- Email: [email protected]
Fuzzy data analysis — Methods and industrial applications
Fuzzy Sets and Systems 61 (1994) 19-28 North-Holland
19
Fuzzy data analysis - Methods and industrial applications Willi Meier", Richard Weber a and Hans-Jurgen Zimmermann b aMIT GmbH, 52076 Aachen, Germany bELITE Foundation, RWTH Aachen Institute of Technology, 52076 Aachen, Germany Received 1 September 1993 Revised 1 October 1993
Abstract: Many
industrial problems require adequate interpretation of data which are present in the respective situations. For example process monitoring, diagnosis, quality control, and prediction are some of these tasks. All the related problems have in common that a large amount of data describing the respective area exists. But in most cases the information contained in the data is not used sufficiently. Since the above described problems have different characteristics a multitude of methods for analysing the existing data is needed to solve the related problems. In this article we give an overview over advanced methods for data analysis, present a software tool which supports the application of these methods, and show some industrial realizations to emphasize the benefits of advanced data analysis.
Keywords: Cluster
analysis; pattern recognition; data analysis; neural networks; production and process control; quality control.
1. Introduction
This article presents approaches of data analysis with intelligent technologies as for example fuzzy technology and neural networks. After having seen the wave of successful industrial applications of fuzzy control, data analysis has become a very fast growing and important area where fuzzy and neural methods are applied. Especially their combination offers high potentials for future use. In Chapter 2 a brief introduction to data Correspondence to: Dr. R. Weber, MIT Management lntelligenter Technologien GmbH, Promenade 9, D-52076 Aachen, Germany.
analysis and the related terminology is given. Chapter 3 proposes possibilities to support a potential user with methods and tools for data analysis. While methods used in fuzzy control are based primarily on the formulation of fuzzy If-Then rules, data analysis requires several different methods as shown briefly in Chapter 3.1. In Chapter 3.2 a software-tool which contains the respective approaches is presented. Chapter 4 describes some industrial applications in which methods for data analysis are used and further possible applications are pointed out. The conclusions in Chapter 5 show some directions for future developments of data analysis. 2. Basics of data analysis
In general, data analysis can be considered as a process in which starting from some given data sets information about the respective application is generated. In this sense data analysis can be defined as search for structure in data [4]. In order to clarify the terminology about data analysis used throughout this paper a brief description of its general process is given below. In data analysis objects are considered which are described by some attributes. Objects can be for example persons, things (machines, products, ...), time series, sensor signals, process states, and so on. The specific values of the attributes are the data to be analyzed. The overall goal is to find structure (information) about these data. This can be achieved by classifying the huge amount of data into relatively few classes of similar objects. This leads to a complexity reduction in the considered application which allows for improved decisions based on the gained information. Figure 1 shows the process of data analysis described so far which can be separated into feature analysis, classifier design, and classification.
0165-0114/94/$07.00 © 1994--Elsevier Science Publishers B.V. All rights reserved SSDI: 0165-0114(93)E0192-U
20
W. Meier et al. / Fuzzy data analysis
1. Feature Analysis
(xl)
Features Objects Xl]
:
Xt2
X,r
2. Classifier Design Object Xt with features
-
W. Meier et al. / Fuzzy data analysis - Methods and industrial applications
becomes necessary to filter these data in order to overcome the problems of noisy input (see also Chapter 4.2). In addition to these filter methods some transformations of the measured data as, for example, Fast Fourier Transformation (FFF) could improve the respective results. Both, filter methods as well as FFF, belong to the class of signal processing techniques. Data Preprocessing includes signal processing and also conventional statistical methods. Statistical approaches could be used to detect relationships within a data set describing a special kind of application. Here correlation analysis, regression analysis, and discrimination analysis can be applied adequately. These methods could be used for example to facilitate the process of feature extraction (see Chapter 2). If, for example, two features from the set of available features are highly correlated, it could be sufficient for a classification to consider just one of these two.
21
described by several features, to fuzzy classes. Objects belong to these classes with different degrees of membership. Here no explicitly formulated expert knowledge is required for the task of data analysis. If an expert has some knowledge about the analysis of data (as for example in the area of diagnosis), this knowledge should be used for the evaluation. Then knowledge-based methods for fuzzy data analysis are suitable [17]. This class is similar to the approach taken in fuzzy control systems where fuzzy If-Then rules are formulated and a process of fuzzyfication, inference, and defuzzyfication leads to the final decision [18]. The automatic construction of such systems can be supported by fuzzy techniques from the area of machine learning; see e.g. [14]. If an expert can not describe his knowledge explicitly but is able to deliver some examples for 'correct decisions' which contain the expert knowledge implicitly, a neural network can be trained with these training examples, see e.g. [9].
