development of a neuro fuzzy model for noise prediction in
TRANSCRIPT
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International Journal of Uncertainty,Fuzziness and Knowledge-Based SystemsVol. 17, No. 5 (2009) 729–745c© World Scientific Publishing Company
DEVELOPMENT OF A NEURO FUZZY MODEL FOR NOISEPREDICTION IN OPENCAST MINES
SANTOSH KUMAR NANDA∗,† and DEBI PRASAD TRIPATHY‡
Department of Mining Engineering, National Institute of Technology,Rourkela, Orissa 769008, India
∗santosh [email protected]†[email protected]
‡debi [email protected]
SARAT KUMAR PATRA
Department of Electronics and Communication Engineering,National Institute of Technology, Rourkela, Orissa 769008, India
Received 13 August 2008Revised 10 May 2009
The need of developing appropriate noise prediction models for finding out the accuratestatus of noise levels (>90 dBA) generated from various opencast mining machineriesis overdue. The measured sound pressure levels (SPL) of equipments are not accuratedue to instrumental error, attenuation due to geometrical aberration, atmospheric at-tenuation etc. Some of the popular noise prediction models e.g. VDI and ENM havebeen applied in mining and allied industries. Among these models, VDI2714 is simpleand less complex model. In this paper, a neuro-fuzzy model is proposed to predict themachinery noise in an opencast coal mine. The proposed model is trained with VDI2714and the model output is seen very closely to matching with VDI2714 output. The modelproposed has a mean square error of 2.73%. This model takes CPU time of nearly 0.0625sec where as it takes 0.5 sec for VDI2714 i.e. approximately twelve times faster.
Keywords: Noise prediction models; opencast mines, VDI2714; fuzzy system; neuro-fuzzymodel.
1. Introduction
Noise is generated from all most all the opencast mining operations from differ-ent fixed, mobile and impulsive sources; thereby becoming an integral part of themining environment. With increased mechanization, the problem of noise has gotaccentuated in opencast mines. Prolonged exposure of miners to the high levelsof noise can cause noise induced hearing loss besides several non-auditory healtheffects. The impact of noise in opencast mines depends upon the sound power level
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of the noise generators, prevailing geo-mining conditions and the meteorologicalparameters of the mines. The noise levels need to be studied as an integrated ef-fect of the above parameters. In mining conditions the equipment conditions andenvironment continuously changes as the mining activity progresses. Depending ontheir placement, the overall mining noise emanating from the mines varies in qualityand level. Thus for environmental noise prediction models, the noise level at anyreceiver point needs to be the resultant sound pressure level of all the noise sources.
The need for accurately predicting the level of sound emitted in opencast minesis well established. Some of the noise forecasting models used extensively in Europeare those of the German Draft Standard VDI-2714 Outdoor Sound Propagation andEnvironmental Noise Model (ENM). These models are generally used to predictnoise in petrochemical complexes and mines. The algorithm used in these modelsrely for a greater part on interpolation of experimental data which is a valid anduseful technique, but their applications are limited to sites which are more or lesssimilar to those for which the experimental data were assimilated.
A number of models were developed and were extensively used for the assessmentof sound pressure level and their attenuation around industrial complexes. Generallyin Indian mining industry, ENM (Environmental Noise Model) developed by RTAgroup, Australia is mostly used to predict noise. Pal et al. (1997) applied ENMmodel to predict sound pressure level in mining complexes at Moonidih Project inJharia Coalfield (Dhanbad, Inida).1 The applied model output was represented asnoise contours.
Tonin (1985) studied the applications of different noise prediction models in-cluding VDI2714 for various mines and petrochemical complexes and discussedthat VDI2714 model is the simplest and least complex as compared to other mod-els.2 Rabeiy et al. (2004) used VDI2714 and ISO (1996) noise prediction modelsin Assiut cement plant, Assiut cement quarry and El-Gedida mine at El-Bahariaoasis of Egypt to predict noise. They concluded that the prediction models can beused to identify the safe zones with respect to the noise level in mining and indus-trial plants. They also inferred that the VDI2714 model is the simplest model forprediction of noise in mining complexes and workplace.3 Pathak et al. (2000) devel-oped air attenuation model for noise prediction in limestone quarry and mines ofIreland.5 They developed the model to predict attenuation in air due to absorptionin air considering temperature and relative humidity.
