Advances in Fuzzy SystemsVolume 2011 (2011), Article ID 831261, 11 pagesdoi:10.1155/2011/831261

Research Article

Application of Functional Link Artificial Neural Network for Prediction of Machinery Noise in Opencast Mines

Santosh Kumar Nanda¹ and Debi Prasad Tripathy²

¹Department of Computer Science and Engineering, Eastern Academy of Science and Technology, Bhubaneswar, Orissa 754001, India²Department of Mining Engineering, National Institute of Technology, Rourkela, Orissa 769008, India

Received 16 December 2010; Accepted 22 April 2011

Academic Editor: E. E. Kerre

Copyright © 2011 Santosh Kumar Nanda and Debi Prasad Tripathy. This is an open access articel distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Functional link-based neural network models were applied to predict opencast mining machineries noise. The paper analyzes the prediction capabilities of functional link neural network based noise prediction models vis-à-vis existing statistical models. In order to find the actual noise status in opencast mines, some of the popular noise prediction models, for example, ISO-9613-2, CONCAWE, VDI, and ENM, have been applied in mining and allied industries to predict the machineries noise by considering various attenuation factors. Functional link artificial neural network (FLANN), polynomial perceptron network (PPN), and Legendre neural network (LeNN) were used to predict the machinery noise in opencast mines. The case study is based on data collected from an opencast coal mine of Orissa, India. From the present investigations, it could be concluded that the FLANN model give better noise prediction than the PPN and LeNN model.

1. Introduction

At the present time, owing to the improvements in technology through superior energy competence, higher labor output, continuous production methods, and operating flexibility, automation has also advanced rapidly in open and underground pits together with mineral processing plants. In parallel to this improvement, sources of noise and ambient noise at work place in the mining industry have increased significantly. In general, noise is generated from all most all the opencast mining operations from different fixed, mobile, and impulsive sources, thereby becoming an integral part of the mining environment. With increased mechanization, the problem of noise has got accentuated in opencast mines. Prolonged exposure of miners to the high levels of noise can cause noise-induced hearing loss besides several nonauditory health effects [1]. The impact of noise in opencast mines depends upon the sound power level of the noise generators, prevailing geomining conditions and the meteorological parameters of the mines [2–4]. The noise levels need to be studied as an integrated effect of the above parameters. In mining conditions the equipment conditions and environment continuously change as the mining activity progresses. Depending on their placement, the overall mining noise emanating from the mines varies in quality and level. Thus, for environmental noise prediction models, the noise level at any receiver 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 mines is well established. Some of the noise forecasting models used extensively in Europe are those of the German draft standard VDI-2714 outdoor sound propagation and environmental noise model (ENM) of Australia [5]. These models are generally used to predict noise in petrochemical complexes, and mines. The algorithm used in these models rely for a greater part on interpolation of experimental data which is a valid and useful technique, but their applications are limited to sites which are more or less similar to those for which the experimental data were assimilated.

A number of models were developed and extensively used for the assessment of sound pressure level and their attenuation around industrial complexes. Generally, in Indian mining industry, environmental noise model developed by RTA group, Australia is mostly used to predict noise [4, 6]. ENM was used to predict sound pressure level in mining complexes at Moonidih Project in Jharia Coalfield, Dhanbad, India [6]. The applied model output was represented as noise contours. The application of different noise prediction models was studied for various mines and petrochemical complexes and it was reported that VDI2714 model was the simplest and least complex model vis-à-vis other models [5]. VDI2714 and ISO (1996) noise prediction models were used in Assiut cement plant, Assiut cement quarry and El-Gedida mine at El-Baharia oasis of Egypt to predict noise. From the study, it was concluded that the prediction models could be used to identify the safe zones with respect to the noise level in mining and industrial plants. It was also inferred that the VDI2714 model is the simplest model for prediction of noise in mining complexes and workplace [7]. Air attenuation model was developed for noise prediction in limestone quarry and mines of Ireland. The model was used to predict attenuation in air due to absorption [8].