Classifier design and classification In order to find classes in some data sets, methods for classifier design and classification can be used. Based on the specific data analysis formulation these tasks can be performed with algorithmic techniques as for example clustering methods, knowledge-based systems, and neural networks. Which of these methods is most appropriate depends on the specific problem structure, see also [10, 15]. In the literature, a lot of different algorithmic methods for data analysis have been suggested [5, 13]. One of the most frequently used cluster algorithms which has been applied very extensively so far is the Fuzzy c-means (FCM) [2]. This algorithm assigns objects, which are
New developments of methods for data analysis Recently a lot of research efforts are directed towards the combination of different intelligent techniques. Here the elaboration of neuro-fuzzy systems is one cornerstone for the future development of intelligent machines, see e.g. [6]. One of these methods is a fuzzy version of Kohonen's network [3] (Figure 2). It is expected that in the near future the areas of fuzzy technology, neural networks, and genetic algorithms will be combined to a higher degree. Especially for data analysis the combination of these methods could give promising results.
supervised learning neu ro
neuro - fuzzy
Back -
propagation
Fu z zy Back -
propagation
unsupervised learning Kohonen
network
Fuzzy
Kohonen network
Fig. 2. Neural and neuro-fuzzy methods for data analysis.
22
W. Meier et al. / F u z z y data analysis - Methods and industrial applications
performance in time-critical applications.
3.2. DataEngine - A software-tool for data analysis DataEngine is a software tool that contains methods for data analysis which are described above. Especially the combination of signal processing, statistical analysis, and intelligent systems for classifier design and classification leads to a powerful software tool which can be used in a very broad range of applications (Figure 3). DataEngine is written in an object oriented concept in C + + and runs on all usual hardware platforms. Interactive and automatic operation supported by an efficient and comfortable graphical user interface facilitates the application of data analysis methods. In general, applications of that kind are performed in the following three steps:
Modelling of a specific application with DataEngine. Each sub-task in an overall data analysis application is represented by a so called function block in DataEngine. Such function blocks represent software modules which are specified by their input interfaces, output interfaces, and their function. Examples are a certain filter method or a specific cluster algorithm. Function blocks could also be hardware modules like neural network accelerator boards. This leads to a very high
Classifier design (off-line data analysis). After having modeled the application in DataEngine off-line analysis has to be performed with given data sets to design the classifier. This task is done without process integration. Classification. Once the classifier design is finished, the classification of new objects can be executed. Depending on the specific requirements this step can be performed in an on-line or off-line mode. If data analysis is used for decision support (e.g. in diagnosis or evaluation tasks) objects are classified off-line. Data analysis could also be applied to process monitoring and other problems where on-line classification is crucial. In such cases, direct process integration is possible by configuration of function blocks for hardware interfaces (Figure 4). 4. Industrial applications Here two applications of advanced methods for data analysis are shown in order to emphasize the wide range of related problems and the high potentials for industrial use. In both cases the above described tool DataEngine was used to solve the respective problems of data analysis.
[-.... m
Data Preprocessing
Data "~npu!
i ] I [ |
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File Serial Ports Data Acquisition Boards
Knowledgo Based Data Analysis
I '
. IBM-Compatible (MS Windows) - Sun SPARe II (MOTIF) - other platforms User I~terfaee: - graphical Programming - interactive and automatic modes
"~
"
~
[ C + + P . . . . pi|er for [ _ _ ~ -- *Alsorithmic C l a s s i f i ~ *Rulebased S y s t e m s / ~ * Neural Nets
/
V
Structure Output
Fig. 3. Structure of DataEngine.
Data ~utput
File Serial Ports Data Acquisition Boards Printer _ _ / 2D and 3D Graphics
Neural Data Analysis
Hardwareplat forms:
~
.,~
23
W. Meier et al. / Fuzzy data analysis - Methods and industrial applications
File
_Confl..~. VVindow
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i .
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.
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4.1. Maintenance management in petrochemical plants Problem formulation Over 97% of the worldwide annual commercial production of ethylene is based on thermal cracking of petroleum hydrocarbons with steam [12]. This process is commonly called pyrolysis or steam cracking. Naphtha, which is obtained by distillation of crude oil, is the principal raw ethylene material. Boiling ranges, densities, and compositions of Naphtha depend on crude oil quality. Naphtha is heated in cracking furnaces up to 820°C-840°C, where the chemical reaction starts. The residence time of the gas stream in the furnace is determined by the severity of the cracking process. The residence time for low severity is about 1 s, for high severity 0.5 s. The severity of the cracking process specifies the
product distribution. By high severity cracking the amount of ethylene in the product stream is increased and the amount of propylene is decreased significantly. After the cracking reaction the gasstream has to be cooled very quickly to avoid further chemical reactions. This process is called quenching. After that the product stream is fractionated several times and the ethylene is purified. Commercial thermal cracking plants produce about 360 000 t ethylene a year. During the cracking process also acetylenic, diolefenic and aromatic compounds are produced, which are known to deposit coke on the inside surfaces of the furnace tubes. This coke layer inhibits heat transfer from the tube to the process gas, so that at some time the furnace must be shut down to remove the coke. To guarantee a continuous run of the whole plant, several furnaces are parallel integrated into the production process. The crude on-line measured
W. Meier et al. / F u z z y data analysis - Methods and industrial applications
24
process data is not suitable for determining the degree of coking. About 20 different measurements of different indicators, such as temperatures, pressures, or flows, are taken every minute. By regarding only this data it is not possible for the operator to decide whether the furnace is coked or not. His experience and the running time of the regarded furnace is the basis for his decision. Some work has been done to give computational support concerning the coking problem [8]. There, an expert system was used to determine times of decoking processes. In the next chapter a method is described, which is suitable to determine the degree of coking based on on-line measured process data.