All the noise models treat noise as a function of distance, sound power level, dif-ferent form of attenuations such as geometrical absorptions, barrier effects, groundtopography etc. Generally these parameters are measured in the mines and best fit-ting models are applied to predict noise. Mathematical models are generally com-plex and cannot be implemented in real time systems. Additionally they fail topredict the future parameters from current and past measurements. It has beenseen that noise prediction is a non-stationary process and soft-computing tech-niques like Fuzzy system, Adaptive network based fuzzy inference system (ANFIS)or (Neuro-Fuzzy), Neural Network etc. have been tested for non-stationary time-
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series prediction nearly for two decades. Fuzzy logic was introduced by Zadeh (1965)as a mathematical way to represent vagueness in linguistics and can be consideredas a generalization of classical set theory.6 This great innovation had supplementedconventional technologies in many scientific applications and engineering applica-tions.There is a scope of using soft-computing techniques viz. Fuzzy system, Ar-tificial neural networks, Radial basis function etc. for noise prediction in miningindustry. The authors had earlier investigated the use of fuzzy model for predictingnoise induced hearing loss in mining industry.4 The results have encouraged themto investigate noise prediction in opencast mine using Neuro-Fuzzy model.
In this paper, an attempt has been made to develop a neuro-fuzzy model fornoise prediction in Balaram opencast mine of Talcher, Orissa, India. The data as-sembled through surveys, measurement or knowledge to predict sound pressure levelin mines is often imprecise or speculative. Since fuzzy system is a good predictedtool for imprecise and uncertainty information, hence fuzzy approach would be themost appropriate technique for modeling the prediction of sound pressure level inopencast mines.
2. VDI-2714 Noise Prediction Model
In 1976, the VDI (Verein Deutscher Ingenieur) draft code 2714 on Outdoor SoundPropagation was issued by the VDI Committee on Noise Reduction.2 The soundpressure level at an environmental point is calculated form the following equation:
LpdB(A) = Σlogall sources[LW +K1 − 10 log(4ΠR2)
+ 3dB −K2 −K3 −K4 −K5 −K6 −K7] (1)
whereLw = source power level re 10−12 wattsK1 = source directivity index
−10 log(4ΠR2) + 3dB = geometric spreading term including infinite hard planecoinciding with the source
R = source to receiver distanceK2 = atmospheric attenuation = 10 log(1 + 0.0015R)dB(A)K3 = attenuation due to meteorological conditions
= [(12.5/R2) + 0.2]−1 dB(A)K4 = ground effects = 10 log[3 + (R/160)]−K2 −K3dB(A)K5 = barrier value (0-10) = 10 log(3 + 20d) dB(A)d = barrier path difference
K6 = attenuation due to woodland areasK7 = attenuation due to built-up areas.
3. Fuzzy Expert System An Introduction
This section provides an introduction to fuzzy systems. Detailed analysis on fuzzysystem can be found in numerous literature [6–9]. Block diagram of a typical fuzzylogic system is presented in Fig. 1. As outlined in Fig. 1, a fuzzy rule based system
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732 S. K. Nanda, D. P. Tripathy & S. K. Patra
Fig. 1. Structure of fuzzy rule based system.
consists of four parts: fuzzifier, knowledge base, inference engine and defuzzifier.These four parts are described below:
• Fuzzifier: The real world input to the fuzzy system is applied to the fuzzzi-fier. In fuzzy literature, this input is called crisp input since it containsprecise information about the specific information about the parameter.The fuzzifier converts this precise quantity to the form of imprecise quan-tity like ‘large’, ‘medium’, ‘high’ etc. with a degree of belongingness to it.Typically, the value ranges between 0 to 1.
• Knowledge base: The main part of the fuzzy system is the knowledge basein which both rule base and database are jointly referred. The databasedefines the membership functions of the fuzzy sets used in the fuzzy ruleswhere as the rule base contains a number of fuzzy if-then rules.
• Inference engine: The inference system or the decision-making unit per-forms the inference operations on the rules. It handles the way in whichthe rules are combined.