All the noise prediction models treat noise as a function of distance, sound power level, different form of attenuations such as geometrical absorptions, barrier effects, ground topography. Generally, these parameters are measured in the mines and best fitting models are applied to predict noise. Mathematical models are generally complex and cannot be implemented in real time systems. Additionally, they fail to predict the future parameters from current and past measurements. It has been seen that noise prediction is a nonstationary process and soft-computing techniques like fuzzy system, adaptive neural network-based fuzzy inference system (ANFIS), neural network, and so forth, have been tested for nonstationary time-series prediction nearly for two decades. There is a scope of using different soft computing techniques:  fuzzy logic, artificial neural networks, radial basis function (RBF) and so forth, for noise prediction in mines. In comparison to other soft computing techniques, functional link artificial neural network (FLANN) and Legendre Neural Network (LeNN) has less computational cost and easily implemented in hardware applications. This is the motivation on which the present research work is based.

In this paper, an attempt has been made to develop three types of functional link artificial neural network models (FLANN, PPN, and LeNN) for noise prediction of machineries used in Balaram opencast coal mine of Talcher, Orissa, India. The data assembled through surveys, measurement or knowledge to predict sound pressure level in mines is often imprecise or speculative. Since neural network-based systems are good predictive tools for imprecise and uncertainty information; therefore, the proposed approach would be the most appropriate technique for modeling the prediction of sound pressure level in opencast coal mines.

2. VDI-2714 Noise Prediction Model

In 1976, the VDI draft code 2714 “outdoor sound propagation” was issued by the VDI committee on noise reduction [5]. The sound pressure level at an environmental point is calculated from the following equation (1) : source power level re 10⁻¹² watts, : source directivity index, : geometric spreading term including infinite hard plane coinciding with the source, : source to receiver distance, : atmospheric attenuation = , : attenuation due to meteorological conditions = : ground effects = : barrier value (0–10) = : barrier path difference, : attenuation due to woodland areas, : attenuation due to built-up areas.

3. Introduction to Functional Link-Based Artificial Neural Network

Neural network (NN) represents an important paradigm for classifying patterns or approximating complex nonlinear process dynamics. These properties clearly indicate that NN exhibit some intelligent behavior, and are good candidate models for nonlinear processes, for which no perfect mathematical model is available. Neural networks have been a powerful tool for their applications for more than last two decades [9–13]. Multilayer perceptron (MLP), radial basis function (RBF), Support vector machine (SVM) and so forth, are the types of Neural Network Model, where these models have better prediction competence with high computational cost. Generally, these models have high computational cost due to the availability of hidden layer. To minimize the computational cost, structures like, polynomial perceptron network (PPN) [14], functional link artificial neural network (FLANN) [15–18], Legendre neural network (LeNN) [19, 20] were proposed. In this paper, three types of functional based artificial neural networks have been applied to predict mining machinery noise. These include polynomial perceptron network (PPN), functional link artificial neural network (FLANN), and Legendre neural network (LeNN).

In general, the functional link-based neural network models were single-layer ANN structure possessing higher rate of convergence and lesser computational load than those of an MLP structure. The behavior and mapping ability of a PPN and its application to channel equalization is reported by Xiang et al. (1994) [14]. The mathematical expression and computational calculation is evaluated as per MLP. Figure 1(a) represents the structure of PPN. Patra originally proposed functional link artificial neural network (FLANN), and it is a novel single-layer ANN structure capable of forming arbitrarily complex decision regions by generating nonlinear decision boundaries [15–18]. In FLANN, the hidden layers are removed. Further, the FLANN structure offers less computational complexity and higher convergence speed than those of an MLP because of its single-layer structure. The FLANN structure is depicted in Figure 1(b). Here, the functional expansion block makes use of a functional model comprising a subset of orthogonal sin and cos basis functions and the original pattern along with its outer products. For example, considering a two-dimensional input pattern . The enhanced pattern is obtained by using the trigonometric functions as which is then used by the network for the equalization purpose. The BP algorithm, which is used to train the network, becomes very simple because of absence of any hidden layer. Justification for the use of the trigonometric functions in the FLANN model is provided in [15–18].

(a) PPN structure

(b) FLANN structure

(c) Legendre neural network structure

Figure 1: System architecture of function link based artificial neural networks.