Solution by data analysis Clustering methods compress information of data sets by finding classes, which can be used for a classification [2]. Similar objects are assigned to the same class. In our case objects are different states of a cracking furnace during a production period. Objects are described by different features. Features are the on-line measured quantities like temperatures etc. The problem is to find the right features for the regarded problem. There are some mathematical methods like principal component
analysis to reduce the number of features down to three or two. Now graphical methods can be used to see and recognize the dependencies. Normally the loss of information is too big when using these techniques. Figure 5 sketches the principle way of analysing process data by clustering methods. Modern process control systems collect the data and archive them. Based on this archived data set, the classifier is designed with the help of clustering. For this task the support of experts of the plants is also required. Each feature leads to one dimension of the feature space. Clustering algorithms find accumulations of objects in that space. These accumulations are the different classes. A new object can now be classified. Therefore it is important that the so found classes can be interpreted by the practitioner. He may recognize that one class contains good process states, the other class bad ones. So the information, which is hidden in a big data set, can be compressed by finding classes and designing a classifier. With fuzzy classification, processes can be studied, which continuously move from one state to another. One of these processes is the above described coking of cracking furnaces. After the production period the furnace is shut down and the coke is burned out with a mixture of steam and air.
Current Process Process
IIIIII
IIIIII
1 1
Features: F1, F2, ...
T Feature Selection
Classifier Design
Expert Fig. 5. Analysing process data.
Classification
Process Analysis
W. Meier et al. / Fuzzy data analysis - Methods and industrial applications
25
Cracking-Furnace
~
exhaust gasj~ i
/[ exhaust gas ~ control
~emperatur~.~-! i
zone 1
i
m
zone 2 temperature
/
L
Fig. 6. Cracking furnace. After that the furnace is reintegrated into the production process until it has to be decoked again. The state of the furnace is described by several features. Figure 6 shows a brief sketch of the furnace and the measured features. For the determination of coking of a furnace it is not necessary to find classes by clustering. Two different classes describing the coked and decoked states are already known. The center of these classes in the multidimensional feature space are also known, so that the classifier can be built from the history of the process data. After a decoking process the values of the features for this state can be acquired. A short time before a decoking process the values of the coked state are obtained analogously. This
classifier can be used to classify the current furnace state and to support the operator's decision, whether the furnace is coked or not. Results and discussion Cluster-methods mentioned in Chapter 3.1 were used to determine the coking of 10 cracking furnaces of a thermal cracker [11]. The data of one year have been analyzed. The process of coking lasts about 60 days. Therefore only mean values of a day of the measured quantities were considered. Each object (furnace) is described by features sketched in Figure 6. For different furnaces the centers of coked and decoked classes were found by searching for coked and decoked states in the data set.
26
W. Meier et al. / F u z z y data analysis - Methods and industrial applications
temperature In °C
membership
1200
u(i) 1.0
1000 0.8-
800 -
coked 0,6-
600
/
400 -
0.4-
J
J
transition - s t a t e
200 0.2
o 60
120
180
240
300
360
days
0.0
i
40
50
60
70
80
90
lOO
days
Fig. 7. Furnace temperature.
Fig. 9. Transition of process states.
Figure 7 shows the temperature profile of a furnace during the whole year. Characteristic peaks, where temperature decreases significantly, result from decoking processes. K1 and K2 describe decoked and coked states of the furnace. The temperature profile shows no characteristic shape, which results from coking. Furnace temperature is only one of the features sketched in Figure 6. There are dependencies between features, so that a determination of coking is not possible considering only the feature 'temperature'. The whole feature set shown in Figure 6 is suitable to find coked and decoked classes and to build a classifier, which can be used to classify current furnace states. In Figure 8 the process of classification is sketched schematically. During a production period the furnace state starts at the decoked class and runs continuously towards the coked class.
Figure 9 shows the membership values of a furnace state during a production period using the classifier. The values describe the membership of the current furnace state to the coked class. The membership values increase continuously and reach nearly 1 at the end of the production period. By using the classifier the information, which is hidden in the data set, is compressed. The membership value sketched in Figure 9 shows the degree of coking and hence the feature which describes coking of cracking furnaces. The classifier works on-line and classifies the current furnace state concerning the coking problem. The operator can use this information to check how long the regarded furnace will be able to run until it has to be decoked. Now it is easier to make arrangements concerning logistical questions like ordering right amounts of raw material or not to be understaffed at certain times.
4.2. Acoustic quality control K1 arbitrary units
............... arbitrary units
L ~
Fig. 8. Fuzzy classification of a continuous process.
In acoustic quality control many efforts have been undertaken to automatize the respective control tasks which are usually performed by humans. Even if there are many computerized systems for automatic quality control by analysis of acoustic signals, some of the problems could not yet be solved adequately. Here an example of acoustic control of ceramic goods is presented to show the potentials of fuzzy data analysis in this respect.
27
W. Meier et al. / F u z z y data analysis - Methods and industrial applications
A/DConverter Data Acquisition Board
DataEngine
Fig. 10. Automized quality control of tiles.