• Defuzzifier: The output generated by the inference block is always fuzzyin nature. A real world system will always require the output of the fuzzysystem to the crisp or in the form of real world input. The job of thedefuzzifier is to receive the fuzzy input and provide real world output. Inoperation, it works opposite to the input block.
The above description shows the behavior of a fuzzy expert system. Let X be theuniverse of discourse and x is the elements of X . A fuzzy set A in a universe ofdiscourse X is characterized by a membership function µA(x) which has a valueranging from 0 to 1. If there are n fuzzy sets associated with a given input x,then fuzzifier would produce n fuzzy sets as A1(x), A2(x) . . . An(x) with n numberof membership function µAi(x), i = 1, 2 . . . n. This process is called the fuzzifica-tion. After fuzzification the information goes to knowledge base which comprises adatabase and rule base. Membership functions of the fuzzy sets are contained inthe data base. The rule base is a set of linguistic statements in the appearance of
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IF-THEN rules with antecedents and consequents, correspondingly, with AND orOR operators. Fuzzy rule based system with multi-inputs single-output(MISO)canbe represented in the following manner:10
IF X1 is B11 AND X2 is B12 AND .. AND Xr is B1r THEN Y1 is D1
ALSO . . .ALSOIF X1 is Bm1 AND X2 is Bm2 AND . . .AND Xr is Bmr THEN Ys is Ds
Where X1, X2 . . . Xr are the input variables and Y1, Y2 . . . Ys are the output vari-ables, Bij(i = 1, ..m; j = 1, ..r) and Di(i = 1, ..s) are the linguistic labels defined asreference fuzzy sets over the input space X1, X2 . . .Xr and output space Y1, Y2..Ys
of the MISO system. After formation of rule base the last unit block defuzzifierconverts the fuzzy output obtained by inference engine into a non-fuzzy output realnumber domain and this process is called defuzzification. Among several methodsof defuzzification, the Center of Area (COA) is the most widely used method.
In general two most popular fuzzy inference systems are available i.e. Mamdanifuzzy model and Sugeno fuzzy model depending on the fuzzy reasoning and formu-lation of fuzzy IF-THEN rules. Mamdani fuzzy model11 is based on the collectionsof IF-THEN rules with both fuzzy antecedent and consequent predicts. The benefitof this model is that the rule base is generally provided by an expert and henceto a certain degree it is translucent to explanation and study. Because of its ease,Mamdani model is still most commonly used technique for solving many real worldproblems. Sugeno fuzzy model was proposed by Takagi and Sugeno12 ; Sugeno andKang13 in an attempt to build up a methodical approach to generate fuzzy rulesfrom a given input-output data. These models are build up with the help of if-thenrules that have fuzzy antecedent and functional consequent. Typically the conse-quent is a polynomial function of the desired input variables. When the order of thepolynomial function is first order, then the resulting fuzzy expert system is calledthe first order Sugeno or TSK fuzzy model, which was originally, proposed by Tak-agi and Sugeno;12 Sugeno and Kang.13 When the order of the polynomial functionof zero or the consequent part is totally constant, then the system is called zeroorder Sugeno and TSK fuzzy model, in which each rule’s consequent, is specifiedby a fuzzy singleton. To represent this constant output, a single spike is used whichalso known as singleton output membership function. The main advantage of theTSK model is its computational efficiency.
In general fuzzy systems work with a set of heuristic rules. Sometimes if, a setof training data (prototype output for given inputs) is available, fuzzy system canbe updated iteratively by training. These systems are generally termed as ANFIS(adaptive network based fuzzy inference system) or Neuro-Fuzzy systems [15–17].Here the fuzzy parameters consisting of rule base, defuzzification and fuzzy inferencerules are iteratively updated to provide optimal performance.
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4. Adaptive Fuzzy Model for Predicting Sound Pressure Level
In this present study, an attempt has been made to use adaptive fuzzy modelto predict sound pressure level by using sound power level and distance as inputparameters. This model can be treated as a MISO (multi inputs and single output)system. Figure 2 shows the general architecture of the adaptive fuzzy system forthis application. Here the fuzzy model is designed so as to predict the output asgiven by VDI2714. The system adapts itself during the training period using a setof training samples. As per Fig. 2, the primary model is VDI2714 and secondarymodel is ANFIS (Adaptive network based fuzzy inference system) model. Soundpower level (LW ) and distance (R) are the two input parameters to the system.The output of the VDI-2714 is Sound pressure level,denoted as (y) where as (y)denotes the predicted output of the system.
