS values became 1 did the NN signal a coal 

fire. A combined total of 12,485 temporal samples 

was collected from the sensors for the 12 periods 

when air mixed with diesel-exhaust and hazardouscombustion products was flowing over the sensors. 

During all of these latter periods, percentages ranging from 5% to 44% of the samples were classified 

initially as being from diesel exhaust with the mean 

percentage being 18% and the standard deviation of 

the percentages being 11%. The NN classified the 

first 6% of 2,509 temporal data samples from the 

hazardous combustion period of the first coal experiment as being from clear air. 

-2

0

2

4

6

0 1000 2000 3000 4000 5000

Seconds

Signal

CO

COXSmoke

MOS

Smoke

Digitized MOS

NN Alarm

-1

0

1

2

3

4

5

6

7

0 1000 2000 3000 4000 5000 6000 7000 8000

Seconds

Signal

CO

COXS moke

MOS

S moke

NN Alarm

Dig itized MOS

Figure 5. Inputs to the NN from station-2 data from the

second coal fire with diesel exhaust. 

Figure 7. Inputs to the NN from station-2 data from the third

belt fire with diesel exhaust. 

-1

0

1

2

3

0 1000 2000 3000 4000 5000 6000 7000

Seconds

Signal

0

5

10

15

20

CO, pp

m

MOS

CO

S moke

COXS moke

Dig itized MOS

NN Alarm

Figure 6. Inputs to the NN from station-1 data from the

third belt fire with diesel exhaust. 
5 CONCLUSIONS 

1. A two-layer, perceptron neural network (NN) 

with 10 process elements in the first layer and 5 

process elements in the second layer was superior to other neural networks investigated at discriminating diesel exhaust from coal and belt 

smoldering combustion without any false alarms. 

2. Digitized MOS-sensor data, CO-sensor data, optical-path smoke-sensor data, and the product of 

the CO and smoke-sensor data were the best inputs found for the discriminations. 

3. All clear-air and diesel-exhaust data were recognized by the NN from 13 sets of inputs from four 

coal and three belt combustion experiments with 

two collections of sensors 18 m and 148 m from 

the fire sources. 

4. Smoldering combustion was detected by the NN 

within 9.67 min from the first visual indication 

from the MOS sensor at a station that hazardous 

combustion products were present for all of the 

13 sets of inputs. 

5. The applicability of a neural network to the discrimination of hazardous underground-mine 

combustion from diesel-engine exhaust was determined to be viable. 

REFERENCES 

Brinn M and Bott B [1994]. “A fresh approach to mine fire 

detection,” Mining Engineer (London), v. 154, n. 396, pp. 

71-74. 

Edwards JC, Friel, GF, Franks RA, Lazzara CP, and Opferman 

 JJ [2000]. “Mine fire source discrimination using fire sensors and neural network analysis,” In: Proceedings of the 

2000 Technical Meeting of Central States Section of The 

Combustion Institute. Indianapolis, IN, pp. 207-211. 

Edwards JC, Franks RA, Friel GF, Lazzara CP, and Opferman 

 JJ [2001]. “Discriminatory mine fire source detection,” 

Seventh International Mine Ventilation Congress, Cracow, 

Poland, June 17-22. 

Principe JC, Euliano NR, and Lefebvre WC [2000]. “Neural 

 and adaptive systems: fundamentals through simulations,” 

John Wiley & Sons, NY.