Structure of the Legendre neural network [19, 20] (LeNN) (Figure 1(c)) is similar to FLANN. In contrast to FLANN, in which trigonometric functions are used in the functional expansion, LeNN uses Legendre orthogonal functions. LeNN offers faster training compared to FLANN. The performance of this model may vary from problem to problem. The Legendre polynomials are denoted by , where is the order and is the argument of the polynomial. The zero and the first-order Legendre polynomials are, respectively, given by and . The higher order polynomials are given by , and so forth. Polynomials are generated by using the following mathematical expression: Similar to FLANN, the two-dimensional input pattern is enhanced to a seven dimensional pattern by Legendre functional expansion. For Legendre neural network, the training is carried out in the same manner as FLANN and PPN. In all models, supervised learning is used. As in normal artificial neural network techniques, the presence of hidden layers increases the complexity in the real-time system, therefore, FLANN and LeNN is suitably used at here due to less computational cost.

4. Development of Functional Link Artificial Neural Network Based-Noise Prediction Model

The functional link artificial neural network-based noise prediction models consist of two input parameters: sound power level and distance . The inputs patterns are , , , , , and the desired output patterns are: , , . During training period, the desired network output was calculated with VDI-2714 noise prediction model. Since the procedures of these three models were similar; therefore, one algorithm is presented here to emphasize the development of functional-based neural network-based noise prediction models. Figure 2 graphically represent the algorithm of functional-based neural network-based noise prediction models.

Figure 2: Systematic algorithm of functional based neural network based noise prediction models.

Step 1. Initialize the inputs distance = , (), sound power level = (), where and are the number of input pattern and an error tolerance parameter . The dimension of and should be same.

Step 2. Randomly select the initial values of the weight vectors , for , where “” is the number of functional elements.

Step 3 (Initialization). All the weights were initialized to random number and given as

Step 4 (Produce functional blocks). For FLANN the functional block is made as follows: For PPN the functional block is made as follows: for LeNN the functional block is made as follows: where ,  , and so forth.

Step 5 (Calculation of the system outputs). For functional based neural network models, the output was calculated as follows:

Step 6 (Calculation of the output error). The error was calculated as . It may be seen that the network produces a scalar output.

Step 7 (Updating the weight vectors). The weight matrixes are updated next using the following relationship: where is the time index and is the momentum parameter.

Step 8. If error then go to Step 8 otherwise, go to Step 3.

Step 9. After the, learning is complete, the weights were fixed, and the network can be used for testing.

5. Simulation Result and Discussion

The proposed system models for noise prediction were validated using simulation studies. The studies were carried out by using MATLAB simulation environment. For validation of the models, the noise data was collected from Balaram opencast coal mine of Mahanadi Coalfields Limited (MCL), Talcher (Orissa, India). The test data was measured using Brüel and Kjaer 2239 (Denmark) precision sound level meter. From the measured parameter, VDI-2714 gives prediction by calculating all the sound attenuations in “dB(A)” not in octave frequency band. SPL of the different machineries from the above mine was collected. These machineries include Shovel (10 m³ bucket capacity), dozer (410 HP), tipper (10 T-160 HP), grader (220 HP), and dumper (85 T).

According to Figure 2, the stepwise procedures were required to design the model. In this problem, the system is a MISO (multi input and output system) system. The system architectures of these proposed functional link-based noise prediction models are the same, whereas only the input pattern or functional blocks are different. To design these models, total number of 3200 dataset were selected. Out of 3200, 3000 dataset were selected for training process and 200 data were selected for testing process. In this proposed systems, iteration based training methods were applied. The mean square error (MSE) plot of FLANN-based noise prediction model is represented in Figure 3, where Figures 4 and 5 are represented PPN and LeNN noise prediction models. Performance of these models for 200 testing samples or validation samples was represented from Figures 6, 7, and 8.The average percentage error (APE) was used as the performance index and was calculated as

Figure 3: Mean square plot of FLANN based noise prediction model for 3000 iteration.

Figure 4: Mean square plot of PPN based noise prediction model for 3000 iteration.

Figure 5: Mean square plot of LeNN based noise prediction model for 3000 iteration.

Figure 6: Prediction performance of FLANN system for 200 samples.

Figure 7: Prediction performance of PPN system for 200 samples.

Figure 8: Prediction performance of LeNN system for 200 samples.

