In-mine evaluation of smart 

mine fire sensor

R.A. Franks, G.F. Friel, J.C. Edwards and A.C. Smith

Research electronics engineer, research chemical engineer (retired), research physicist (retired) and 

supervisory research chemist, respectively, Pittsburgh Research Laboratory, National Institute for 

Occupational Safety and Health (NIOSH), Pittsburgh, Pennsylvania

Abstract

An evaluation of a nuisance-emissions-discriminating smart mine fire sensor system was made in an 

operating coal mine. These field evaluations were conducted to determine the sensor system’s ability to 

discern nuisance emissions, such as diesel exhaust, emissions from flame cutting and welding operations, 

or hydrogen gas from a charging station, from real fires and to compare the number of falsely reported 

fire alarms generated between the sensor system and a standard carbon monoxide (CO) monitor. The 

sensor system’s ability to operate successfully in the working environment of an operating coal mine 

was also evaluated. The smart mine fire sensor system consisted of four sensors whose data outputs were 

fused with the use of a neural-network-type computer program. Long-term trials were conducted in a 

haulageway, a belt entry, and a track entry. The system functioned successfully in the belt entry, in accordance with its developmental goals, where the sensor system even discriminated events not anticipated 

during development. It was only partially effective in the haulageway and track entry, though, due to a 

combination of significant diurnal air temperature variations, dust, and mechanically induced vibrations. 

Also, deteriorating rib conditions contributed to operational problems in the haulageway evaluation. In 

general, the smart mine fire sensor provided nuisance emissions discrimination and was shown to be an 

interesting new approach for mine atmospheric monitoring, and possibly a new method for enhancing 

miner safety. This paper describes the in-mine evaluation of the smart mine fire sensor system and discusses 

recommendations for improving the system.

Introduction

Typically, mines use fire protection monitoring systems that 

depend on carbon monoxide (CO) electrochemical cell sensors to detect fires. These devices have a cross sensitivity to a 

number of other gasses that are not necessarily from fires, such 

as hydrogen produced by lead acid battery charging, which 

when produced in sufficient quantities cause false responses 

in this type of sensor. Beltline drive assemblies have been 

known to produce large amounts of smoke from dangerous 

friction-producing stall conditions that generate very little CO 

and, therefore, are not detected by this type of sensor. Flame 

cutting and welding operations in coal mines have been registered by electrochemical CO sensors as a fire alarm, a key 

issue. Also, CO emissions from diesel engine exhaust from all 

types of diesel-powered underground mining equipment have 

generated false fire alarms. The only alternative would be to 

increase the ambient alarm level, which would be dangerous. 

These situations have lead to a need from the mining community for mine monitoring systems that will more precisely 

register the nature of the combustion event in question, that 

is, a fire signature.

Previous NIOSH research identified four sensors that, when 

used in conjunction with a neural-network (NN) software, 

could discriminate a mine fire from nuisance combustion 

emissions (Edwards et al., 2001). These sensors were a carbon 

monoxide (CO) sensor;1 two metal oxide semiconductor sensors (MOS), both of which responded to hydrocarbons with a 

decreased sensor resistance,2 but the second MOS sensor also 

responded to nitric oxide (NO) from diesel engine emissions 

with an increased sensor resistance;3 and an infrared open-path 

smoke sensor (Beam4). (Reference to a specific product does 

not imply endorsement by NIOSH.) 

1 Conspec Controls Inc. P2030KP Carbon Monoxide Monitor.

2 Figaro USA TGS-2600 metal oxide semiconductor sensor. 

3 Figaro USA TGS-2105 metal oxide semiconductor sensor.

4 Detection Systems, Inc Beam Smoke Detector, DS-240 (now, 

Bosch Security Systems model D-296). 5 Microsoft Office 2003 Excel Spreadsheet with Visual Basic

for Applications Macro Language from Microsoft, Redmond, 

Washington.

