DIAGNOSTIC INFORMATION RENEWAL BASIS OF
COMPLEX TECHNICAL OBJECTS
INFORMACJA DIAGNOSTYCZNA PODSTAWĄ
ODNAWIANIA ZŁOŻONYCH OBIEKTÓW
TECHNICZNYCH
Stanisław Duer
Technical University of Koszalin, e-mail: stanislaw.duer@tu.koszalin.pl
Abstract: This paper presents a method to creation of a servicing information in
the expert system including an artificial neural network. The article presents the
organization and structure of the expert systems witch support renovate of technical objects. Presented expert system uses information from an artificial neural network. The article presents of the basis for building servicing expert systems based on rule-based structure. Presented and described in diagram of the structure developed expert system. Expert discusses the fundamentals of building a knowledge base using rules way it works.
Keywords: intelligent system, servicing process, system modelling, expert system,
artificial neural networks, knowledge base, diagnostics information
Streszczenie: W artykule przedstawiono metodę tworzenia informacji obsługowej
w systemie ekspertowym, który zawiera sztuczną sieć neuronową. Artykuł przedstawia także organizacje i strukturę systemu ekspertowego, który wspomaga obsługiwanie obiektów technicznych. Prezentowany system ekspertowy wykorzystuje informację ze sztucznej sieci neuronowej. W artykule przedstawiono podstawy budowy systemów ekspertowych w oparciu o zasady regułowe. Przedstawiono w nim schemat struktury obsługowego systemu ekspertowego. Artykuł omawia podstawy tworzenie bazy wiedzy za pomocą reguł obsługiwania oraz sposób jej tworzenia.
Słowa kluczowe: inteligentny system, proces obsługiwania, modelowanie
systemów, system ekspertowy, sztuczne sieci neuronowe, bazy wiedzy, informacja diagnostyczna
1. Introduction
The technical state of the object in a given time of the use determines the possibilities of the realization of its required functions. It is determined by a subset of its physical properties [1-5, 7, 8], which describe a given object. For practical reasons, to the states of the object in the diagnosing process, numerical values are assigned, which depend from the logics of the classification of the states applied. For divalent logics, these are states from set {1, 0}, where: “1” is the operational state, and “0” is the non-operational state. For the trivalent assessment of the classification of states [1, 3-4], to the states of the object, states marked with the values from set {2, 1, 0}, were assigned, where: “2” – the state of full operation; “1” – the state of incomplete usability; “0” – the state of non-operation (defect). The problem of the description of a technical object: “an incomplete usability state”, which is presented among other things in the author’s papers [1-4]: technical diagnostics presents the basis and organization of diagnostic inference in technical objects as the final element of diagnosing. The effect of diagnostic inferring is the determined (recognized) states of the object’s functional elements, on the basis of which the object’s resultant stage is determined. Diagnosing of a technical object can also be performed in divalent logic {1, 0} or trivalent logic {2, 1, 0}. The basis for diagnosing of technical objects is constituted by possible changes of the values of output diagnostic signals (mainly in the analogue form, but also in other forms) from the object’s functional elements. Divalent logic constitutes the basis for the application of the trivalent logic of the evaluation of the object’s states. Changes of the values of diagnostic signals are only in the range of their permissible and boundary changes. The range of these changes for a given object is constant regardless of the type of the valence used for the determination of the object’s states. Additionally, for trivalent logic, the range of changes was divided-determined: state {1}, state of incomplete usability.
2. Information from the artificial neural network to determine the basis
of information maintenance of a technical object
The ANN network developed is presented in Fig. 1. It consists of three layers: F1 – the input layer, F2 – the output layer and the intermediate layer. The input cells of layer F1 process the initial diagnostic information according to the algorithm of the DIAG programme.
The whole of the issue of information processing by ANN neurons (Fig. 1) [1, 2, 5, 6, 10] takes place in D-dimension diagnostic space (ω) determined by the elementary signal vectors (Xi). The input signal in the form of
Tn
i
x
x
x
X
1,
2,...,
is being passed to all neurons of the ANN’s input layer. The input cells memorize the vectors of signal standards {Xi}. Basing upon that, the neurons from the input layer determine the measures of similarity between the input signal vector and its standard, and the length of the input signal {Xi} to all vectors of weightsw
i,j
w
1,
w
2,...,
w
n
T, where i =1,…,N. In the ANNnetwork presented in Fig. 1, the neuron (i) placed in layer F1 is connected to neuron (j) placed in layer F2, where: j = 1,2,…N.
Neuron (i) sends the signal of value (xi) with the connecting strength (wi,j) of the activation function. Following the literature of the subject [1, 2, 5, 6, 10], the Minkowski’s measure is used for the analysis of the measures of signal vectors. The Minkowski’s measure can be expressed by the following relation (1):
/ 1 1,
,
N i i w i i w i MX
X
X
X
D
(1)where: DM - the standard deviation of the signal measure vector.
