=Paper=
{{Paper
|id=Vol-2565/paper12
|storemode=property
|title=Management of the Technical System Operation Based on Forecasting its “Aging”
|pdfUrl=https://ceur-ws.org/Vol-2565/paper12.pdf
|volume=Vol-2565
|authors=Anatoliy Shakhov,Varvara Piterska,Olha Sherstiuk,Olena Rossomakha,Antonii Rzheuskyi
|dblpUrl=https://dblp.org/rec/conf/itpm/ShakhovPSRR20
}}
==Management of the Technical System Operation Based on Forecasting its “Aging”==
https://ceur-ws.org/Vol-2565/paper12.pdf
Management of the Technical System Operation Based
on Forecasting its “Aging”
Anatoliy Shakhov1[0000-0003-0142-7594], Varvara Piterska1[0000-0001-5849-9033],
Olha Sherstiuk1[0000-0002-0482-2656], Olena Rossomakha1[0000-0002-4425-2192],
Antonii Rzheuskyi2[0000-0001-8711-4163]
1Odessa National Maritime University, Odessa, Ukraine
varuwa@ukr.net
2Lviv Polytechnic National University, Lviv, Ukraine
antonii.v.rzheuskyi@lpnu.ua
Abstract. The subject of the research is the models and mechanisms of
operation of technical systems. The purpose of the paper is to develop a model
for managing the technical system operation based on the forecasting its
“aging”. The following tasks are solved in the article: determination of the
failure modes and effects of technical systems; building a model for managing
the technical system in order to forecast the development of malfunctions and
the appearance of failures of its elements; development of a system for
managing the risk of failures of the technical system elements based on the
method of Failure Modes Effects Criticality Analysis; determination of the
technical system bifurcation points. The following results were obtained: on the
basis of graph theory, a conceptual model for managing the technical system
was developed, which serves as a basis for forecasting its operation; the matrix
of failure of the technical system elements is built on the basis of the ranking
system for evaluating parameters and determining the severity of the failure
effects for the technical system elements; a risk management system for the
failure of the technical system elements was developed based on the method of
Failure Modes Effects Criticality Analysis. The points of bifurcation of the
technical system were determined, that is, the points in time when it is
necessary to analyze the technical system progress and decide on the
advisability of stopping the system and conducting maintenance and repair of
its elements.
Keywords: Technical System, Forecasting, Risk Management.
1 Introduction
Improving the efficiency of using technical systems at the present stage is impossible
without constant monitoring of their condition and forecasting the development of
malfunctions during operation.
The full cycle of monitoring the state of technical systems includes the following
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0)
2020 ITPM Workshop.
steps: detecting deviations in the technical system; identification of malfunctions and
their causes; forecasting the development of malfunctions; adoption of
recommendations for corrective actions; analysis of the state after stopping the
technical system. Obviously, forecasting the state of technical systems is an important
action, the results of which will later help diagnose the operability of the technical
system and identify the risks of its failure. To forecast the development of the
malfunction, knowledge of the possible failure modes for machines of this mode and
thorough understanding of the relationships between the operating states of technical
systems and failure modes are required. The forecasting should include the
application of analytical models of origin and development of damage in the technical
system [1]. Failure Modes Effects Criticality Analysis (FMEСA) is a systematic
analysis method to identify possible failures, their causes and effects, as well as the
impact on the functioning of a system (system as a whole or its components and
processes) [2]. Failures in the technical system are determined by controlled
parameters. The general forecasting procedure includes the following main points:
determining the end point (usually the stopping point of the technical system);
observation of parameter changes and assessment of the speed of the technical system
development; determination of the current technical condition; obtaining an estimate
of the time to failure or the technical system residual resource; setting the horizon for
forecasting events.
2 Analysis of Literature Data and Resolving the Problem
In the development, design and creation of complex technical systems, knowledge of
the quantitative and qualitative laws inherent in the objects under consideration is
required. In connection with the large expenses of finance and time that are necessary
to establish these regularities experimentally, the use of mathematical modeling
methods to study the system properties is becoming increasingly important [3, 4].
The form and structure of the presentation of the technical system depend on the
nature of the processes occurring in it and external factors, on the nature of the
quantitative relationships between parameters and characteristics, as well as on which
aspects of the process and factors are put forward in relation to a specific goal, means
and methods of research [5-7]. To control the operation of technical systems on the
basis of forecasting their “aging”, the studied elements are distinguished as
characteristic generating the technical system couplings. External technological,
technical and economic relations are replaced by their generalized description or
quantitative characteristics [8-10]. The forecast is an estimate of the time to failure
and the probability of a single or multiple failure due to malfunctions (damage) that
occur at the moment or are expected in the future [11, 12]. The effectiveness of the
forecast depends on how well the modes of malfunctions and failures for a technical
system of this mode are known and described by the model, how they depend on the
service life and project of a particular technical system, how they develop over time
[13, 14]. The forecast is based on experience-proven knowledge about the
development processes of various modes of malfunctions [15, 16]. The forecast
makes it possible to evaluate the technical system residual life with a sufficient degree
of certainty to make a decision that helps prevent a possible failure, take corrective
actions to extend the technical system life, or use the available time reserve to prepare
for an impending failure [17].