The neural network program, NeuroSolutions, was acquired 

from NeuroDimension Inc. An MS Office Excel5 spreadsheet 

was configured with a Visual Basic (VB) macro that implemented the NN algorithm to provide a real-time evaluation 

of fire probability. The NN algorithm classifies three outputs 

into probabilities that each outcome would occur. The output 
possibilities were clean air, only diesel engine exhaust in 

air and combustion products with or without diesel engine 

exhaust in air.

The neural network (calibration). This sensor system was 

evaluated in fire experiments in the presence of diesel emissions 

in the Safety Research Coal Mine (SRCM) at the National Institute for Occupational Safety and Health (NIOSH), Pittsburgh 

Research Laboratory (PRL) (Edwards et al., 2003, 2004) to 

obtain fire signatures from various materials burned with and 

without the presence of diesel engine exhaust. The materials 

included coal, diesel fuel, electrical cable and conveyer belt. 

Flame cutting and welding experiments were also conducted 

at that time to evaluate the sensor system’s response to these 

mining activities (Edwards et al., 2000). In addition, the smart 

fire sensor system was evaluated in an above-ground batterypowered mine locomotive charging building at PRL (Edwards 

et al., 2001) and at a battery-powered equipment charging 

station at the mine of Foundation Coal affiliate Emerald Coal 

Resources, LP in Greene County, Pennsylvania (Edwards 

et al., 2002). The system was then installed at the mine of 

Foundation Coal affiliate Cumberland Coal Resources, LP in 

Greene County, Pennsylvania, to determine the capability of 

the neural network for discriminating against false alarms in an 

operating underground coal mine environment and to evaluate 

its durability and the reliability of its operational systems. This 

paper discusses the results of this last evaluation and makes 

recommendations as to how the smart mine fire sensor system 

can be enhanced for optimum performance.

Smart mine fire sensor system installation at 

Cumberland Mine

The system was installed at the Cumberland Mine in Greene 

County, Pennsylvania, and operated from Sept. 17, 2004, until 

June 29, 2006, a total of more than 21 months. The system 

ran almost continuously except for short periods of shutdown 

for data collection, usually less than an hour, once every two 

weeks. The monitoring system interrogated the sensors once 

every minute, not too rapidly to inundate the system with large 

amounts of collected data in the long-term, and yet sufficiently 

rapid to keep up with sensor changes. A 30-minute daily average 

of the sensors’ background signals was collected from 1:00 to 

1:30 AM each day to account for daily changes in the mine’s 

ambient environment.

The 30-minute daily average processing consisted of the 

testing of a small group of data (30 one-minute samples) for 

statistical noisiness and, if acceptably quiet, used the group to 

calculate the group’s mean and standard deviation (SD). The 

calibration was acceptable if no excursions in sensor response 

were found. If excursions were found, then a subsequent 

30-minute calibration was automatically initiated. The mean 

and SD were then used to calculate a normalized sensor value 

from each subsequent sampled sensor value. This normalization 

of the data enabled trends from the mine’s usual conditions to 

be more easily detected. The normal values were then condition tested before the NN calculation for such events as sensor 

electronic drift and equipment or people blocking the Beam 

sensor’s IR path to avoid false alarms.

To implement the NN algorithm, two data functions derived 

from the four sensor inputs together with three of the sensor 

inputs were used as the five inputs into a trained, two-hiddenlayer perceptron neural network. Twenty process elements (PEs) 

were in the first hidden layer and ten PEs were in the second 

hidden layer. The NN was trained over the default number of 

1,000 iterations (epochs) of the training set consisting of seven 

sets of data from various materials burned in the SRCM with 

and without the presence of diesel engine exhaust. A total of 

13,190 rows of data collected at two-second intervals and 

randomly sorted were present in the training data set. The 

NN training convergence criterion was the mean squared error. The momentum learning rule was used to determine the 

change of the weights in the NN after each row of input data 

(exemplar) was processed. The momentum constant used was 

the default value of 0.7. The weights were only changed after 

each epoch, and these changes were composed of the means 

of the calculated weight changes after each exemplar in the 

epoch. The activation function in each PE of the hidden layers 

was the hyperbolic tangent function. The activation function 

in each PE of the output layer was the SoftMax function. The 

SoftMax function classified three outputs into probabilities that 

each outcome would occur given a particular set of the five 

inputs. These outcomes are the output possibilities enumerated 

in the Introduction. 