The architecture of the fuzzy prediction model is analyzed here and representedin Fig. 3. The methodology for the development of the adaptive fuzzy noise predic-tion model involves the following steps:
(1) Selection of input and output variables;(2) Selection of membership functions;(3) Formation of linguistic rule base;(4) Defuzzification and(5) Training of parameters of the fuzzy model.
4.1. Selection of input and output variables
The first step in system modelling is the identification of input and output variablescalled the system’s variables. Here, sound power level and distance from the sourceare used as the input parameters. These parameters are also inputs to the VDI-2714 model. The output of the system is denoted as (y). This output is expectedto match the output (y) of VDI-2714 model.
4.2. Selection of membership functions for inputs
Linguistic values are expressed in the form of fuzzy sets. A fuzzy set is usuallydefined by its membership functions. In general, triangular membership functionis used to normalize the crisp inputs because of its simplicity, mathematicallydescribed in the following manner:14
triangle(x ; a, b, c) =
0, x ≤ a
x− a
b− a, a ≤ x ≤ b
c− x
c− b, b ≤ x ≤ c
0, c ≤ x
(2)
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Fig. 2. Block diagram for adaptive fuzzy system of noise prediction model.
triangle(x; a, b, c) = max(
min(x− a
b− a,c− x
c− b
), 0
), (3)
where a, b, c are the parameters of the linguistic value and x is the range of theinput parameters. This triangular membership function as described in the aboveexpressions (2) and (3) convert the linguistic values in a range of 0 to 1.
In the proposed model, shown in Fig. 3, the two inputs are sound power level LW
and distance from the source (R) respectively. Each input has five membership func-tions e.g. L(1)
W , L(2)W . . . L
(5)W corresponding to (LW ) and R(1), R(2) . . . R(5) for R. The
process of fuzzification of input (LW ) and R is done with triangular membershipfunction. This is shown in Figs. 4 and 5 for input LW and R respectively. The mem-bership functions are represented as “L(1)
W = low (80–100 dB)”, “L(2)W = medium
(90–110 dB)” . . . “L(5)W = very high (120–140 dB)” for the input LW . The member-
ship functions corresponding to this input variables are µL1 , µL2 , . . . µL5 . Similarlythe membership functions for R are represented as “R(1) = medium low (0–10meter)”, “R(2) = low (1–15 meter)” . . . “R(5) = high (20–30 meter)” for the inputR and the membership corresponding to these input variables are µR1 , µR2 , . . . µR5
respectively.
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736 S. K. Nanda, D. P. Tripathy & S. K. Patra
Fig
.3.
Adapti
ve
fuzz
ysy
stem
arc
hit
ectu
refo
rnois
epre
dic
tion.
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Development of a Neuro Fuzzy Model for Noise Prediction in Opencast Mines 737
80 90 100 110 120 130 1400
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1Low Medium Medium High High Very High
Sound power level in dB (Lw)
Mem
ber
ship
gra
de
Fig. 4. Membership function of sound power level.
5 10 15 20 25 300
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1Medium Low Low High
Distance from the source in meter (R)
Mem
ber
ship
gra
de
Medium Medium High
Fig. 5. Membership function of distance.
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738 S. K. Nanda, D. P. Tripathy & S. K. Patra
4.3. Formation of linguistic rule-base
The relationship between input and the output are represented in the from of IF-THEN rules. The membership function L(1)
W , L(2)W . . . L
(5)W and R(1), R(2) . . . R(5) are
the inputs to the rule-base. Let the output, Sound pressure level is expressed as Z.Since there are two inputs and each input has five possible fuzzy sets. The systemcan have at most 52 =25 rules. Here in this proposed model, product inferencewas considered as discussed by Wang and Mendel.17 Each of these rules receivethe membership from each of input variable fuzzy sets. Hence rules are formed infollowing manner;
R1 : IF LW is L(1)W AND R is R(1) THEN sound pressure level (Z) is Z =
(µL1(L(1)W )×µR1(R(1))×w1;
R2 : IF LW is L(1)W AND R is R(2) THEN sound pressure level (Z) is Z =
(µL1(L(1)W )×µR2(R(2))×w2;
...
