artificial neural networks for fault diagnosis of milk ... · diagnostic approaches have emerged as...

Proceedings of the International Conference on Industrial Engineering and Operations Management Dubai, UAE, March 10-12, 2020

© IEOM Society International

Artificial Neural Networks for Fault Diagnosis of Milk

Pasteurization Process - A Comparative Study

Chebira Samia, Bourmada Noureddine, and Boughaba Abdelali

Industrial Safety Department, Hygiene and Industrial Safety Institute

Mustafa Ben Boulaid BATNA 2 University

Batna, Algeria

[email protected]

Abstract

The increasing complexity of most industrial processes always tends to create problems in monitoring and

supervision systems. Detection and early fault diagnosis are the best way to manage and solve these problems.

Artificial neural networks (ANNs), by their ability to learn and store a large volume of information, are tools

particularly suitable for diagnostic support systems. Effectiveness of ANNs for fault diagnosis in milk

pasteurization process is presented in this paper. The initial data base used for fault diagnosis is constructed

using data extracted from FMEA (Failure Modes and Effects Analysis) tables of milk pasteurization process.

Indeed, this analysis makes it possible to establish the links of cause and effect between the faulty components

and the observed symptoms. Three models of ANNs, namely Feed-Forward Back Propagation (FFBP), Radial

Basis Function based Neural Network (RBNN), and Generalized Regression Neural Networks (GRNN) are

developed and compared. The determination coefficient (R2), Root Mean Square Error (RMSE), and Mean

Absolute Error (MAE) statistics were used as evaluation criteria of all the models. The comparison results

indicate that the performances of GRNN model are better than the FFBP and RBNN models. The same neuronal

models can be extended to any technical system by considering appropriate parameters and defects.

Keywords Fault diagnosis, Feed-forward back propagation, Radial basis function based neural network, Generalized

regression neural networks.

1. Introduction

The problem of detecting and diagnosing faults in complex industrial plants is strategically important for its

various implications, e.g., avoiding breakdowns that can lead to major industrial disasters, problems related to

the workers and plants safety, fast and appropriate response to emergency situations and plant maintenance. For

example, the following systems represent only a small part of systems where fault detection and diagnosis are

usually a very difficult but important tasks: chemical and petrochemical plants, refineries, power plants,

airplanes, ships, submarines, space vehicles and space stations, automobiles and household appliances.

Generally, in industrial plants, there is a crucial need for checking and monitoring the equipment condition

precisely since they are mostly subject to hazardous environments, such as severe shocks, vibration, heat,

friction, etc. So fault detection, fault identification and diagnosis of equipments, machineries and systems have

become a vigorous area of work. Due to the broad scope of the process fault diagnosis problem and the

difficulties in its real-time solution, many analytical-based techniques (Isermann ,1997; Leonhardt and Ayoubi,

1997) have been proposed during the past several years for the fault detection of technical plants. The important

aspect of these approaches is the development of a model that describes the ‗cause and effect‘ relationships

between the system variables using state estimation or parameter estimation techniques.

The problem with these mathematical model-based techniques is that under real conditions, no accurate models

of the system of interest can be obtained. In that case, the better strategy is of using knowledge-based techniques

where the knowledge is derived in terms of facts and rules from the description of system structure and behavior

(Rajakarunakaran et al., 2008). Classical expert systems were used for this purpose. The major weakness of this

approach is that binary logical decisions with Boolean operators do not reflect the gradual nature of many real

world problems. Recently, with the development of artificial intelligence, Computational Intelligence (CI)

methods, Neural Networks (NN), Fuzzy Logic (FL), Evolutionary Algorithms (EA), etc., more and more fault

diagnostic approaches have emerged as new techniques for fault diagnostic systems (Venkatasubramanian et al.,

2003; Patton et al., 2000) .