During the course of the evaluations, the sensors were maintained within operating specifications. The CO and CO-NO 

sensors were calibrated at 30-day intervals. Electrical resistances in the MOS sensors were adjusted to provide ambient 

output signals indicative of a fire products-of-combustion-free 

environment. The MOS sensors were replaced when needed 

as their surface elements lost sensitivity. The Beam sensor 

was reset to provide automatic calibration during each mine 

visit. At the conclusion of the evaluation of the smart sensor 

system in Cumberland Mine, electrical continuity checks 

were made on the signal and power lines from the PC to the 

sensors. No significant unwarranted resistance values in the 

lines were detected.

The Smart Mine Fire Sensor System was initially installed 

in the haulageway during this evaluation. The MOS sensor unit 

containing the two MOS sensors and the CO sensor unit along 

with a Conspec Diesel Discriminator6 and a Becon Smoke 

Detector7 were spread across the haulage entry and attached to 

the entry roof by a bar. These last two sensor units were used 

on the NIOSH mine monitoring system, but were not part of 

the Smart Sensor set. The Beam smoke sensor transmitter unit 

was located on the rib near the MOS sensor unit. The Beam 

receiver was located on the opposite rib 9 m (30 ft) down the 

haulageway from the transmitter unit. Signal transmission 

and power lines were connected between the sensors and a 

Conspec Senturion 500 mine monitoring system at a surface 

station via a Conspec mine trunk cable.8 Linear airflow in the 

haulageway was usually about 1.8 m/s (350 fpm). There were 

significant temperature variations in the haulageway, ranging 

from 7° to 29°C (20° to 85oF), due to the proximity of the 

sensors’ location to the intake air shaft. The evaluation of the 

system in the haulageway lasted for 14 months.

The equipment was then moved into the beltline entry, 

which was in an adjacent entry to the haulageway. The objective of the move was to place the sensors in an environment 

more representative of where their location would be for 

normal in-mine use. In the belt entry the linear air velocity 

in the vicinity of the sensors was approximately 1.2 m/s (230 

fpm). The location was two miles from the intake, resulting in 

a fairly constant temperature of approximately 16°C (60°F). 

The ambient temperature was elevated due to the additional 

heat from the beltline equipment. 

6 Conspec Controls Inc. P2512 Diesel Discriminating Monitor.

7 Anglo American Electronics Laboratory C121B Becon Ionisa

tion Smoke Detector. 8 The mine monitoring system used was a Conspec Controls, 

Inc. S500 Senturion Super-system.
Lastly, the system was moved to a track 

entry in the mine’s shaft-bottom area (the 

north track area) for two months. The track 

entry was the second entry toward the 

elevator shaft from the main haulageway. 

This area is where miners normally departed from at the beginning of shift work 

and returned to at the end of shift work 

by motorized equipment, mainly diesel 

man-trips. The track entry did not have the 

intensity of the vehicular traffic observed 

in the haulageway. The entry had approximately the same linear air velocity as in the 

haulageway, i.e., 1.8 m/s (350 fpm). The 

entry was about the same distance from the 

intake air as the haulageway, consequently 

having the same relative air temperature 

variations.

Evaluation of durability, functionality and reliability

Drift. The Smart Fire Sensor System experienced significant drift problems with the 

Beam Smoke Sensor unit when the system 

was installed into the main haulageway at 

Cumberland mine. The drift was attributed 

to a combination of three or four persistent 

problems; loosening of screws in the Beam 

unit’s interface assembly due to constant 

vibration from equipment moving in the 

haulageway, misalignment of the Beam 

transmitter unit due to the deterioration of 

the rib to which it was mounted, periodic 

build-up of dust on the lens assembly from 

the high air-flow and temperature fluctuations in the haulageway. No one specific 

cause was determined to be the reason for 

the drift. An example of the drift over a 

20-day period is shown in Fig. 1.