D D D D x1 x2 xn
n i i ix y 1 1 1 w11 w12 w1n w21 w22 w2n wj1 wj2 wjn wN1 wN2 wNn
n i i ix y 1 2 2
n i i ji j x y 1
n i i Ni n x y 1 i j i K i l f X y 1 , Fig. 1. Diagram of an artificial neural networks
For the ANN presented in Fig. 1, neuron (i) is connected with neuron (j), so it transmits a signal of value (Xi) with weight coefficient (wi,j) and the activation function, represented by the relation:
i K i j i lx
w
w
X
f
1 ,,
(2)The value of its output function is s derived from the relation (3)
i j i K i l
f
X
y
1 ,
(3)On the final stage of the work of a neural network, a classification process of the object’s states is realized according to the algorithm. For this purpose, to the values of the output function as determined by the network, proper classes of the object’s states [1-5] were assigned according to the classification diagram [1, 2]. The results of the object’s diagnosis are presented in Table 1.
Table 1. Table of object’s states
State of State of Vector of states of elementary components {ei,j} object module (e1,1) ... (ei,j) ... (ei,J) W((O))
W((E1)) W((e1,1)) ... W((e1,j)) ... W((e1,J))
... ...
W((Ei)) W((ei,1)) ... W((ei,j)) ...
... ...
W((EI)) W((eI,1)) ... W((eI,j)) ... W((eI,J)) where: W((ei,j)) – value of state assessment logics for jth element within ith module (from the set of the accepted three-value logic of states’ assessment) - {2, 1, 0}), - symbol complementing the size of table.
A particularly important element of the maintenance system is the knowledge base (Fig. 2). It can be defined as specialized set of the object’s maintenance information which is determined by the following: the maintenance structure of the object {Wz(ei,j)}, the set of rules for maintenance (repairing) {Rr} and the set of preventive activities {A(ei,j)} [3, 9].
i j
i j
i j
p i j r i j
E
e
f
e
W
z
e
A
e
R
e
M
,
,;
,;
,;
,(4)
where: {Wz(ei,j)} - the maintenance structure of the object, {Rr(ei,j)} - the set of rules for maintenance (repairing), {Ap(ei,j)} - the set of preventive activities, {ME(ei,j)} - the maintenance system produces a set of maintenance information, {M(ei,j)} – the set of elements of internal structure of the object, f - the function which renovates the object in the servicing system.
A set of the object’s servicing information which constitutes the basis in the process of designing of the structure of the servicing system (Fig. 2) is presented in the form of the following dependence:
ij
ij ij p ij r ij
E
e
M
e
W
z
e
A
e
R
e
The set of rules for repairing {Rr(ei,j)} Servicing infotmation of the object {ME(ei,j)} Input information of the object Diagnostics intelligent system with ANN
The set of preventive activities {A(ei,j)} Servicing of the object Maintenance information set {ME(ei,j)} Output information of the object
The structure of adjustment system of the object
(Expert system)
{ME(ei,j)} X(ei,j) X(w)(ei,j)
The Expert system
Fc max Fc min
The object
Fig. 2. the structure of the expert system which renovates of the object in the servicing system.
where: X(ei,j) – diagnostic signal in jth element of ith set; X(w)(ei,j) – model signal for X(ei,j)
signal; FC – function of the use of the object, {Wz(ei,j)} – the maintenance structure of the
object, {Rr(ei,j)} – the set of rules for maintenance (repairing), {Ap(ei,j)} – the set of
preventive activities, {ME(ei,j)} – the maintenance system produces a set of maintenance
information, {M(ei,j)} – the set of elements of internal structure of the object.
The studies by the author [1-5] contribute to the development concerning a practical application of intelligent systems to support the operation of technical objects. The theoretical bases in the scope of the construction of intelligent operating systems were presented in these studies. A particularly important element was drawing up of theoretical bases in the scope of the creation of an operating base of expert knowledge including the following in particular:
- bases to determine the structure of an operating object through a transformation of the internal structure of the object examined,
- development of the rules of classification (grouping) in sth classes of the elements of the operating structure of an object,
- rules to determine the subsets of preventive activities that are adequate for sth classes of the elements of the operating structure of object,
- development of a set of rules of the subordination of the subset of preventive activities for the elements of the operating structure of an object,
- development of a set of operating rules on the grounds of which the process concerning the preventive activities of an object is realized,
- a final comparison of the set of operating information,
- a presentation of the structure of the set of the operating information of an object.