R25 : IF LW is L(5)W AND R is R(5) THEN sound pressure level (Z) is Z =
(µL5(L(5)W )×µR5(R
(5))×w25;
Since the system has 25 rules, each rule is associated with a weight. The rulesR1, R2 . . . R25 are associated with weights (w1, w2 . . . w25) respectively. Theseweights are initialized to random values at the beginning. Since this model wasproduct inference the fuzzy system can be considered as Takagi-Sugeno-Kang (TSK)fuzzy system.
4.4. Defuzzification
In the proposed model, Centroid of area (COA) method of defuzzification has beenused for determining the output. Centroid of area (COA) was expressed as (4).14
Centroid of areaz COA =
∫z µA(z)zdz∫z µA(z)dz
(4)
Here µA(z) is the membership value of set A and z is the output variable. In thismodel (Fig. 3), the estimated output is determined as
y =∑25
i=1 wi × ψi∑25i=1 wi
(5)
where ψi= µLk×µRm. where k = 1, ..5,m = 1, ..5 corresponding to number of fuzzysets in Lw and R.
4.5. Training of parameters of the fuzzy model
In the model presented at Fig. 3, weights wi, (i = 1, 2, . . . 25) are unknown. Theseare initialized to random values at the beginning. Subsequently these weights are
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updated using LMS algorithm which was first proposed by Widrow and McCool in1976.18 This is similar to the adaption used by Patra and Mulgrew.19 The weightscan be updated iteratively by;
e(k) = y(k) − y(k) (6)
W (k + 1) = W (k) + 2αe(k)ψ(k) (7)
Where k is the time index, W(k+1)refers to the new weights of the system andW(k) is the existing weight. W=[(w1, w2 . . . w25)]T is the weight vector and ψ =[(ψ1, ψ2 . . . ψ25)] is the output of the inference engine.
5. Simulation Result and Discussion
The proposed model is simulated using MATLAB software. The flowchart for theANFIS system is shown in Fig. 6.
The training data set was derived from the VDI-2714 noise prediction model(Eq. (1)). A set of 3200 data set was generated for different values of input pa-rameters LW and R.Using these data in VDI model SPL was determined. Thisconstituted y for training. The fuzzy network was trained with 3000 sets out ofthe total data generated. Remaining 200 data set was used for testing the model.The performance of training was validated using mean square error(MSE)as per-formance index. The efficiency and simplicity of the fuzzy system was validatedusing the CPU time. Figure 7 shows the error update curve during training of thesystem. Figure 8 shows the performance of the system with 200 samples and it wasobserved that the mean square error for prediction is 2.73%. The adaptive fuzzyalgorithm took CPU time of 0.0625 sec approximately as compared to 0.5 sec forthe VDI2714 model.
To test the stability of the model, validation data is essential. The validationdata is collected from Balaram Opencast coal mines, Mahanadi Coalfield Limited(MCL), Talcher (Orissa, India). The test data or the field data was measured usingBruel & Kjaer 2239 (Denmark) precession sound level meter. From the measuredparameter, VDI-2714 gives prediction by calculating all the sound attenuations in‘dB (A)’ not in octave frequency band. In general, numerous machineries are usedin opencast mines for production, so it is difficult to show the noise predictionof all the machineries using the proposed model here. The machineries ex. Shovel(10m3 bucket capacity), Dozer (410HP), Tipper (10T-160HP), Grader (220 HP)and Dumper (85T) were selected to predict the sound pressure level (SPL) by usingVDI2714 and adaptive fuzzy system. Prediction results of the two models (VDI-2714 and Neuro-Fuzzy) for Dozer machine were graphically represented in Fig. 9(a).These models were compared by using measured distance (R) and sound powerlevel (SWL). The prediction results were also represented in Table 1. Similar plotfor other machineries were represented in Figs. 9(b), (c), (d) and (e) respectively.Table 1 shows the measured field data (validation data) and prediction results of
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740 S. K. Nanda, D. P. Tripathy & S. K. Patra
Fig. 6. Flowchart of the neuro-fuzzy noise prediction model.
the two models (VDI-2714 and Neuro Fuzzy) of all the selected machineries. It isseen that the Neuro-Fuzzy noise prediction model closely matches with VDI-2714model results in noise prediction.