Tables 1, 2, 3, 4, and 5 summarizes the results for noise prediction by proposed models and compares it with standard VDI-2714 noise prediction model for all selected opencast machineries. From these tables it can be seen that the proposed PPN, FLANN and LeNN models provided average percentage error of 7.03, 5.68, and 8.42, respectively, for shovel. For dumper, the average percentage errors were 7.27, 5.77, and 8.20; for Grader, the APE were 9.56, 6.15, and 9.76; for Tipper, APE for three systems were 13.59, 4.36, and 10.03, respectively. The average percentage errors of the dozer were found as 10.94, 6.32, and 9.53 respectively. From the simulation studies, it was observed that the average percentage error of FLANN model was lower than the other two models.

Table 1: Simulation study of shovel noise.

Distance from the source (meters)
Measured field data (dBA)
Prediction result (dBA)
Average percentage error (dBA)

VDI
PPN
FLANN
LeNN
PPN
FLANN
LeNN

1
102.3000
95.6919
74.1198
88.6731
81.4871
7.03
5.68
8.42

2
102.1000
95.4828
74.1198
94.3428
81.4871

3
98.6000
91.9738
74.1198
94.5178
81.4871

4
98.2000
91.5648
74.1198
89.4572
81.4871

5
97.5000
90.8559
74.1198
87.9020
81.4871

6
97.5000
90.8469
85.8609
96.5394
94.5712

7
96.7000
90.0380
85.8609
90.3449
94.5712

8
95.2000
88.5291
85.8609
93.3190
94.5712

9
93.3000
86.6202
85.8609
94.0383
94.5712

10
92.4000
85.7113
85.8609
94.0383
94.5712

11
91.5000
84.8025
85.8609
87.6493
94.5712

12
91.5000
84.7937
85.8609
88.3686
94.5712

13
91.3000
84.5848
85.8609
85.2195
94.5712

14
90.4000
83.6760
85.8609
91.4140
94.5712

15
88.8000
82.0672
76.5756
85.3685
85.3813

16
88.4000
81.6585
76.5756
85.3685
85.3813

17
87.9000
81.1497
76.5756
88.5889
85.3813

18
87.1000
80.3410
76.5756
88.4139
85.3813

19
86.7000
79.9323
76.5756
87.6947
85.3813

20
86.3000
79.5236
76.5756
87.6947
85.3813

21
85.7000
78.9149
76.5756
85.1287
85.3813

22
85.2000
78.4063
74.3142
84.4095
85.3813

23
85.3000
78.4976
74.3142
84.4095
85.3813

24
85.5000
78.6890
73.2126
81.1891
85.3813

25
85.5000
78.6804
73.2126
80.1264
82.0894

26
85.3000
78.4718
75.3380
81.6816
82.0894

27
84.7000
77.8632
71.4890
81.4353
82.0894

28
84.2000
77.3547
72.2342
81.6103
82.7253

29
83.8000
76.9461
72.2342
82.3295
82.9287

30
82.7000
75.8376
72.2342
82.3295
82.9287

Table 2: Simulation study of dumper noise.

Distance from the source (meters)
Measured field data (dBA)
Prediction result (dBA)
Average percentage error (dBA)