3. Preparation of the set of the elements of the object’s maintenance
structure
The diagnostic information obtained during diagnosing in the form of the knowledge base {W((ei,j))} constitutes the input information in the process of obtaining of the expert knowledge base which assists the maintenance of the technical object tested. The test of the state of the object with the use of DIAG programme is conducted according to the algorithm. On the basis of the measurements made of the properties of the distinguished diagnostic signals, tests and an analysis by DIAG programme with an artificial neural network (ANN), which belongs to the group of self-organizing networks, were made of them [1-5]. The results of the object’s diagnosis obtained is presented in Table 2.
Table 2. Table of object’s states
State of State of Vector of states of elementary components {ei,j} object Module (e1,1) ... (ei,j) ... (ei,J) W((O))
W((E1)) W((e1,1)) ... W((e1,j)) ... W((e1,J))
... ...
W((Ei)) W((ei,1)) ... W((ei,j)) ...
... ...
W((EI)) W((eI,1)) ... W((eI,j)) ... W((eI,J))
where: W((ei,j)) – value of state assessment logics for jth element within ith module
(from the set of the accepted three-value logic of states’ assessment-{2, 1, 0}) of the
object, - symbol complementing the size of table.
The methods for the minimization of the set of checks presented in [1, 2, 7] impose specific requirements for the set of elementary components. The elementary component can have in this case any number of signal inputs but only one output. In the determined set of the object’s functional elements, only those elements will be used during the maintenance whose states required repair. For this purpose, a relation for the comparison of the states of the object’s elements included in (Table 4) with their standards was developed in accordance with the following:
ij e w
i j e
ij eij EiW
e
ij EiW
e
ij EiW
z
e
, , , , , ,
(6)where: W((ei,j)) – the value of state assessment logics for j th
element within ith
module of the object, Ww((ei,j)) – the standard value of state assessment logics for
for jth element within ith module of the object, W(z(ei,j)) – the resulting value of the
state assessment logics for for jth element within ith module of the object,
-comparison relation,
- resulting relation.4. Classification of the elements of the internal structure of a technical
object
Control of the quantity of the qualitative usability function (FC) [1-3] in the operation process requires, among other things, recognition and description of an object’s internal structure, the nature of its work, etc. In modern systems for the servicing of technical objects, with a computer aided organization of this process, an important role is played in them by specialist (expert) databases [1-5, 9]. This specialist set of information concerning the object of servicing is determined on the basis of a description of the elements of the object’s servicing structure, grouping of them into classes, and assigning of a specific subset of preventative activities to them, which are characteristic only of a given class of the elements of the structure. From the determined set of classes of operational elements of the object’s maintenance structure on the basis of the following dependence:
ij
i j
i jk
e
if
s
e
I
to
VIII
then
for
s
e
S
,:
,
,(7)
where: Sk(ei,j) – k th
rule of the classes of operational elements of the object
maintenance,
- symbol of assignment, {s(ei,j) = (I to VIII)} – the set of classes ofthe elements of maintenance structure of the object.
5. Determination of the set of preventive activities to renovate of the
servicing object
An assumption is accepted in the paper that the maintenance structure of the object is determined by a set of maintenance elements (levels, and maintenance layers in these). For this reason, the determined set of preventive activities {Ap(ei,j)} possesses a structure which is compliant with the object’s maintenance structure (levels, and layers of preventive activities in these). The developed set of preventive activities to renovate the object presents the following dependence:
ij
i j
ij
l i jp
e
if
s
e
I
to
VIII
then
for
e
a
e
A
,:
,
,
, (8)where: Ap(ei,j) – p th
rule of the preventive activities,
- assignment relation,{al(ei,j)}- the subset of l th
activities from the set of preventive activities of elements
6. Determination of the set of rules of operation to renovate the
servicing object
From the determined a set of preventive activities (Table 3) their subsets were determined and assigned [1-5, 7] to further elements of the set of the object’s maintenance structure (Fig. 3) on the basis of the following dependence:
ij Z
ij
ij
l E
ijr
e
if
W
e
then
for
M
e
a
M
e
R
,:
, ,
, (9)where: Rr(ei,j) – rth rule of maintenance, → - symbol of assignment, {al}- the subset
of lth activities from the set of preventive activities, a set of maintenance
information {ME(ei,j)}.