Problem formulation In cooperation with a producer of tiles a prototype has been built which shows the potentials of automatic quality control. So far an employee of this company has to check the quality of the final product by hitting it with a hammer and deciding about the quality of the tile based on the resulting sound. Since cracks in the tile cause an unusual sound an experienced worker can distinguish between good and bad tiles. Solution process In this application algorithmic methods for classifier design and classification were used to detect cracks in tiles. In the experiments the tiles are hit automatically and the resulting sound is recorded via a microphone and an A/Dconverter. (See Figure 10.) Then signal processing methods like filtering and Fast-Fourier-Transformation (FFT) transform these sound data into a spectrum which can
be analyzed. For example, the time signal is transformed by an FFT into the frequency spectrum. From this frequency spectrum several characteristic features are extracted which could be used to distinguish between good and bad tiles. The feature values are the sum of amplitude values in some specified frequency intervals. In the experiments a 6-dimensional feature vector showed best results. After this feature extraction the fuzzy c-means algorithm found fuzzy classes which could be interpreted as good and bad tiles. Since a strict distinction between these two classes is not always possible fuzzy cluster techniques have the advantage that they do not only distinguish bad from good tiles but that intermediate qualities can also be defined (Figure 11). Based on this prototype an automatic system for acoustic quality control can be installed at the production lines. In the future this prototype will be enlarged to support also the optical quality control by methods of computer vision
FourierTransformation MDI Converter ] Intensity [ Data 1===~ IAcquisitionl [Board [
A
[m v ]
pI
Feature Extraction " ~
tud
[ ClusteringI F¢
o Time Ims--J
~meal
Frequency
[kHz]
Frequemeyspectrum
~'~ Feature space
DataEngine Fig. 11. Application of DataEngine for acoustic quality control.
F1 Clustering
28
W. Meier et al. / Fuzzy data analysis - Methods and industrial applications
[7]. Especially if the overall quality of tiles has to be evaluated fuzzy technology offers methods to aggregate different evaluations as for example acoustical and optical ones. A lot of research has been done in this respect in the past [19].
5. Conclusion Data analysis has large potentials for industrial applications. As shown in this article it can lead to the automation of tasks which are too complex or too ill-defined to be solved satisfactorily with conventional techniques. This can result in the reduction of cost, time, and energy which also improves environmental criteria. In contrast to fuzzy controllers where the behaviour of the controlled system can be observed and therefore the performance of the controller can be stated immediately, many applications of methods for data analysis have in common that it will take some time to exactly quantify their influences. In the near future many beneficial applications of data analysis are expected and their advantages will be reported.
6. References [1] H. Bandemer and W. Nather, Fuzzy Data Analysis (Kluwer, Dordrecht, 1992). [2] J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum Press, New York, 1981). [3] J.C. Bezdek, E.C.-K. Tsao and N.R. Pal, Fuzzy Kohonen clustering networks, in: IEEE Int. Conf. on Fuzzy Systems (San Diego, 1992) 1035-1043.
[4] J.C. Bezdek and S.K. Pal (Eds.), Fuzzy Models for Pattern Recognition (IEEE Press, New York, 1992). [5] A. Kandel, Fuzzy Techniques in Pattern Recognition (John Wiley and Sons, New York, 1982). [6] B. Kosko, Neural Networks and Fuzzy Systems (Prentice-Hall, Englewood Cliffs, 1992). [7] R. Krishnapuram and J. Lee, Fuzzy-set-based hierarchical networks for information fusion in computer vision, Neural Networks 5 (1992) 335-350. [8] P.A. Paardekooper, C. van Leeuwen, H. Koppelaar and A.G. Montfoort, Simulatie van een ethyleenfabriek bespaart tijd en moeite, PT Polytechnische tijdschrift, Simulatie (1990) 30-34 (in Dutch). [9] Y.-H. Pao, Adaptive Pattern Recognition and Neural Networks (Addison-Wesley, Reading, MA., 1989). [10] R. Schalkoff, Pattern Recognition Statistical, Structural and Neural Approaches (John Wiley and Sons, New York, 1992). [11] B. Trompeta and W. Meier, Erfahrungen bei der Prozel3identifikation von verfahrenstechnischen GroBanlagen, 2. Anwendersymposium zu Fuzzy Technologien 23.-24. March 1993, Aachen (in German). [12] Ullmanns Encyclopedia of Technical Chemistry, Vol. 8, 4th Edition (New York, 1982). [13] J. Watada, Methods for fuzzy classification, Japanese Journal of Fuzzy Theory and Systems 4 (1992) 149-163. [14] R. Weber, Fuzzy-ID3: A class of methods for automatic knowledge acquisition, Proc. of the 2nd Int. Conf. on Fuzzy Logic and Neural Networks (lizuka, Japan, July 1992) 265-268. [15] S.M. Weiss and C.A. Kulikowski, Computer Systems that Learn (Morgan Kaufmann, San Mateo, 1991). [16] H.-J. Zimmermann, Fuzzy sets in pattern recognition, in: P.A. Devijer and J. Kittler, Eds., Pattern Recognition Theory and Applications (Springer-Verlag, Berlin, 1987) 383-391. [17] H.-J. Zimmermann, Fuzzy Sets, Decision Making, and Expert Systems (Kluwer, Boston, 1987). [18] H.-J. Zimmermann, Fuzzy Set Theory - And Its Applications, 2nd rev. ed. (Kluwer, Boston, 1991). [19] H.-J. Zimmermann and P. Zysno, Latent connectives in human decision making, Fuzzy Sets and Systems 4 (1980) 37-51.