Figure 1 — Beam sensor response in the main haulageway.

Temperature effects. Diurnal temperature 

effects on the Beam and MOS2105 sensors 

were observed in the haulageway. This temperature change was due to the proximity of 

the haulageway sensor station to the intake 

air. An example of the close correlation of 

the periodic fluctuations in signal level of 

the Beam sensor with a similar change in 

ambient air temperature in the haulageway is shown in Fig. 2

for a 7-day period. The maximum value occurred at about 6:00 

PM and the minimum value occurred at about 7:00 AM on a 

recurring daily basis. These times corresponded to maximum 

and minimum outside air temperatures.

Figure 2 — Beam and temperature responses.

In an attempt to correct this temperature response problem, 

foam insulation was placed around the open perimeter of the 

Beam housing to isolate the electronics from the mine air and 

prevent convective cooling. However, this did not eliminate 

the problem. The MOS2105 sensor also showed a voltage 

drop due to severe winter temperatures, but recovered well 

whenever the low temperature conditions abated. 

In the Emerald Mine charger entry, the Beam sensor’s 

output temperature dependence did not manifest itself, as 

was also the case in all of the SRCM tests prior to the Cumberland evaluation, but this was under more stable temperature conditions. The sensors were located in a split from the 

mine’s electric-vehicle charger entry. In this evaluation, the 

temperature was not recorded by the mine monitoring system 

as it was at Cumberland Mine. Also, at the sensors’ location 

in the split the air velocity had a relatively low value of about 

0.2 m/s (40 fpm).

An attempt was made to determine the exact dependence 

of the Beam’s output upon ambient temperature by an evaluation of the correlation between the two parameters for a stable 

haulageway data set. For a 20-day period, the dependence of 

the measured Beam output signal on the temperature resulted 

in a fit of the Beam signal to a power of the air temperature 

measurement, as follows

PREDICTED_BEAM = T0.329 (1)

where 

T is the measured temperature in oF and 

PREDICTED_BEAM is the predicted Beam signal in volts. 
The correlation coefficient is 0.62, which defines a moderate 

correlation. Figure 3 indicates that the period of the measured 

Beam oscillations is well represented by the PREDICTED_

BEAM value, but the amplitudes are less well represented.

Figure 3 — Measured and predicted beam signals, based on ambient temperature.

It should be noted that this correlation applies to this particular set of data and for which there was no long-term downward 

drift in the Beam output signal. Any modifications to the smart 

sensor unit as imposed temperature compensation would require 

the addition of a temperature measurement device to the smart 

fire sensor set and that a temperature compensation algorithm 

would need to be applied to the data normalization process.

Dust problems.Because the Beam smoke detector is an optical 

device, the Beam had a persistent problem with dust accumulation on its lens surfaces and the unit was indiscriminant in its 

response to the various types of particulate 

matter that were dispersed into its optical 

path. The effect of rock dust on the Beam 

sensor had been investigated in the SRCM. 

In the SRCM rock dust was hand-dispersed, 

at the entry’s mid-height, upwind from the 

Beam sensor unit. The Beam sensor responses in the SRCM evaluations are shown 

in Fig. 4. The airflow was maintained at 1.3 

m/s (250 fpm) during these evaluations.

Figure 4 — The beam affected by rock dust in the SRCM.