The module "Rules" of programme "SERV"
The set of rules for classification of class of the elements of the object {Rs(ei,j)} The knowledge base for servicing of the object {ME(ei,j)} Internal structure of
the object {ei,j}
The set of rules for creation of
structure of preventive activities of the object {Rs(ei,j)}
The set of rules for creation of
structure element servicing of the object {Re(ei,j)}
Fig. 3. Diagram of the information structure of the “Rules” module in the intelligent system of operating of the object
Once this module has been started on the screen, various possibilities are available to present the set of information in the form of “Windows of Rules” in the following sequence:
1. {Re(ei,j)} – those that determine the structure of the object’s servicing elements
(a comparison of the state of the object’s elements);
2. {Rs(ei,j)} – those that determine the structure of the classes of the object’s servicing elements;
3. {Ra(ei,j)} – those that determine the structure of the preventive activities of the object’s servicing elements (depending of the classes of the object’s servicing elements);
4. {Ra(1)(ei,j)} – those that determine the structure of the preventive activities of the object’s servicing elements (for state {1} of the object’s servicing elements);
5. {Ra(0)(ei,j)} – those that determine the structure of the preventive activities of the object’s servicing elements (for state {0} of the object’s servicing elements);
{Rr(ei,j)} – those that determine the servicing structure of the object (setting up of a set of the object’s servicing information).
As a result, the maintenance system produces a set of maintenance information {ME(ei,j)} is presented in Table 3.
Table 3. Structure of the servicing information of the object
Object Servicing levels of object
Vector of the servicing information of the object [ME(ei,j)]
ME(e1,1) ... ME(ei,j) ... ME(ei,J)
O
1 ME(e1,1) ... ME(e1,j) ... ME(e1,J)
... ...
i ME(ei,1) ... ME(ei,j) ... ME(ei,J)
... ...
I ME(eI,1) ... ME(eI,j) ... ME(eI,J)
where: ME(ei,j) – servicing information of j
th
element in ith assembly.
6. Conclusions
On the grounds of the investigations carried out, it can be stated that an intelligent maintenance system completely decreases up to the minimum those costs which are connected with the regeneration of technical devices. An optimal and effective regeneration of technical objects is possible only with the use of intelligent systems which support the activity of the human (the user of an object). An optimal system of the maintenance of objects is such a system where the object is regenerated precisely in the time when this is required. An intelligent evaluation system of an object, which is constructed on the basis of an artificial neural network, guarantees such an approach. This system eliminates costs connected with the regeneration of unnecessary elements, i.e. those elements which do not require it and are in the state of fitness. The intelligent maintenance system designed of the object guarantees a high effectiveness of the maintenance approach, i.e. it ensures a regeneration of those internal (structural) elements which really require this, that is they are in the state of an incomplete fitness {1} or unfitness {0}.
7. References
[1] Duer S. 2009: Artificial Neural Network-based technique for operation process control of a technical object. Defence Science Journal, DESIDOC, Vol. 59, No. 3, pp. 305-313.
[2] Duer S., Duer R. 2010: Diagnostic system with an artificial neural network which determines a diagnostic information for the servicing of a reparable technical object. Neural Computing & Applications, Vol. 19, No. 5, pp. 755-766. [3] Duer S.: Expert knowledge base to support the maintenance of a radar system.
Defence Science Journal, 2010, Vol. 60, No. 5, pp. 531-540. http://publications.drdo.gov.in/ojs/index.php/dsj
[4] Duer S.: Modelling of the operation process of repairable technical objects with the use information from an artificial neural network. Expert Systems With Applications. 38 (2011), pp. 5867-5878. http://dx.doi.org/10.1016/j.eswa.2010.11.036. [5] Duer S.: Artificial neural network in the control process of object’s states
basis for organization of a servicing system of a technical objects. Neural Computing & Applications. 2011. DOI: 10.1007/s00521-011-0606-6
[6] Madan M. Gupta, Liang Jin and Noriyasu Homma 2003: Static and Dynamic Neural Networks, From Fundamentals to Advanced Theory. John Wiley & Sons, Inc, Hoboken, New Jersey, p. 718.
[7] Nakagawa T. 2005: Maintenance Theory of Reliability. Springer – Verlag London Limited, p. 264.
[8] Nakagawa T., Ito K. 2000: Optimal inspection policies for a storage system with degradation at periodic tests. Math. Comput. Model. Vol. 31., pp. 191-195. [9] Palkova Z., Okenka I.: Programovanie. Slovak University of Agriculture in
Nitra, 2007, p. 203.
[10] Zurada I. M. 1992: Introduction to Artificial Neural Systems. West Publishing Company, St. Paul, MN, p. 324.
Ppłk rez. dr inż. Stanisław Duer was born in Latyczyn/Zamość,
Poland. He obtained his Ph.D. (Technical Diagnostic) from the Department of Mechatronics of the Military University of Technology, Warsaw, Poland in 2003. Since 2003 he has been working at the Department of Mechanics of Technical University Koszalin as the academic teacher (adjunct). His areas of interest are: expert systems, control theory, modelling system and
diagnostic systems with an artificial neural network in diagnostics of a complex technical object.