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Development of a Neuro Fuzzy Model for Noise Prediction in Opencast Mines 741
Fig. 7. Square error of the neuro-fuzzy model.
0 20 40 60 80 100 120 140 160 180 20060
70
80
90
100
110
120
130
Samples
SP
L in
dB
desired outputestimated output
Fig. 8. Matching with 200 samples.
5.1. Advantages of neuro-fuzzy model
From the present research study, it is found that the Neuro-Fuzzy model takes0.0625 sec CPU time vis-a-vis 0.5 sec CPU time for VDI2714 noise predictionmodel. Some of the advantages of the model are enumerated below:
• The noise generated from industrial machineries is generally represented bymathematical models. One such model is VDI-2714. In this model, the input
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742 S. K. Nanda, D. P. Tripathy & S. K. Patra
5 10 15 20 25 3070
75
80
85
90
95
100
105
110
115
120
Distance from the Source in meter
So
un
d P
ress
ure
Lev
el in
dB
Neuro Fuzzy Noise Prediction of Shovel Noise
FuzzyVDI2714
(a)
5 10 15 20 25 3070
75
80
85
90
95
100
105
110
115
120
Distance from the Source in meter
So
un
d P
ress
ure
Lev
el in
dB
Neuro Fuzzy Noise Prediction of Dumper Noise
FuzzyVDI2714
(b)
5 10 15 20 25 3070
75
80
85
90
95
100
105
110
115
120
Distance from the Source in meter
So
un
d P
ress
ure
Lev
el in
dB
Neuro Fuzzy Noise Prediction of Grader Noise
FuzzyVDI2714
(c)
5 10 15 20 25 3070
75
80
85
90
95
100
105
110
115
120
Distance from the Source in meter
So
un
d P
ress
ure
Lev
el in
dB
Neuro Fuzzy Noise Prediction of Heavy Truck(Tipper) Noise
FuzzyVDI2714
(d)
5 10 15 20 25 3070
75
80
85
90
95
100
105
110
115
120
Distance from the Source in (meter)
So
un
d P
ress
ure
Lev
el in
(d
B)
Neuro Fuzzy Noise Prediction of Bull Dozer Noise
FuzzyVDI2714
(e)
Fig. 9. Neuro-fuzzy noise prediction for machineries in the study area.
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Development of a Neuro Fuzzy Model for Noise Prediction in Opencast Mines 743Table
1.
Neu
ro-fuzz
ynois
epre
dic
tion
for
diff
eren
tm
ach
iner
ies.
Shovelnois
ein
dB
Dum
per
nois
ein
dB
Gra
der
nois
ein
dB
Tip
per
nois
ein
dB
Dozer
nois
ein
dB
Dis
tance
from
the
sourc
ein
mete
r
Measu
red
field
data
(dB
A)
Pre
dic
tion
resu
lt(d
BA
)
Measu
red
field
data
(dB
A)
Pre
dic
tion
resu
lt(d
BA
)
Measu
red
field
data
(dB
A)
Pre
dic
tion
resu
lt(d
BA
)
Measu
red
field
data
(dB
A)
Pre
dic
tion
resu
lt(d
BA
)
Measu
red
field
data
(dB
A)
Pre
dic
tion
resu
lt(d
BA
)
N-fuzzy
VD
IN
-fuzzy
VD
IN
-fuzzy
VD
IN
-fuzzy
VD
IN
-fuzzy
VD
I
1102.3
000
90.2
051
95.6
919
102.4
000
90.2
853
95.7
919
105.3
000
92.6
115
98.6
919
100.9
000
89.0
821
94.2
919
100.5
000
88.7
612
93.8
919
2102.1
000
92.9
455
95.4
828
101.3
000
92.2
722
94.6
828
103.4
000
94.0
081
96.7
828
99.7
000
90.9
254
93.0
828
100.2
000
91.3
463
93.5
828
398.6
000
91.1
476
91.9
738
98.2
000
90.7
961
91.5
738
101.2
000
93.2
710
94.5
738
98.6
000
91.1
476
91.9
738
98.2
000
90.7
961
91.5
738
498.2
000
91.1
557
91.5
648
97.7
000
90.7
761
91.0
648
98.7
000
91.5
560
92.0
648
97.5
000
90.6
297
90.8