Artificial neural networks, by their ability to learn and store a large volume of information, are tools particularly

suitable for diagnostic support systems. Neural networks are known to approximate any non-linear function,

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given suitable weighting factors and architecture. NN can generalize when presented with inputs not appearing

in the training data and make intelligent decisions in cases of noisy or corrupted data. However, the NN operates

as a ―black box‖ with no qualitative information available of the model it represents (Patton et al., 1994) .

In this study, effectiveness of ANNs for fault diagnosis in milk pasteurization process is presented. The initial

data base used for fault diagnosis is constructed using data extracted from FMEA tables, of milk pasteurization

process. Indeed, this analysis makes it possible to establish the links of cause and effect between the faulty

components and the observed symptoms. Three models of ANNs, namely Feed-Forward Back Propagation

(FFBP), Radial Basis Function based Neural Network (RBNN), and Generalized Regression Neural Networks

(GRNN) are developed and compared.

This paper is organized as follows: The next section is devoted to present the review of artificial neural

networks. Section 3 describes the milk pasteurization process. Sections 4 presents the fault detection and

diagnosis in milk pasteurization process. Simulation results of fault diagnosis are presented in Section 5. Finally,

in Section 6, conclusions are drawn from the work.

2. Review of Artificial Neural Networks (ANNs)

2.1 Artificial Neural Networks (ANNs)

Artificial neural networks (ANNs) are computational modeling tools that have recently emerged and found

extensive acceptance in many disciplines such as data processing, process analysis and control, fault detection

and diagnosis, pattern recognition, and defining complex and nonlinear relationship and employs number of

input–output training patterns from the experimental data (Hagan et al., 1996; Chen et al., 2015; Yerrabolu et al.,

2013) . Finding a nonlinear algorithm between inputs and outputs is obtained by natural ability of ANNs. They are made

of nodes or neurons, number of simple computing components, which utilized to form respectively an input

layer, one or more hidden layers and an output layer (Hornik et al., 1990). Flexibility of model and accuracy in

prediction are needed for developing a model. Also, neural networks are suitable for prediction of complex

nonlinear functions compared to other literature models (Carrera and Aires-de-Sousa, 2015; Ghaedi et al., 2015).

An artificial ANN consists of some basic elements called neurons. Input variables are processed through

successive layers of neurons. There is always an input layer, with a number of neurons equal to the number of

variables of the problem and an output layer, where the response is made available with a number of neurons

equal to the desired number of quantities computed from the inputs. Layers between the input and output layers

are called hidden layers and may contain a large number of hidden processing units. The ability to effectively

approximate non-linear systems is due to the presence of this hidden layers and non-linear transfer functions in

the hidden layer‘s neurons. The output of each neuron is determined by using an activation function; usually

nonlinear activation functions are used, such as sigmoid or Gaussian. To obtain the desired output for any given

input, the coefficients should be determined by training the network where sets of inputs with the corresponding

outputs are given to the network through a training algorithm. This process should be repeated several times in

order to minimize the output error. Each run of a complete set is called an epoch (Bishop, 1996; Haykin, 1994).

The neural networks used in this work are Feed-Forward Back Propagation (FFBP), Radial Basis Function based

Neural Network (RBNN), and Generalized Regression Neural Networks (GRNN).

2.2 Feed-Forward Back Propagation (FFBP)

An FFBP network structure has one input layer, one output layer, and at least one hidden layer with hidden

neurons.

Figure 1 illustrates a three-layer neural network consisting of layers i, j, and k, with interconnection weights Wij

and Wjk between layers of neurons. The input signals presented to the system in input layer are processed in

forward through to the hidden layer. The summation of weighted input signals is transferred by a nonlinear

activation function.

The response of network is compared with the actual observation results and the network error is calculated

(Sen, 2004) . The error of network is propagated backwards through the system and the weight coefficients are

updated (Firat et al., 2010). After that, a feed-forward process is again formed until a target total error or number

of prescribed iterations is reached (Partal, 2009). The numbers of hidden layer neurons is found using simple

trial–error method in applications. The detailed theoretical information about FFBP can be found in (Haykin,

1999), ( Medhat et al., 2016), and (Bilhan, 2010).