It can be seen in Fig. 4 that the Beam 

recovered to its pre-rock-dusting value 

after the last intense injection of rock dust 

into the air stream. Two differences were 

possible from the perspective of rock-dust 

deposition between the SRCM and the inmine evaluations. One possible difference 

was the rate of deposition of rock dust on 

the Beam lens cover. In the case of the inmine evaluation, the deposition rate would 

be gradual. A gradual deposition rate might 

not be detectable by the logic inherent 

to the Beam sensor. Another possibility 

could be the presence of diesel combustion byproducts that could accumulate 

as a film on the Beam’s plastic surface to 

which the dust could adhere. During the 

evaluation, it was also observed that rock 

dust periodically accumulated on the Beam 

lenses. The Beam lenses were cleaned and 

the unit reset to restore the Beam to its 

normal value of 4 volts.

In the north track area, dust and diesel 

particulate deposits built up on the Beam’s 

transmitter unit causing the Beam’s output to 

drift down over time by as much as 1 volt. 

The Beam’s transmitter was well aligned 

with the end of the exhaust pipe on diesel 

man-trips when they parked in this area. 

This area would be subjected to diesel exhaust from these diesel-particulate-emitting 

vehicles during each shift change. The mantrips not only dispersed dust as they entered 

and exited the parking area, but the miners 

disembarking and entering the man-trips 

also contributed to dust dispersion. When 

the Beam was cleaned, the unit returned to 

its normal ambient output voltage without 

the necessity of resetting the unit.

In the main haulageway the Beam had 

some dust build up from heavily laden haulage trains and exposure to diesel exhaust because the location of this evaluation 

was at the entry switch to the mine bottom where the shops 

and crew parking areas were located. The difference between 

the main haulageway location and the north track location 

was that the Beam was mounted higher up on the rib in the 

haulageway and any man-trips entering or leaving the bottom 

never idled there for more than a minute or so before moving 

along the haulageway. These differences provided for a lighter 

rock-dust-laden buildup on the transmitter and receiver units of 

the Beam in the main haulageway than in the north track entry. 

However, even with a lighter dust build-up, the Beam unit in 

the haulageway would not return to its normal 4.0 volt clear air 

operation after a lens cleaning and always required a reset to 

return it to normal operation. This was a good indication that a 
gradual misalignment process was at work 

in the haulageway on the Beam mountings, and that vibrational loosening of the 

terminal-strip screws was occurring.

Vibration effects.In an attempt to address 

the downward drift of the Beam’s output 

signal in the environment of the main 

haulageway, the screw connections at the 

terminal strips in the Beam’s interface box 

were tightened, and the unit was returned to 

service. The results of the evaluation for a 

20-day period showing the stability of the 

Beam output signal are depicted in Fig. 5. 

This would indicate vibrations induced in 

the Beam’s interface box was a significant 

factor in the long-term evaluation of the 

smart fire sensor in the haulageway.

It should be noted that Fig. 5 shows an 

isolated fire probability of 0.8 that occurred 

on Day 7. However, during that time only a 

small decrease in the Beam output voltage 

occurred, so the NN does not indicate an 

alarm event.

Figure 5 — Sensor responses and predicted fire probability in the haulageway.

Ground movement effects. Spalling of the haulageway rib 

to which the Beam’s transmitter unit was attached also caused 

alignment problems with the Beam detector. In a simple lab 

experiment, with the Beam set up along an approximate 1.8-m 

(6-ft) path, the unit showed a significant voltage loss in the 

receiver output with slight lateral movement of one corner of 

the receiver’s mounting-plate in a direction perpendicular to 

the optical path. Although the same displacement on the 9-m 

(30-ft) path-length in the mine would require five times the 

movement, the results indicated that a continual slow movement 

of the rib mounting caused by the working rib would produce 

a continual voltage drop in the Beam. The Beam transmitter 

mounting supports on the main haulageway rib eventually 

collapsed at some point in time after the Beam unit had been 

removed from the haulageway.

Evaluation of sensor ability to discern false 

alarms

The sampled data at Cumberland Mine were recorded every 

minute on the surface mine-site monitoring system PC and the 

fire probability was calculated with the NN software in real 

time using the algorithm derived from data collected from 

previous experiments in the SRCM. After initial mine-site 

installation, site-specific modifications were required to the 

algorithm related to the calculation of the daily averages of the 

sensor ambient values and the criterion for an alarm. 