19
Fuzzy data analysis - Methods and industrial applications Willi Meier", Richard Weber a and Hans-Jurgen Zimmermann b aMIT GmbH, 52076 Aachen, Germany bELITE Foundation, RWTH Aachen Institute of Technology, 52076 Aachen, Germany Received 1 September 1993 Revised 1 October 1993
Abstract: Many
industrial problems require adequate interpretation of data which are present in the respective situations. For example process monitoring, diagnosis, quality control, and prediction are some of these tasks. All the related problems have in common that a large amount of data describing the respective area exists. But in most cases the information contained in the data is not used sufficiently. Since the above described problems have different characteristics a multitude of methods for analysing the existing data is needed to solve the related problems. In this article we give an overview over advanced methods for data analysis, present a software tool which supports the application of these methods, and show some industrial realizations to emphasize the benefits of advanced data analysis.
Keywords: Cluster
analysis; pattern recognition; data analysis; neural networks; production and process control; quality control.
1. Introduction
This article presents approaches of data analysis with intelligent technologies as for example fuzzy technology and neural networks. After having seen the wave of successful industrial applications of fuzzy control, data analysis has become a very fast growing and important area where fuzzy and neural methods are applied. Especially their combination offers high potentials for future use. In Chapter 2 a brief introduction to data Correspondence to: Dr. R. Weber, MIT Management lntelligenter Technologien GmbH, Promenade 9, D-52076 Aachen, Germany.
analysis and the related terminology is given. Chapter 3 proposes possibilities to support a potential user with methods and tools for data analysis. While methods used in fuzzy control are based primarily on the formulation of fuzzy If-Then rules, data analysis requires several different methods as shown briefly in Chapter 3.1. In Chapter 3.2 a software-tool which contains the respective approaches is presented. Chapter 4 describes some industrial applications in which methods for data analysis are used and further possible applications are pointed out. The conclusions in Chapter 5 show some directions for future developments of data analysis. 2. Basics of data analysis
In general, data analysis can be considered as a process in which starting from some given data sets information about the respective application is generated. In this sense data analysis can be defined as search for structure in data [4]. In order to clarify the terminology about data analysis used throughout this paper a brief description of its general process is given below. In data analysis objects are considered which are described by some attributes. Objects can be for example persons, things (machines, products, ...), time series, sensor signals, process states, and so on. The specific values of the attributes are the data to be analyzed. The overall goal is to find structure (information) about these data. This can be achieved by classifying the huge amount of data into relatively few classes of similar objects. This leads to a complexity reduction in the considered application which allows for improved decisions based on the gained information. Figure 1 shows the process of data analysis described so far which can be separated into feature analysis, classifier design, and classification.
0165-0114/94/$07.00 © 1994--Elsevier Science Publishers B.V. All rights reserved SSDI: 0165-0114(93)E0192-U
20
W. Meier et al. / Fuzzy data analysis
1. Feature Analysis
(xl)
Features Objects Xl]
:
Xt2
X,r
2. Classifier Design Object Xt with features
-
W. Meier et al. / Fuzzy data analysis - Methods and industrial applications
becomes necessary to filter these data in order to overcome the problems of noisy input (see also Chapter 4.2). In addition to these filter methods some transformations of the measured data as, for example, Fast Fourier Transformation (FFF) could improve the respective results. Both, filter methods as well as FFF, belong to the class of signal processing techniques. Data Preprocessing includes signal processing and also conventional statistical methods. Statistical approaches could be used to detect relationships within a data set describing a special kind of application. Here correlation analysis, regression analysis, and discrimination analysis can be applied adequately. These methods could be used for example to facilitate the process of feature extraction (see Chapter 2). If, for example, two features from the set of available features are highly correlated, it could be sufficient for a classification to consider just one of these two.
21
described by several features, to fuzzy classes. Objects belong to these classes with different degrees of membership. Here no explicitly formulated expert knowledge is required for the task of data analysis. If an expert has some knowledge about the analysis of data (as for example in the area of diagnosis), this knowledge should be used for the evaluation. Then knowledge-based methods for fuzzy data analysis are suitable [17]. This class is similar to the approach taken in fuzzy control systems where fuzzy If-Then rules are formulated and a process of fuzzyfication, inference, and defuzzyfication leads to the final decision [18]. The automatic construction of such systems can be supported by fuzzy techniques from the area of machine learning; see e.g. [14]. If an expert can not describe his knowledge explicitly but is able to deliver some examples for 'correct decisions' which contain the expert knowledge implicitly, a neural network can be trained with these training examples, see e.g. [9].
Classifier design and classification In order to find classes in some data sets, methods for classifier design and classification can be used. Based on the specific data analysis formulation these tasks can be performed with algorithmic techniques as for example clustering methods, knowledge-based systems, and neural networks. Which of these methods is most appropriate depends on the specific problem structure, see also [10, 15]. In the literature, a lot of different algorithmic methods for data analysis have been suggested [5, 13]. One of the most frequently used cluster algorithms which has been applied very extensively so far is the Fuzzy c-means (FCM) [2]. This algorithm assigns objects, which are
New developments of methods for data analysis Recently a lot of research efforts are directed towards the combination of different intelligent techniques. Here the elaboration of neuro-fuzzy systems is one cornerstone for the future development of intelligent machines, see e.g. [6]. One of these methods is a fuzzy version of Kohonen's network [3] (Figure 2). It is expected that in the near future the areas of fuzzy technology, neural networks, and genetic algorithms will be combined to a higher degree. Especially for data analysis the combination of these methods could give promising results.
supervised learning neu ro
neuro - fuzzy
Back -
propagation
Fu z zy Back -
propagation
unsupervised learning Kohonen
network
Fuzzy
Kohonen network
Fig. 2. Neural and neuro-fuzzy methods for data analysis.