VDI
PPN
FLANN
LeNN
PPN
FLANN
LeNN

1
102.4000
95.7919
74.1198
88.6731
81.4871

2
101.3000
94.6828
74.1198
93.0404
81.4871

3
98.2000
91.5738
74.1198
94.5178
81.4871

4
97.7000
91.0648
74.1198
89.4572
81.4871

5
97.2000
90.5559
74.1198
87.9020
81.4871

6
96.8000
90.1469
85.8609
96.5394
94.5712

7
94.2000
87.5380
74.1198
82.7701
81.4871

8
94.1000
87.4291
85.8609
93.3190
94.5712

9
93.6000
86.9202
85.8609
94.0383
94.5712

10
93.2000
86.5113
85.8609
94.0383
94.5712

11
93.2000
86.5025
85.8609
84.6752
94.5712

12
92.5000
85.7937
85.8609
85.3944
94.5712

13
92.2000
85.4848
85.8609
86.3534
94.5712

14
90.6000
83.8760
85.8609
88.4398
94.5712

15
89.7000
82.9672
76.5756
85.3685
85.3813
7.27
5.77
8.20

16
88.3000
81.5585
76.5756
85.3685
85.3813

17
88.2000
81.4497
76.5756
88.5889
85.3813

18
87.6000
80.8410
76.5756
88.4139
85.3813

19
87.1000
80.3323
76.5756
87.6947
85.3813

20
86.8000
80.0236
76.5756
87.6947
85.3813

21
86.5000
79.7149
76.5756
85.1287
85.3813

22
86.2000
79.4063
74.3142
84.4095
85.3813

23
85.8000
78.9976
74.3142
84.4095
85.3813

24
85.6000
78.7890
73.2126
81.1891
85.3813

25
84.8000
77.9804
73.2126
83.1006
82.0894

26
84.2000
77.3718
75.3380
84.6557
82.0894

27
84.2000
77.3632
71.4890
81.4353
82.0894

28
83.7000
76.8547
72.0592
81.6103
82.7253

29
83.4000
76.5461
72.2342
82.3295
82.9287

30
82.8000
75.9376
72.2342
82.3295
82.9287

Table 3: Simulation study of grader noise.

Distance from the source (meters)
Measured field data (dBA)
Prediction result (dBA)
Average percentage error (dBA)

VDI
PPN
FLANN
LeNN
PPN
FLANN
LeNN

1
105.3000
98.6919
74.1198
89.9755
81.4871

2
103.4000
96.7828
74.1198
95.4768
81.4871

3
101.2000
94.5738
74.1198
92.6776
81.4871

4
98.7000
92.0648
74.1198
89.4572
81.4871

5
97.2000
90.5559
74.1198
87.9020
81.4871

6
95.5000
88.8469
74.1198
85.9905
81.4871

7
94.3000
87.6380
74.1198
82.7701
81.4871

8
94.1000
87.4291
85.8609
93.3190
94.5712

9
93.7000
87.0202
85.8609
94.0383
94.5712

10
93.2000
86.5113
85.8609
94.0383
94.5712

11
92.6000
85.9025
85.8609
87.6493
94.5712

12
91.8000
85.0937
85.8609
88.3686
94.5712

13
90.4000
83.6848
85.8609
88.1936
94.5712

14
88.6000
81.8760
85.8609
91.4140
94.5712

15
88.5000
81.7672
76.5756
85.3685
85.3813
9.56
6.15
9.76

16
88.2000
81.4585
76.5756
85.3685
85.3813

17
87.9000
81.1497
76.5756
88.5889
85.3813

18
87.3000
80.5410
76.5756
88.4139
85.3813

19
86.5000
79.7323
76.5756
87.6947
85.3813

20
85.8000
79.0236
76.5756
87.6947
85.3813

21
85.4000
78.6149
76.5756
85.1287
85.3813

22
85.1000
78.3063
74.3142
84.4095
85.3813

23
84.6000
77.7976
74.3142
84.4095
85.3813

24
84.2000
77.3890
73.2126
81.1891
85.3813

25
83.8000
76.9804
73.2126
83.1006
82.0894

26
83.2000
76.3718
63.5968
74.1068
66.3679

27
82.9000
76.0632
71.4890
81.4353
82.0894

28
82.5000
75.6547
60.3181
71.0614
67.0982

29
82.1000
75.2461
60.3181
71.7806
67.3327

30
81.8000
74.9376
60.3181
71.7806
67.3327

Table 4: Simulation study of tipper noise.

Distance from the source (meters)
Measured field data (dBA)
Prediction result (dBA)
Average percentage error (dBA)