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2.3 Radial Basis function based Neural Network (RBNN)

Radial-basis function neural networks were proposed by Broomhead and Lowe (1988). An RBNN neural

network is a type of feed-forward network that learns using a supervised training technique, and its output nodes

form a linear combination of the radial basis functions computed by the hidden layer nodes (Hush and Horne,

1993). The RBNN structure consists of an input layer, a single hidden layer, and an output layer as shown in

Figure 2.

The basic functions in the hidden layer produce a significant nonzero response to input stimulus only when the

input falls within a small localized region of the input space. The input-output relationship of this RBNN

network can be described by:

𝑌𝑖 = 𝑊𝑖𝑗𝜑𝑗 𝑥 + 𝑏𝑖𝑁ℎ

𝑗=1 (1)

where φ = the radial basis function of the hidden unit j; x = input data vector; wij represents a weighted

connections between the radial basis function and output layer; Nh = the number of hidden-layer neurons. The

constant term bi in Eq. (1) represents a bias. The hidden neuron of an RBNN has a Gaussian function as its

activation function.

𝜑𝑖 𝑥 = 𝑒𝑥𝑝 − 𝑥−𝑐𝑖

2

2𝜎𝑖2 , 𝑖 = 1,2,𝑁ℎ (2)

Here cis are centers and σi widths (or spreads). ‖. ‖ is the Euclidean distance norm. For simplicity, the centers and

variances are predefined and fixed.

Figure 2: Schematic diagram of RBNN

Outputs Inputs

Output

Layer

Input

Layer

Hidden

Layer

Weights Weights

LLayer LLayer LLayer

Figure 1: Typical feed forward network architecture

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From a design point of view, the training of RBNN networks involves finding the number of hidden layer nodes

(neurons) Nh and the appropriate parameter set (ci, σi and wij) to map a given input vector to a desired output

scalar efficiently with good accuracy and generalization.

2.4 Generalized regression neural network (GRNN)

The generalized regression neural network, as proposed by Donald Specht (Specht, 1991), falls into the category

of probabilistic neural networks. GRNN is a neural network architecture that can solve any function

approximation problem in the sense of estimating a probability distribution function. GRNN is a universal

approximator (Park and Sandberg, 1991) for smooth functions, allowing it to solve any function approximation

and estimate any continuous variable problem when given enough data.

A schematic of the GRNN is shown in Figure 3. The GRNN consists of four layers (Patterson, 1996): input,

pattern, summation, and output layers.

The number of input units in the first layer is equal to the total number of parameters. The first layer is fully

connected to the second, pattern layer, where each unit represents a training pattern and its output is a measure of

the distance of the input from the stored patterns. Each pattern layer unit is connected to the two neurons in the

summation layer: S-summation neuron and D-summation neuron. The S-summation neuron computes the sum of

the weighted outputs of the pattern layer while the D-summation neuron calculates the unweighted outputs of the

pattern neurons. The connection weight between the ith neuron in the pattern layer and the S-summation neuron

is yi; the target output value corresponding to the ith input pattern. For D-summation neuron, the connection

weight is unity (Yilmaz et al., 2010).

The output layer merely divides the output of each S-summation neuron by that of each D-summation neuron,

yielding the predicted value to an unknown input vector x as

𝑌𝑖 = 𝑦𝑖 .𝑒𝑥𝑝 −𝐷 𝑥 ,𝑥𝑖 𝑛𝑖=1

𝑒𝑥𝑝 −𝐷 𝑥 ,𝑥𝑖 𝑛𝑖=1

(3)

Where n indicate the number of training patterns, and the Gaussian D function in (3) is defined as

𝐷 𝑥, 𝑥𝑖 = 𝑥𝑘−𝑥𝑖𝑘

𝜎 2𝑚

𝑘=1 (4)

yi is the weight connection between the ith neuron in the pattern layer and the S-summation neuron, n is the

number of the training patterns, D is the Gaussian function, m is the number of elements of an input vector, and

xk and xik are the jth element of x and xi, respectively. The σ notation, known as the spread (or width), determines

the generalization performance of the GRNN. In general, a larger σ value may result in better generalization; its

optimal value is determined via trial and error. It should be noted that in conventional GRNN applications, all

units in the pattern layer have the same single spread (Specht, 1991).