648
97.5
000
90.6
297
90.8
648
597.5
000
90.5
989
90.8
559
97.2
000
90.3
440
90.5
559
97.2
000
90.3
440
90.5
559
96.5
000
89.7
812
89.8
559
96.7
000
89.9
376
90.0
559
697.5
000
90.5
077
90.8
469
96.8
000
89.8
325
90.1
469
95.5
000
88.7
188
88.8
469
96.2
000
89.2
976
89.5
469
95.4
000
88.6
398
88.7
469
796.7
000
89.4
805
90.0
380
94.2
000
87.2
589
87.5
380
94.3
000
87.3
358
87.6
380
95.8
000
88.6
022
89.1
380
94.8
000
87.7
336
88.1
380
895.2
000
87.7
212
88.5
291
94.1
000
86.7
376
87.4
291
94.1
000
86.7
376
87.4
291
94.8
000
87.3
480
88.1
291
94.2
000
86.8
217
87.5
291
993.3
000
85.4
354
86.6
202
93.6
000
85.6
965
86.9
202
93.7
000
85.7
857
87.0
202
94.3
000
86.3
448
87.6
202
93.6
000
85.6
965
86.9
202
10
92.4
000
83.6
372
85.7
113
93.2
000
84.3
542
86.5
113
93.2
000
84.3
542
86.5
113
93.7
000
84.8
404
87.0
113
92.5
000
83.7
230
85.8
113
11
91.5
000
83.2
331
84.8
025
93.2
000
84.8
830
86.5
025
92.6
000
84.2
596
85.9
025
92.8
000
84.4
620
86.1
025
91.8
000
83.4
994
85.1
025
12
91.5
000
83.6
705
84.7
937
92.5
000
84.7
308
85.7
937
91.8
000
83.9
743
85.0
937
90.6
000
82.8
242
83.8
937
89.6
000
81.9
843
82.8
937
13
91.3
000
83.9
048
84.5
848
92.2
000
84.9
497
85.4
848
90.4
000
82.9
754
83.6
848
89.5
000
82.2
313
82.8
848
89.3
000
81.9
700
82.5
848
14
90.4
000
83.2
946
83.6
760
90.6
000
83.5
142
83.8
760
88.6
000
81.5
435
81.8
760
88.4
000
81.3
709
81.6
760
88.8
000
81.7
200
82.0
760
15
88.8
000
81.9
558
82.0
672
89.7
000
82.8
950
82.9
672
88.5
000
81.6
649
81.7
672
86.8
000
80.1
925
80.0
672
88.2
000
81.3
840
81.4
672
16
88.4
000
81.7
271
81.6
585
88.3
000
81.6
275
81.5
585
88.2
000
81.5
292
81.4
585
86.2
000
79.7
832
79.4
585
87.9
000
81.2
411
81.1
585
17
87.9
000
81.2
228
81.1
497
88.2
000
81.5
252
81.4
497
87.9
000
81.2
228
81.1
497
85.8
000
79.3
791
79.0
497
87.4
000
80.7
425
80.6
497
18
87.1
000
80.3
335
80.3
410
87.6
000
80.8
185
80.8
410
87.3
000
80.5
240
80.5
410
85.2
000
78.7
269
78.4
410
86.6
000
79.8
765
79.8
410
19
86.7
000
79.7
007
79.9
323
87.1
000
80.0
842
80.3
323
86.5
000
79.5
157
79.7
323
85.2
000
78.4
125
78.4
323
85.5
000
78.6
528
78.7
323
20
86.3
000
78.7
986
79.5
236
86.8
000
79.2
704
80.0
236
85.8
000
78.3
542
79.0
236
84.7
000
77.4
620
77.9
236
85.5
000
78.0
998
78.7
236
21
85.7
000
78.1
231
78.9
149
86.5
000
78.8
310
79.7
149
85.4
000
77.8
747
78.6
149
84.5
000
77.1
786
77.7
149
84.8
000
77.4
028
78.0
149
22
85.2
000
77.7
547
78.4
063
86.2
000
78.6
088
79.4
063
85.1
000
77.6
748
78.3
063
83.8
000
76.7
150
77.0
063
84.3
000
77.0
677
77.5
063
23
85.3
000
77.9
729
78.4
976
85.8
000
78.4
024
78.9
976
84.6
000
77.4
136
77.7
976
83.5
000
76.6
210
76.6
976
84.2
000
77.1
139
77.3
976
24
85.5
000
78.0
839
78.6
890
85.6
000
78.1
699
78.7
890
84.2
000
77.0
572
77.3
890
83.5
000
76.5
662
76.6
890
83.8
000
76.7
719
76.9
890
25
85.5
000
77.9
792
78.6
804
84.8
000
77.4
094
77.9
804
83.8
000
76.6
758
76.9
804
83.2
000
76.2
752
76.3
804
83.5
000
76.4
721
76.6
804
26
85.3
000
77.5
712
78.4
718
84.2
000
76.7
400
77.3
718
83.2
000
76.0
750
76.3
718
82.8
000
75.8
299
75.9
718
83.5
000