VDI
PPN
FLANN
LeNN
PPN
FLANN
LeNN

1
100.9000
94.2919
74.1198
85.6989
81.4871

2
99.7000
93.0828
74.1198
93.0404
81.4871

3
98.6000
91.9738
74.1198
94.5178
81.4871

4
97.5000
90.8648
74.1198
92.4313
81.4871

5
96.5000
89.8559
74.1198
87.9020
81.4871

6
96.2000
89.5469
85.8609
96.5394
94.5712

7
95.8000
89.1380
85.8609
93.3190
94.5712

8
94.8000
88.1291
85.8609
93.3190
94.5712

9
94.3000
87.6202
85.8609
94.0383
94.5712

10
93.7000
87.0113
85.8609
91.0641
94.5712

11
92.8000
86.1025
85.8609
84.6752
94.5712

12
90.6000
83.8937
85.8609
88.3686
94.5712

13
89.5000
82.8848
85.8609
88.1936
94.5712

14
88.4000
81.6760
74.1198
80.8651
81.4871

15
86.8000
80.0672
64.8344
74.8196
70.1930
13.59
4.36
10.03

16
86.2000
79.4585
64.8344
74.8196
70.1930

17
85.8000
79.0497
64.8344
78.0400
70.1930

18
85.2000
78.4410
64.8344
77.8650
70.1930

19
85.2000
78.4323
64.8344
77.1458
70.1930

20
84.7000
77.9236
64.8344
77.1458
70.1930

21
84.5000
77.7149
64.8344
74.5798
70.1930

22
83.8000
77.0063
62.5730
73.8606
70.1930

23
83.5000
76.6976
62.5730
73.8606
70.1930

24
83.5000
76.6890
61.4715
70.6402
70.1930

25
83.2000
76.3804
61.4715
72.5517
66.3679

26
82.8000
75.9718
63.5968
74.1068
66.3679

27
82.6000
75.7632
59.7479
70.8864
66.3679

28
82.2000
75.3547
60.3181
71.0614
67.0982

29
82.2000
75.3461
60.3181
71.7806
67.3327

30
82.2000
75.3376
72.2342
82.3295
82.9287

Table 5: Simulation study of dozer noise.

Distance from the source (meters)
Measured field data (dBA)
Prediction result (dBA)
Average percentage error (dBA)

VDI
PPN
FLANN
LeNN
PPN
FLANN
LeNN

1
100.5000
93.8919
74.1198
85.6989
81.4871

2
100.2000
93.5828
74.1198
93.0404
81.4871

3
98.2000
91.5738
74.1198
94.5178
81.4871

4
97.5000
90.8648
74.1198
92.4313
81.4871

5
96.7000
90.0559
74.1198
87.9020
81.4871

6
95.4000
88.7469
74.1198
85.9905
81.4871

7
94.8000
88.1380
85.8609
93.3190
94.5712

8
94.2000
87.5291
85.8609
93.3190
94.5712

9
93.6000
86.9202
85.8609
94.0383
94.5712

10
92.5000
85.8113
85.8609
94.0383
94.5712

11
91.8000
85.1025
85.8609
87.6493
94.5712

12
89.6000
82.8937
74.1198
77.8197
81.4871

13
89.3000
82.5848
85.8609
88.1936
94.5712

14
88.8000
82.0760
85.8609
91.4140
94.5712

15
88.2000
81.4672
76.5756
85.3685
85.3813
10.94
6.32
9.53

16
87.9000
81.1585
76.5756
85.3685
85.3813

17
87.4000
80.6497
76.5756
88.5889
85.3813

18
86.6000
79.8410
76.5756
88.4139
85.3813

19
85.5000
78.7323
64.8344
77.1458
70.1930

20
85.5000
78.7236
76.5756
87.6947
85.3813

21
84.8000
78.0149
64.8344
74.5798
70.1930

22
84.3000
77.5063
62.5730
73.8606
70.1930

23
84.2000
77.3976
62.5730
73.8606
70.1930

24
83.8000
76.9890
61.4715
70.6402
70.1930

25
83.5000
76.6804
61.4715
72.5517
66.3679

26
83.5000
76.6718
75.3380
84.6557
82.0894

27
82.8000
75.9632
59.7479
70.8864
66.3679

28
82.5000
75.6547
60.3181
71.0614
67.0982

29
82.4000
75.5461
72.0592
82.3295
82.9287

30
82.4000
75.5376
72.2342
82.3295
82.9287

6. Conclusion

This paper introduced the idea of designing noise prediction model for opencast mining machineries using functional link artificial neural network systems. From the present study, it was observed that the average percentage error using FLANN lower than PPN and LeNN systems for all the machineries. These functional link artificial neural network based noise prediction models can be useful tools for mining engineers to estimate the actual noise condition of the machineries accurately.

Acknowledgments

The authors like to thank the anonymous referees for their valuable comments and words of encouragement. Their comments helped to improve the clarity of this paper.

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