Figure 3: Schematic diagram of GRNN

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2.5 Performance indices

Various statistical measures have been developed and used in the literature. To assess the fitting and predictive

accuracy of the models, the data sets were mathematically evaluated by calculating the following evaluation

criteria: determination coefficient (R2), root mean squared error (RMSE), and mean absolute error (MAE). In

addition, a graphical comparison was performed to illustrate the accuracy of the proposed models. The

computations of R2, RMSE, and MAE are given below:

𝑅2 = 1 − 𝑂𝑖−𝑃𝑖

2𝑁𝑖=1

𝑂𝑖 2𝑁

𝑖=1

(5)

𝑅𝑀𝑆𝐸 = 1

𝑁 𝑂𝑖 − 𝑃𝑖

2𝑁𝑖=1 (6)

𝑀𝐴𝐸 =1

𝑁 𝑂𝑖 − 𝑃𝑖

𝑁1 (7)

where N is the number of the points in the data set, Oi is some measured value, and Pi is the corresponding model

prediction (Willmott, 1981; Willmott, 1982). In addition, a graphical comparison was performed to illustrate the

accuracy of the proposed models.

3. The proposed fault diagnosis methodology

The proposed methodology for fault detection and diagnosis as shown in Figure 4, is based on using three

ANNs, namely FFBP, RBNN, and GRNN to detect and diagnose the failures which can lead to abnormal

operating conditions. The main purpose of selecting ANNs as a tool is ability to capture the non-linear

relationship between the inputs and the outputs, generalization ability and fast real-time operation. The neural

network approach for this application has two phases; training and testing. During the training phase, neural

network is trained to capture the underlying relationship between the chosen inputs and outputs. After training,

the networks are tested with a test data set, which was not used for training. Once the networks are trained and

tested, they are ready for real-time application. Then, a comparison of the training and testing performances

between the FFBP, RBNN, and GRNN models is carried.

For the application of machine learning approaches, it is important to properly select the input variables, as

ANNs are supposed to learn the relationships between input and output variables on the basis of input–output

pairs provided during training. In this work, the starting data are extracted from the FMEA tables and associated

with an initial rule base for establishing cause-and-effect relationships between the failing organs and the

observed symptoms. These data are used as a database of neural networks, and the cause-and-effect links will be

represented in the form of a binary coding constructing the data set, corresponding to 44 vectors.

The data vectors used in the three models ANNs are intervals limited by two values, minimum and maximum.

The symbol '1' represents a normal functioning of the system, and the symbol '0' represents a failure situation.

FMEA cause-and-

effect

FFBP

RBNN

GRNN

Fault

diagnosis

Figure 1: The proposed methodology for fault diagnosis using three models of ANNs

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4. Study system

In this section, the proposed methodology is applied to milk pasteurization process, the schematic diagram of

which is shown in Figure 5. The process of milk pasteurization consists mainly of five major elements, a control

system, a balancing tank (constant level tank), a plate heat exchanger with four sections, two pumps and valves

which can be interconnected as shown in Figure 5. A detailed explanation of the working of the process is

described in (Djelloul, 2013).

5. Results and discussions

This section presents the details of the training and testing of ANN models for fault diagnosis on the milk

pasteurization process. Three different ANN models were developed for fault diagnosis, FFBP, RBNN, and

GRNN. In all models, 70 % of the data set was randomly assigned as the training set and 30 % was used for

testing the performance of the model predictions. Note that the FFBP, RBNN, and GRNN models employ the

same training and test data sets for appropriate performance comparison. The neural network model is

developed using MATLAB 8.1 Neural Network Toolbox.