To be acceptable data for a daily average calculation: 

• the Beam sensor output voltage was required to be greater 

than 3.7 volts and less than 4.3 volts; 

• the CO sensor output deviation from its mean had to be 

less than 1.5 times its standard deviation; and 

• for the MOS sensors and Beam sensor, the ratio of the 

signal (mean) to noise (standard deviation) had to be 

greater than 5. 

If any set of sensor values did not conform to the above 

constraints, another set of 30 one-minute scans would be collected and tested until the constraints were satisfied. Once the 

daily averages were determined, all data were normalized by 

dividing each value by its associated daily average. A very high 

probability fire alarm (HH) required that the NN predicted fire 

probability be greater than 0.9 and the normalized Beam data 

be less than 0.8. Blockage of the Beam sensor optical path by 

an object occurs often. To exclude a blockage as a fire event 

by the NN program required the provisional evaluation of the 

Beam, the CO sensor, and the MOS2105 sensor. If the normalized Beam deviation was greater than ten standard deviations 

(SD), the normalized CO deviation was less than one and one 

half SD and the normalized MOS2105 increase was less than 

ten SD, then the event was considered to be a blockage of the 

Beam path by an object and not a fire event. If the event met 

the conditions for a blockage, then the fire probability was 

not calculated. 

In-mine verification of the NN program modifications — 

rail burn. For validation purposes, the new modifications of 

the NN program were applied to the data from the in-mine 

rail-cutting operation performed at the Emerald Mine as a 

flame cutting experiment. The sensor measurements are shown 

in Fig. 6. No HH alarms resulted. Figure 6 shows that there 

were CO values greater than the 10-ppm alarm level. Although 

the fire probability exceeded 0.9 during the burn, the normalized Beam values were greater than 0.8. As noted above, the 

criterion for an HH alarm required both the fire probability to 

exceed 0.9 and the normalized Beam values to be less than 

0.8. This demonstrated the nuisance discrimination capability 

of the smart sensor system.

Figure 7 shows the identification of a typical nuisance emission in the haulageway during the evaluation that shows the 

MOS2105 sensor responded simultaneously with the NO and 

CO sensors. The responses by the CO and NO sensors were 

less than 2 ppm. However, the absence of the Beam sensor’s 

response is indicative of an absence of optical obscuration. The 

predicted fire probability from the NN algorithm was less than 

0.11. Although the emission source was not known, it most 

likely was a diesel engine that produced insignificant smoke 

obscuration compared to a fire.

An example of the Smart Sensor’s ability to discern a false 

alarm in a belt entry is shown in Fig. 8. In this case, given the 
response of the MOS2105 to NO and a small response of the 

NO chemical cell sensor to this event, the speculation was 

that a diesel-powered scoop operation was in progress in the 

belt entry to remove coal spillage upwind from the sensors, 

which resulted in dust-laden airflow. Another event of the type 

depicted in Fig. 8 occurred in the belt entry later in the evaluation. Figure 9 shows the sensors’ outputs. This resulted in one 

HH alarm. Figure 9 also shows a small increase in the CO at 

the time of the HH alarm. The broken fire probability curve 

in Figs. 8 and 9 is a consequence of the Beam sensor response 

being more than ten standard deviations below its mean value 

and the MOS2105 sensor response increase being less than ten 

standard deviations above its mean value. In this case there is 

a source other than smoke, such as optical path blockage by 

equipment or dust, which induces the Beam sensor response. 

When this occurs, the fire probability is not 

calculated and no alarm is given. 

Figure 6 — CO and beam responses, and predicted fire probability

Figure 7 — Sensor responses in the haulage way at Cumberland.

Another case for which the fire probability cannot be calculated is when a sensor or 

communication link fails. In these cases, the 

mine monitoring system produces textual 

error statements. The algorithm does not 

process these textual errors.