Failure Modes Effects Criticality Analysis – FMEСA is applicable at various levels
of the technical system decomposition, that is, from the system as a whole to the
functions of individual components or software commands [2]. The definition of
criticality implies the use of a qualitative measure of the failure modes effects [18,
19]. Therefore, it can be stated that FMECA is a method that allows identifying the
severity of the potential failures modes effects and provide measures to reduce risk
and assess the probability of the technical system failure modes [20].
3 The Purpose and Objectives of the Research
The purpose of this article is to increase the efficiency of the use of technical systems
by improving control procedures and predicting their condition during operation.
The objectives of this research: to determine the failure modes and effects of
technical systems; to build a model for managing the technical system in order to
forecast the development of malfunctions and the appearance of failures of its
elements; to develop a system for identifying, analyzing and managing the risks of
failures of the technical system elements based on the method of Failure Modes
Effects Criticality Analysis FMECA; to determine the bifurcation points of the
technical system.
4 Materials and Methods of the Research
The technical system is considered as a single complex of heterogeneous elements,
designed to generate energy through the simultaneous continuous implementation of
various interconnected processes of a real cycle. Any change in a parameter or
element of the technical system to one degree or another affects the parameters,
characteristics and indicators of the whole complex. This influence for each
individual k-th element is transmitted through the combination of its boundary (input
and output) parameters Zk, which determine the direction and nature of the processes
in the technical system elements and at the same time play a connecting role between
them. The set of values of such coupling parameters completely determines the
technical system operational state as a whole and its individual elements. The
technical system is modeled as a uniform system of elements with connections
between them and complexes of external objects. Each element of the technical
system is designed to carry out a directed technological process or its separate stage
according to certain laws of its stationary course.
It is legitimate to accept that all processes in the technical system elements with
one or another permissible stationary load occur continuously. Such processes are
connected by stationary flows in accordance with a given coupling circuit. The term
"flow" is used hereinafter for various heat transfer mediums and working fluids,
through which processes are carried out in the technical system elements and coupling
(fuel, air, combustion products, feed water, condensate, steam, plasma, etc.). It is
assumed that coupling on the transmission of mechanical and electrical energy is also
carried out by appropriate flows. Each stationary coupling has a strictly defined
direction corresponding to the actual direction of flow between the technical system
elements. Imagine a conceptual model for managing a technical system (Fig. 1).
In recent decades, graph theory has been widely and successfully used to analyze a
number of complex systems [14, 15]. A graph is a collection of segments of arbitrary
length and shape, called arcs, and intersection points of arcs, called vertices. The main
topological information contained in the graph is a graphical expression of the
relationships between the vertices of the graph. The positive aspects of this method
can be used to solve this problem, since the technological scheme of any real
technical system is equivalent in its topological structure to a certain graph.
Presentation of the technical system diagram in the form of a graph will make it
possible to carry out mathematically rigorous and at the same time sufficiently clear
examination of it.
Fig. 1. Conceptual model of the technical system management
A system of elements and relationships modeling a technical system can be
represented in the form of a graph in which each element of the technical system
corresponds to a vertex of the graph, and the connection between the technical system
elements or to external objects is the arc of the graph.
Some adjacent peaks can be connected not by one, but by several opposite or
equally directed arcs. This case reflects the presence of several connections made
using different flows. Any scheme can be specified in the form of a matrix of
connections of the vertices of the graph, supplemented by a matrix of types of
connections along the flows of the technical system.
The equations for the entire technical system and its external relations, related to
the same period of time, have the following form:
- energy balance equation for each k-th element of the technical system:
K
Ek = 0 k = 1,...,K ; (1)
k =1
- the equation of the balance of costs for each l-th flow of the k-th element of the
technical system:
L
Gl = 0, l = 1,...,L, (2)
l =1
where G – flow discharge;
- the equation of the hydraulic (aerodynamic) balance for each l-th flow of the k-th
element of the technical system:
(p p − p ) = 0,
' ''
kl (3)
where p – flow parameter for output (') or input ('') coupling of the technical system
element; ∆р – characteristics of the change in the parameter of the processes in
technical system elements.
Between the parameters and technological characteristics of the individual
technical system elements there are complex dependencies of various kinds. The
establishment of these dependencies is the task of the joint thermal, hydraulic,
aerodynamic and strength calculation of elements. The main characteristics for the
technical system are:
- characteristics of the change in the parameters of each l-th flow in each k-th
element of the technical system:
(
pkl = pkl Z k , Z kK , ) (4)
where Zk – set of coupling parameters of the k-th element of the technical system; ZK
– project parameters of the technical system;
- characteristics of the average speed of the l-th flow of the technical system in
each k-th element of the technical system:
(
Wkl = Wl Z k , Z kK ; ) (5)
- characteristics of the highest wall temperature for each q-th structural part of each
k-th element of the technical system made of a material of the form m:
(
t qmk = t qm Z k , Z kK ; ) (6)
- characteristics of the absolute or relative wall thickness of each q-th structural
part of each k-th element of the technical system made of material m:
qmk = qm (Z k , Z kK ); (7)
- consumption characteristics of metals and other m-x materials for each q-th part
in each k-th element of the technical system:
(
Gqmk = Gqm Z k , Z kK . ) (8)
The influence of the remaining parameters of the technical system Z and Z K, not
related to this k-th element of the technical system, on the characteristics of this
element is manifested implicitly through their relationship with the parameters of this
element of the technical system. Expressions of characteristics take into account the
operating conditions of the technical system at both nominal and partial loads.