76.2
664
76.6
718
27
84.7
000
76.8
183
77.8
632
84.2
000
76.4
671
77.3
632
82.9
000
75.6
479
76.0
632
82.6
000
75.4
758
75.7
632
82.8
000
75.5
899
75.9
632
28
84.2
000
76.1
314
77.3
547
83.7
000
75.8
165
76.8
547
82.5
000
75.1
337
75.6
547
82.2
000
74.9
773
75.3
547
82.5
000
75.1
337
75.6
547
29
83.8
000
75.4
846
76.9
461
83.4
000
75.2
580
76.5
461
82.1
000
74.5
923
75.2
461
82.2
000
74.6
400
75.3
461
82.4
000
74.7
371
75.5
461
30
82.7
000
74.4
604
75.8
376
82.8
000
74.5
073
75.9
376
81.8
000
74.0
617
74.9
376
82.2
000
74.2
338
75.3
376
82.4
000
74.3
228
75.5
376
Mean
square
err
or
0.9
738
0.9
512
0.9
167
0.8
236
0.8
597
September 24, 2009 15:57 WSPC/118-IJUFKS 00623
744 S. K. Nanda, D. P. Tripathy & S. K. Patra
parameters are distance (R) and sound power level (SWL (LW )); the modelpredicts the sound pressure level (SPL (LP )). Sound pressure level (SPL) wasdetermined for each set of measured distance (R) and Sound power level (SWL).The process has to be repeated for each machinery. The calculation was com-plex. The fuzzy models need to be trained once for any specific machinery. Oncethe fuzzy model was trained, SPL can be determined for any input condition.The model predicts SPL with very little CPU time (∼ 12%) compared to VDI-2714. In general, the network can be trained to work for any standard model.The prediction would correspond to the model for which it was trained. In thisspecific case the authors have considered only VDI-2714.
• The model of Neuro-Fuzzy remains fixed as long as input and output remain thesame. This can be implemented as a fixed hardware. The training data is basedon actual measurement. This information can be used to train the network.Hence same network with different training sets can provide approximate resultfor different mines/working conditions.
• Neuro-Fuzzy model can be built using DSP hardware available in market. Thiscan be used in instrument for measurement owing to low CPU time. HigherCPU time of VDI2714 model will prohibit its usage in portable instrument.
• The Neuro-Fuzzy model can be used to predict noise of machineries for othermodels also. This can be done by using a different training data sheet. In aninstrument, this can be implemented easily, where the instrument can provideprediction for different models.
6. Conclusion
The neuro-fuzzy noise prediction model output for five different machineries usedin opencast mines such as dozer, shovel, tipper or dumper matches closely withVDI2714 prediction result. This model takes CPU time of nearly 0.0625 sec whereas it takes 0.5 sec for VDI2714 i.e. it is approximately twelve times faster. Thiscomparison of CPU time shows the computational stability of the model. It ishoped that the developed model will be quite useful in prediction of machinerynoise in opencast mines.
Acknowledgement
The authors like to thank the anonymous referees for their valuable comments andwords of encouragement. Their comments helped to improve the clarity of thispaper.
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