The FFBP used here is composed of an input layer and an output layer that respectively contain the effects and

causes of failures obtained from FMEA arrays. The FFBP can have more than one hidden layer; therefore, in this

study, one hidden layer FFBP was used. The tansigmoidal and linear activation functions were used in the input

layer and the output layer, respectively. Moreover, the activation function, in the hidden layers, was chosen as

tansigmoidal function. The number of hidden layer neurons to minimize MSE was found using simple trial and

error with different architectures of all models. The FFBP model, comprising one hidden layer with 10 neurons

has the lowest performance (0.0526) and hence was considered optimal for this study.

In the RBNN model, the key parameter is the spread constant, plays a crucial role in establishing a good ANN

model with high prediction accuracy and stability. Therefore, this parameter needs to be correctly determined on

the basis of the evaluation criteria to optimize prediction performance. The best performance (0.0010) of the

RBNN is obtained by a value of the spread constant that is equal to 0.1.

In this study, different spreads (between 0.01 and 0.8) were tried to find the best value for the GRNN model. The

best testing performance (1.1166 e-62) of the GRNN was obtained when the spread parameter equal to '0,1'.

Tables 1 present the performance during the training, and testing phases of FFBP, RBNN, and GRNN models for

the detection and diagnosis of milk pasteurization process failures, in terms of R2, RMSE, and MAE statistics.

Figure 5: Milk pasteurization process

1- Tank

2- Feed pump

3- Heat exchanger

4- Valve

5- Pipe

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Table 1 show that the GRNN performed better during training and testing, and it outperforms the FFBP and

RBNN in terms of all the standard statistical measures.

RMSE provides a measure to judge the accuracy of the fit of the models. A lower RMSE indicates a better fit. As

seen from Table 1 that the GRNN model has the smallest RMSE (1.1444e-31) and MAE (1.6601e-32), and the

highest R2 (1) in training period; also in testing period the GRNN has the smallest RMSE (8.0179e-32) and MAE

(8.1494e-33), and the highest R2 (1). According to the training and testing results, the RBNN and FFBP models

provide RMSE, and MAE values close to each other. The RMSE and MAE values of the FFBP model are higher

than the corresponding values of the RBNN model in the training and testing periods, and the values of the

determination coefficient for the two models are identical in both periods.

A very high R2 value (R

2 = 1) for the GRNN model means that it has a better linear relationship (perfect linear

regression) between the observed and calculated failures, compared to the FFBP and RBNN models.

The scatter plots of the observed versus simulated failures of the FFBP, RBNN, and GRNN analyzed herein are

shown in Figures 6, 7, and 8 for the training and testing phases, respectively. It can be obviously seen from the

Figures 6, 7, and 8 that the GRNN simulations are closer to the corresponding observed failures than those of the

RBNN and FFBP. For the training and testing phases, a total coincidence is observed with the adjustment line of

the GRNN model with respect to the RBNN and FFBP models.

The Figures 6, 7, and 8 shows that model performances is generally accurate and that the GRNN model is

consistently superior to the RBNN and FFBP models. Overall, the performance of all models is very satisfying.

The results demonstrate that the GRNN can be applied with better performance, to establish diagnostic models.

6. Conclusion

In this paper, a fault diagnosis system using artificial neural networks was proposed. The FFBP, RBNN, and

GRNN models were developed to detect and diagnose failures of milk pasteurization process. The predictive

performance of each model was assessed using three statistical measures: R2, RMSE, MAE, and a study of the

graphs were used. The results of the statistical measures suggest that GRNN model provides more accurate

results than the FFBP and RBNN models. The high value of the determination coefficient and the low value of

RMSE and MAE in the testing set indicate that the developed models can be used for prediction failures of milk

pasteurization process.