22
W. Meier et al. / F u z z y data analysis - Methods and industrial applications
performance in time-critical applications.
3.2. DataEngine - A software-tool for data analysis DataEngine is a software tool that contains methods for data analysis which are described above. Especially the combination of signal processing, statistical analysis, and intelligent systems for classifier design and classification leads to a powerful software tool which can be used in a very broad range of applications (Figure 3). DataEngine is written in an object oriented concept in C + + and runs on all usual hardware platforms. Interactive and automatic operation supported by an efficient and comfortable graphical user interface facilitates the application of data analysis methods. In general, applications of that kind are performed in the following three steps:
Modelling of a specific application with DataEngine. Each sub-task in an overall data analysis application is represented by a so called function block in DataEngine. Such function blocks represent software modules which are specified by their input interfaces, output interfaces, and their function. Examples are a certain filter method or a specific cluster algorithm. Function blocks could also be hardware modules like neural network accelerator boards. This leads to a very high
Classifier design (off-line data analysis). After having modeled the application in DataEngine off-line analysis has to be performed with given data sets to design the classifier. This task is done without process integration. Classification. Once the classifier design is finished, the classification of new objects can be executed. Depending on the specific requirements this step can be performed in an on-line or off-line mode. If data analysis is used for decision support (e.g. in diagnosis or evaluation tasks) objects are classified off-line. Data analysis could also be applied to process monitoring and other problems where on-line classification is crucial. In such cases, direct process integration is possible by configuration of function blocks for hardware interfaces (Figure 4). 4. Industrial applications Here two applications of advanced methods for data analysis are shown in order to emphasize the wide range of related problems and the high potentials for industrial use. In both cases the above described tool DataEngine was used to solve the respective problems of data analysis.
Data Preprocessing
Data "~npu!
i ] I [ |
Algorithmic Data Analysis
File Serial Ports Data Acquisition Boards
Knowledgo Based Data Analysis
I '
. IBM-Compatible (MS Windows) - Sun SPARe II (MOTIF) - other platforms User I~terfaee: - graphical Programming - interactive and automatic modes
"~
"
~
[ C + + P . . . . pi|er for [ _ _ ~ -- *Alsorithmic C l a s s i f i ~ *Rulebased S y s t e m s / ~ * Neural Nets
/
V
Structure Output
Fig. 3. Structure of DataEngine.
Data ~utput
File Serial Ports Data Acquisition Boards Printer _ _ / 2D and 3D Graphics
Neural Data Analysis
Hardwareplat forms:
~
.,~
23
W. Meier et al. / Fuzzy data analysis - Methods and industrial applications
File
_Confl..~. VVindow
~
i .
File
~ .
U .
Edit Table
Card Edit
Help
.
Configure
Math.
Statistic.,
SignallZroc. Graphics Intensity ImV]
[ntPn, itv
I rn'~ I
Intensitj
ImVl
In*ensity[ mY]
7
T
8 9 10
.,
11 12 13
14 15
o0
Time(sec ]
1~:~ 3--
m Fig. 4. Screenshot of DataEngine.
4.1. Maintenance management in petrochemical plants Problem formulation Over 97% of the worldwide annual commercial production of ethylene is based on thermal cracking of petroleum hydrocarbons with steam [12]. This process is commonly called pyrolysis or steam cracking. Naphtha, which is obtained by distillation of crude oil, is the principal raw ethylene material. Boiling ranges, densities, and compositions of Naphtha depend on crude oil quality. Naphtha is heated in cracking furnaces up to 820°C-840°C, where the chemical reaction starts. The residence time of the gas stream in the furnace is determined by the severity of the cracking process. The residence time for low severity is about 1 s, for high severity 0.5 s. The severity of the cracking process specifies the
product distribution. By high severity cracking the amount of ethylene in the product stream is increased and the amount of propylene is decreased significantly. After the cracking reaction the gasstream has to be cooled very quickly to avoid further chemical reactions. This process is called quenching. After that the product stream is fractionated several times and the ethylene is purified. Commercial thermal cracking plants produce about 360 000 t ethylene a year. During the cracking process also acetylenic, diolefenic and aromatic compounds are produced, which are known to deposit coke on the inside surfaces of the furnace tubes. This coke layer inhibits heat transfer from the tube to the process gas, so that at some time the furnace must be shut down to remove the coke. To guarantee a continuous run of the whole plant, several furnaces are parallel integrated into the production process. The crude on-line measured
W. Meier et al. / F u z z y data analysis - Methods and industrial applications
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process data is not suitable for determining the degree of coking. About 20 different measurements of different indicators, such as temperatures, pressures, or flows, are taken every minute. By regarding only this data it is not possible for the operator to decide whether the furnace is coked or not. His experience and the running time of the regarded furnace is the basis for his decision. Some work has been done to give computational support concerning the coking problem [8]. There, an expert system was used to determine times of decoking processes. In the next chapter a method is described, which is suitable to determine the degree of coking based on on-line measured process data.