The Smart Sensor’s discriminating capability was compared to that of a diesel 

discriminating monitor currently in use in 

mines during the evaluation in the track 

entry, shown in Fig. 10. During this event, 

the CO response from the CO-NO sensor 

(ACO) was 11 ppm for the measured CO 

concentration. The NO sensor response was 

20 ppm. In this figure, ACO refers to the 

actual CO response of the CO-NO sensor. 

CCO refers to the corrected CO response 

read by the CO-NO sensor based upon the 

CO and NO historical record. The CO-NO 

sensor identified this event as a non-alarm 

with a base line CCO value equal to 0.98 

ppm. The fire probability identified with the 

NN evaluation from the smart fire sensors 

was 0.14, and the diesel emissions probability identified with the NN evaluation was 

0.24. This demonstrates a consistency of 

the smart fire sensor system with the diesel 

discriminator unit’s evaluation.

A comparison was made of the response 

of the Smart Fire Sensor system with CO 

alert and alarm signals at the measurement 

sites. During the 21-month evaluation period of the Smart Mine Fire Sensor system 

in the Cumberland mine there were 47 CO 

alert or alarm events that were unrelated 

to a fire as summarized in Table 1. The 

most likely CO sources were operating 

diesel equipment. Eighteen of these events 

occurred in the haulageway, and 29 of these 

events occurred in the north track area. There 

were no CO alerts or alarms in the belt 

entry. The Smart Mine Fire Sensor system 

correctly identified five of the CO events 

in the haulageway as non-fire events. The 

other thirteen events could not be identified due to sensor system malfunction. In 

the north track area, 27 CO events were 

correctly identified by the Smart Mine Fire 

Sensor system as non-fire events. Two additional CO events in 

the north track area occurred during the system setup period 

and consequently were not amenable to analysis.

It should be noted that the stability of the Beam over many 

of the two-week sampling periods in the belt entry, as demonstrated by the stability of the collected data from the belt 

evaluation, was due to the stable belt entry air conditions with 

the exceptions of the coal cleanup activities. This indicates a 

single average of the sensor values during one 30-minute period 

at the beginning of the two-week sensor evaluation in the belt 

entry could be sufficient. By way of example, an evaluation 

with the NN program with a single 30-minute average was made 

for the belt entry data from the time period March 9, 2006, to 

March 23, 2006. As with the previous daily average evaluations, 

there were no HH alarms. The maximum fire probability for 
the single average NN calculation for the 

sensors was 0.42. This is to be contrasted 

with the maximum fire probability of 0.49 

when a daily average was computed.

However, the aforementioned temperature, vibration and ground movement 

effects, which occurred in the haulageway, 

resulted in a significant loss of data acceptable for application of the NN system. The 

primary interference effect in the north track 

area was dust. The loss of the NN system 

evaluation capability occurred both as a 

consequence of the sensor drift beyond its 

normal acceptable reporting range during 

the automated data collection and in the determination of sensor ambient background 

values. In both the haulageway and the 

north track area excessive 30-minute time 

periods were required for the sensors to 

reach an acceptable low level of statistical 

noisiness to be accepted by the NN system 

as ambient background values. This effect 

screened out considerable sensor data from 

NN evaluation, and made the NN system 

ineffective for application during those 

periods. Except for one period of time, the 

determination of the ambient background 

values was a minimal problem in the belt 

entry evaluation.

Figure 8 — Sensor responses and predicted fire probability in belt entry.

Figure 9 — Sensor responses and fire probability in the belt entry.

Table 1 — Alert and alarm summary during evaluation.

 Correctly 

 Entry identified 

 location diesel event Malfunction Total events

 Haulage 5 13 18

 Belt 0 0 0

 Track 27 2 29

 Total 32 15 47

Conclusions and recommendations

As with any initial in-mine evaluation 

of new technology, several issues were 

brought to light. The in-mine, smart mine 

fire sensor evaluation identified significant 

problems with the Beam sensor due to temperature variations, mounting instability 

and mechanical vibrations in the haulageway area. In the track entry, only dust was 

a persistent problem. It should be noted, 

except for one event during an extended 

period of malfunction, that during the CO 

Alert or Alarm events, the Smart Mine Sensor System did not register any HH Alarms 

that would have indicated a false alarm 

by the system. Either the system was not 

processing data properly or if processing 

data properly, it did not indicate an alarm 

during any of the CO events. 