This study has indicated that the GRNN model is the best predictor of failures among three ANN models in

respect to R2, RMSE, and MAE statistics. GRNN model can be successfully employed in fault diagnosis.

RBNN model has lower RMSE and MAE than FFBP model. A lower RMSE means that the accuracy of the

RBNN model is higher than FFBP model. The ranking of prediction was obtained as GRNN, RBNN, and FFBP,

respectively.

Table I: Performances of the FFBP, RBNN, and GRNN models in the training and

testing phases

ANNs

Models

Training Testing

R2

RMSE MAE R2

RMSE MAE

FFBP 0.99978 0.1925 0.0370 0.99958 0.3015 0.0909

RBNN

0.99983 0.0221 9.7752e-04

0.99963 0.0483 0.0047

GRNN 1 1.1444e-31 1.6601e-32 1 8.0179e-32 8.1494e-33

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0 5 10 15 20 25 30 35 40 45 50

0

5

10

15

20

25

30

35

40

45

50

R² = 0.99978

Observed Failures

Calc

ula

ted

Fail

ure

s

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

25

30

35

40

45

50

R² = 0.99958

Observed Failures

Calc

ula

ted

Fail

ure

s

Figure 6: Scatter plots of calculated versus observed failures for

the FFBP for (a) training and (b) testing phases

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

25

30

35

40

45

50

R² = 0.99963

Observed Failures

Calc

ula

ted

Fail

ure

s

0 5 10 15 20 25 30 35 40 45 50

0

5

10

15

20

25

30

35

40

45

50

R² = 0.99983

Observed Failures

Calc

ula

ted

Fail

ure

s


the RBNN for (a) training and (b) testing phases

0 5 10 15 20 25 30 35 40 45 50

0

5

10

15

20

25

30

35

40

45

50

R² = 1

Observed Failures

Calc

ula

ted

Fail

ure

s

0 5 10 15 20 25 30 35 40 45 50

0

5

10

15

20

25

30

35

40

45

50

R² = 1

Observed Failures

Calc

ula

ted

Fail

ure

s


the GRNN for (a) training and (b) testing phases

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Biographies

Chebira Samia is a professor at the Institute of Hygiene and Industrial Safety at Mustafa Ben Boulaid BATNA2

University, Batna, Algeria, and she is member of the Industrial Prevention Research Laboratory (LRPI). She

holds magister in control of industrial risks. She is interested, since the late 1990s, the safe operation and fault

diagnosis of industrial process. His current research themes concern the fault diagnosis using artificial neural

networks.

Institute of Health & Industrial Safety, Mustafa Ben Boulaid BATNA 2 University, Fesdis Road Constantine,

Batna, Algeria.

[email protected]

Bourmada Noureddine is a professor at the Institute of Hygiene and Industrial Safety at Mustafa Ben Boulaid

BATNA2 University, Batna, Algeria, he is doctor of science in spectrochemistry, and he is member of the

Industrial Prevention Research Laboratory (LRPI). He has been director of the Institute of Hygiene and

Industrial Safety since 2001 to 2016. His research focuses on the analysis of environmental risks.

Institute of Health & Industrial Safety, Mustafa Ben Boulaid BATNA 2 University, Fesdis road Constantine,

Batna, Algeria.

[email protected]

Boughaba Abdelali is Professor at the Institute of Hygiene and Industrial Safety at Mustafa Ben Boulaid

BATNA 2University, Batna, Algeria, he is doctor of science in electro-technical, and he is member of the

Industrial Prevention Research Laboratory (LRPI). He was deputy director in charge of post graduation and

currently he deputy director of pedagogy. Since the beginning of the 1990s, he has been interested in the

numerical control of electrical machines and the diagnosis of failures in industrial and particularly electrical

systems. His current research interests concern the control of motors without collectors (BLDCM), the fault

diagnosis using artificial neural networks.

Institute of Health & Industrial Safety, Mustafa Ben Boulaid BATNA 2 University, Fesdis road Constantine,

Batna, Algeria.

[email protected]

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