Solution by data analysis Clustering methods compress information of data sets by finding classes, which can be used for a classification [2]. Similar objects are assigned to the same class. In our case objects are different states of a cracking furnace during a production period. Objects are described by different features. Features are the on-line measured quantities like temperatures etc. The problem is to find the right features for the regarded problem. There are some mathematical methods like principal component
analysis to reduce the number of features down to three or two. Now graphical methods can be used to see and recognize the dependencies. Normally the loss of information is too big when using these techniques. Figure 5 sketches the principle way of analysing process data by clustering methods. Modern process control systems collect the data and archive them. Based on this archived data set, the classifier is designed with the help of clustering. For this task the support of experts of the plants is also required. Each feature leads to one dimension of the feature space. Clustering algorithms find accumulations of objects in that space. These accumulations are the different classes. A new object can now be classified. Therefore it is important that the so found classes can be interpreted by the practitioner. He may recognize that one class contains good process states, the other class bad ones. So the information, which is hidden in a big data set, can be compressed by finding classes and designing a classifier. With fuzzy classification, processes can be studied, which continuously move from one state to another. One of these processes is the above described coking of cracking furnaces. After the production period the furnace is shut down and the coke is burned out with a mixture of steam and air.
Current Process Process
IIIIII
IIIIII
1 1
Features: F1, F2, ...
T Feature Selection
Classifier Design
Expert Fig. 5. Analysing process data.
Classification
Process Analysis
W. Meier et al. / Fuzzy data analysis - Methods and industrial applications
25
Cracking-Furnace
~
exhaust gasj~ i
/[ exhaust gas ~ control
~emperatur~.~-! i
zone 1
i
m
zone 2 temperature
/
L
Fig. 6. Cracking furnace. After that the furnace is reintegrated into the production process until it has to be decoked again. The state of the furnace is described by several features. Figure 6 shows a brief sketch of the furnace and the measured features. For the determination of coking of a furnace it is not necessary to find classes by clustering. Two different classes describing the coked and decoked states are already known. The center of these classes in the multidimensional feature space are also known, so that the classifier can be built from the history of the process data. After a decoking process the values of the features for this state can be acquired. A short time before a decoking process the values of the coked state are obtained analogously. This
classifier can be used to classify the current furnace state and to support the operator's decision, whether the furnace is coked or not. Results and discussion Cluster-methods mentioned in Chapter 3.1 were used to determine the coking of 10 cracking furnaces of a thermal cracker [11]. The data of one year have been analyzed. The process of coking lasts about 60 days. Therefore only mean values of a day of the measured quantities were considered. Each object (furnace) is described by features sketched in Figure 6. For different furnaces the centers of coked and decoked classes were found by searching for coked and decoked states in the data set.
26
W. Meier et al. / F u z z y data analysis - Methods and industrial applications
temperature In °C
membership
1200
u(i) 1.0
1000 0.8-
800 -
coked 0,6-
600
/
400 -
0.4-
J
J
transition - s t a t e
200 0.2
o 60
120
180
240
300
360
days
0.0
i
40
50
60
70
80
90
lOO
days
Fig. 7. Furnace temperature.
Fig. 9. Transition of process states.
Figure 7 shows the temperature profile of a furnace during the whole year. Characteristic peaks, where temperature decreases significantly, result from decoking processes. K1 and K2 describe decoked and coked states of the furnace. The temperature profile shows no characteristic shape, which results from coking. Furnace temperature is only one of the features sketched in Figure 6. There are dependencies between features, so that a determination of coking is not possible considering only the feature 'temperature'. The whole feature set shown in Figure 6 is suitable to find coked and decoked classes and to build a classifier, which can be used to classify current furnace states. In Figure 8 the process of classification is sketched schematically. During a production period the furnace state starts at the decoked class and runs continuously towards the coked class.
Figure 9 shows the membership values of a furnace state during a production period using the classifier. The values describe the membership of the current furnace state to the coked class. The membership values increase continuously and reach nearly 1 at the end of the production period. By using the classifier the information, which is hidden in the data set, is compressed. The membership value sketched in Figure 9 shows the degree of coking and hence the feature which describes coking of cracking furnaces. The classifier works on-line and classifies the current furnace state concerning the coking problem. The operator can use this information to check how long the regarded furnace will be able to run until it has to be decoked. Now it is easier to make arrangements concerning logistical questions like ordering right amounts of raw material or not to be understaffed at certain times.
4.2. Acoustic quality control K1 arbitrary units
............... arbitrary units
L ~
Fig. 8. Fuzzy classification of a continuous process.
In acoustic quality control many efforts have been undertaken to automatize the respective control tasks which are usually performed by humans. Even if there are many computerized systems for automatic quality control by analysis of acoustic signals, some of the problems could not yet be solved adequately. Here an example of acoustic control of ceramic goods is presented to show the potentials of fuzzy data analysis in this respect.
27
W. Meier et al. / F u z z y data analysis - Methods and industrial applications
A/DConverter Data Acquisition Board
DataEngine
Fig. 10. Automized quality control of tiles.