In the belt entry with a less variable 

ambient temperature, the performance of the smart mine-fire 

sensors showed significant improvement and demonstrated 

an important method for mine fire detection with diesel 

emissions, dust and fire discrimination. The in-mine evaluations with iterative improvements in the NN software have 

provided guidance for future improvements in the hardware 

and software and demonstrated that the Smart Mine Fire 

Sensor System could possibly provide a viable method for 

preventing nuisance alarms, thereby, increasing mine safety. 

The multi-sensor mine fire detection system with a trained 

neural network program to provide fire source discrimination 

could be a viable approach for enhancing miner safety.

The following specific recommendations were derived 

from the in-mine evaluation for improvement in the next 
Figure 10 — Sensor responses and predicted fire probability.

generation Smart Mine Fire System:

• The connections within the electronics interfaces should 

be mechanically hardened to exclude vibration-induced 

mechanical loosening.

• A correlation needs to be developed between Beam 

sensor output and ambient air temperature. Temperature 

compensation should be introduced into the NN program. 

This would be useful for field evaluation. For long-term 

in-mine use, the device temperature could be stabilized 

with a heated enclosure. Such a hardware modification 

would directly correct the signal uncertainty.

• The MOS sensors should be temperature compensated.

• Site evaluation should be made specifically for mounting 

stability of the Beam optical units.

• A mechanical design that would reduce the deposition of 

dust on the lens of the Beam should be determined. The 

dust shield design could be simplified and exclude the 

need for the optical path to pass through the shield by 

configuring the Beam with the optical path perpendicular to the axis of the mine entry. In simple lab tests, the 

Beam has worked with optical path lengths as short as 

1.8 m (6 ft), although the unit has never been subjected 

to smoke at these path lengths. Using the Beam with a 

perpendicular optical path in most mine entries would 

reduce the path length to less than two-third the minimum 

path length recommended by the manufacturer. This 

would affect the unit’s sensitivity to smoke because the 

optical attenuation is a function of the product of the 

optical path length and the smoke concentration. As an 

alternative, a reflector could be mounted on one rib. The 

Beam transmitter and receiver would be located on the 

opposite rib. This would double the optical path, and 

thereby maintain the sensitivity of the Beam to small 

smoke concentrations. It would be possible to maintain

the receiver and transmitter nearly 

perpendicular to the rib. An extended 

shroud around the transmitter and 

receiver would prevent airborne dust 

accumulations.

• The sensors should be packaged into a 

compact unit.

• The daily averaging of the sensor 

ambient values could be replaced by 

a biweekly, or longer, averaging if the 

sensors are used in a belt entry or mine 

area with relatively stable temperature 

conditions.

• The software should be modified to report 

the presence of significant airborne dust 

concentration, as a counterpoint to smoke 

particulates, in a belt entry through an 

interpretation of the sensors’ outputs.

The first five recommendations above 

are in response to problems encountered 

in entries with significantly changing air 

temperature, mining equipment induced 

vibrations and spalling of the mine ribs.

Disclaimer

The findings and conclusions in this report are those of the 

authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health.

Acknowledgments

The authors acknowledge the Foundation Coal Corporation and 

its affiliates for providing access to their mine and personnel 

for assistance, without which the sensor evaluations could not 

have been conducted. Particular thanks are extended to John 

Gallick, Robert Bohach, Joseph Culp and Grant Colbert, who 

facilitated the project and the miners who assisted us with the 

in-mine experimental setup. Conspec Controls Inc. provided 

additional support through the loan of a Conspec Senturion 

500 mine monitoring system for data acquisition.

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