Problem formulation In cooperation with a producer of tiles a prototype has been built which shows the potentials of automatic quality control. So far an employee of this company has to check the quality of the final product by hitting it with a hammer and deciding about the quality of the tile based on the resulting sound. Since cracks in the tile cause an unusual sound an experienced worker can distinguish between good and bad tiles. Solution process In this application algorithmic methods for classifier design and classification were used to detect cracks in tiles. In the experiments the tiles are hit automatically and the resulting sound is recorded via a microphone and an A/Dconverter. (See Figure 10.) Then signal processing methods like filtering and Fast-Fourier-Transformation (FFT) transform these sound data into a spectrum which can
be analyzed. For example, the time signal is transformed by an FFT into the frequency spectrum. From this frequency spectrum several characteristic features are extracted which could be used to distinguish between good and bad tiles. The feature values are the sum of amplitude values in some specified frequency intervals. In the experiments a 6-dimensional feature vector showed best results. After this feature extraction the fuzzy c-means algorithm found fuzzy classes which could be interpreted as good and bad tiles. Since a strict distinction between these two classes is not always possible fuzzy cluster techniques have the advantage that they do not only distinguish bad from good tiles but that intermediate qualities can also be defined (Figure 11). Based on this prototype an automatic system for acoustic quality control can be installed at the production lines. In the future this prototype will be enlarged to support also the optical quality control by methods of computer vision
FourierTransformation MDI Converter ] Intensity [ Data 1===~ IAcquisitionl [Board [
A
[m v ]
pI
Feature Extraction " ~
tud
[ ClusteringI F¢
o Time Ims--J
~meal
Frequency
[kHz]
Frequemeyspectrum
~'~ Feature space
DataEngine Fig. 11. Application of DataEngine for acoustic quality control.
F1 Clustering
28
W. Meier et al. / Fuzzy data analysis - Methods and industrial applications
[7]. Especially if the overall quality of tiles has to be evaluated fuzzy technology offers methods to aggregate different evaluations as for example acoustical and optical ones. A lot of research has been done in this respect in the past [19].
5. Conclusion Data analysis has large potentials for industrial applications. As shown in this article it can lead to the automation of tasks which are too complex or too ill-defined to be solved satisfactorily with conventional techniques. This can result in the reduction of cost, time, and energy which also improves environmental criteria. In contrast to fuzzy controllers where the behaviour of the controlled system can be observed and therefore the performance of the controller can be stated immediately, many applications of methods for data analysis have in common that it will take some time to exactly quantify their influences. In the near future many beneficial applications of data analysis are expected and their advantages will be reported.
6. References [1] H. Bandemer and W. Nather, Fuzzy Data Analysis (Kluwer, Dordrecht, 1992). [2] J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum Press, New York, 1981). [3] J.C. Bezdek, E.C.-K. Tsao and N.R. Pal, Fuzzy Kohonen clustering networks, in: IEEE Int. Conf. on Fuzzy Systems (San Diego, 1992) 1035-1043.
[4] J.C. Bezdek and S.K. Pal (Eds.), Fuzzy Models for Pattern Recognition (IEEE Press, New York, 1992). [5] A. Kandel, Fuzzy Techniques in Pattern Recognition (John Wiley and Sons, New York, 1982). [6] B. Kosko, Neural Networks and Fuzzy Systems (Prentice-Hall, Englewood Cliffs, 1992). [7] R. Krishnapuram and J. Lee, Fuzzy-set-based hierarchical networks for information fusion in computer vision, Neural Networks 5 (1992) 335-350. [8] P.A. Paardekooper, C. van Leeuwen, H. Koppelaar and A.G. Montfoort, Simulatie van een ethyleenfabriek bespaart tijd en moeite, PT Polytechnische tijdschrift, Simulatie (1990) 30-34 (in Dutch). [9] Y.-H. Pao, Adaptive Pattern Recognition and Neural Networks (Addison-Wesley, Reading, MA., 1989). [10] R. Schalkoff, Pattern Recognition Statistical, Structural and Neural Approaches (John Wiley and Sons, New York, 1992). [11] B. Trompeta and W. Meier, Erfahrungen bei der Prozel3identifikation von verfahrenstechnischen GroBanlagen, 2. Anwendersymposium zu Fuzzy Technologien 23.-24. March 1993, Aachen (in German). [12] Ullmanns Encyclopedia of Technical Chemistry, Vol. 8, 4th Edition (New York, 1982). [13] J. Watada, Methods for fuzzy classification, Japanese Journal of Fuzzy Theory and Systems 4 (1992) 149-163. [14] R. Weber, Fuzzy-ID3: A class of methods for automatic knowledge acquisition, Proc. of the 2nd Int. Conf. on Fuzzy Logic and Neural Networks (lizuka, Japan, July 1992) 265-268. [15] S.M. Weiss and C.A. Kulikowski, Computer Systems that Learn (Morgan Kaufmann, San Mateo, 1991). [16] H.-J. Zimmermann, Fuzzy sets in pattern recognition, in: P.A. Devijer and J. Kittler, Eds., Pattern Recognition Theory and Applications (Springer-Verlag, Berlin, 1987) 383-391. [17] H.-J. Zimmermann, Fuzzy Sets, Decision Making, and Expert Systems (Kluwer, Boston, 1987). [18] H.-J. Zimmermann, Fuzzy Set Theory - And Its Applications, 2nd rev. ed. (Kluwer, Boston, 1991). [19] H.-J. Zimmermann and P. Zysno, Latent connectives in human decision making, Fuzzy Sets and Systems 4 (1980) 37-51.