The thermodynamic, consumable, and structural parameters of the technical system
Z and ZK cannot take completely arbitrary values, but can only vary within the limits
of physically possible and technically feasible states of the technical system, as well
as within the limits of technically acceptable initial and operational states of materials
in equipment elements. The indicated limitations for various elements of equipment,
materials, and energy carriers can be reflected in the form of inequalities in the sets of
parameters:
` `
Z Z Z , ZK ZK ZK , (9)
where the indices “*” and “**” refer to the minimum and maximum accepted values
of the parameters.
For the given modes and materials of the technical system elements or their
structural parts, limiting conditions are imposed on the characteristics of the form (5)
– (7), reflecting the requirements of manufacturability and long-term reliable
operation of the technical system. These limiting conditions can be represented as
follows:
( )
Wkl Wl Z k , Z kK Wkl ; t qmk ( )
t qm Z k , Z kK t qmk ; qkm
( )
qm Z k , Z kK qmk
. (10)
During the technical system operation, parameters (1) – (8) change, while the
system "becomes old". The specified parameters are determined during the diagnosis.
Determination of the influencing factors (causes) of a technical system failure
implies finding the parameters on which the rate of development of the technical
system malfunction depends (Fig. 2).
Each influencing factor can be considered as one of the driving forces for the
development of an existing fault of a specific mode, but it also affects the
development of other faults and the emergence of the future technical system failures.
Fig.2. Influencing technical system failure: Т – time; р – controlled parameter; 1 – primary
failure; 2 – secondary failure; V – influencing factor; Т2 – estimation of time to the primary
failure; Т2 – estimation of time to the secondary failure; а – initiation of the secondary failure; b
– running time
The failure point for the monitored parameter is the value of the parameter, upon
reaching which the object fails. This point is usually determined based on the
experience of previous failure observations. The technical system stop level for the
same parameter, upon reaching which it is stopped, lies below the point of failure and
is used to indicate a failure and to carry out maintenance and repair. Since this level is
below the point of failure, its achievement does not yet indicate the technical system
complete failure, which makes it possible to avoid destructive damage.
The warning levels are set below the stop level of the technical system elements,
based on the reserve of time during which it will be possible to carry out maintenance
and repair of the system. The forecasting procedure requires knowledge of the
behavior of a set of controlled parameters during the development of a malfunction of
a given mode under given operating conditions.
To forecast future failures of a technical system, first of all, it is necessary to
determine the criteria for their occurrence through influencing factors, taking into
account that the same parameter can serve as an influencing factor for an upcoming
failure and be used as a sign of a malfunction leading to a future failure. In this case,
the main reason for the failure of this mode can be determined through a set of
parameters whose values directly or indirectly indicate the degree of the malfunction
development. The result of forecasting is the technical system element malfunction
probability within a given period of time. Currently, to reduce the probability of an
unfavorable result and minimize possible costs, a risk management methodology is
often used, which allows for reliable operation and stable development of the
researched system. In general, risk management is an economic concept that implies
an iterative process that helps organizations determine a strategy, achieve goals and
make informed decisions, contributing to the improvement of the management
system. However, the peculiarity of risk analysis is that it considers potentially
negative consequences that can also arise as a result of failures in technical systems or
failures in technological processes. The main element of risk analysis is hazard
identification (detection of possible violations) that can lead to negative
consequences. Risk assessment for a technical system includes frequency and impact
analysis. However, when the consequences are insignificant and the frequency is
extremely small, it is enough to evaluate one parameter. The risk for a technical
system is the product of the probability of a particular malfunction on the effects of a
malfunction in the technical system. Identification of the technical system risk failure
and, if necessary, maintenance and repair affect the performance of the system.
Actions should be aimed at identifying and mitigating the effects of failure risks using
risk management methods.
The risk of failure in a technical system can be determined as follows:
R = Pk Z k , (11)
where Рk – probability of failure of the technical system k-th element; Zk – the degree
of impact of the k-th element failure on the technical system, that is, an indicator of
the severity of the effects.
If Рk < 0.2, then:
Pk = 1 − e − H , i
(12)
where Hk –failure criticality level of the k-th element for a technical system
The priority value of the risk of failure in the technical system is determined taking
into account the level of failure detection of the k-th element Gk of the technical
system:
RPN = Z k U k Gk , (13)
where Uk – the probability of failure of the technical system k-th element for a given
period of time.
Determining the level of failure detection of the technical system k-th element is an
assessment of the chance to identify and eliminate the failure of the k-th element by
performing maintenance and repair before the effects for the technical system. The
values of G are ranked in reverse order with respect to the failure probability or the
failure severity. The higher the G value, the less probable failure detection. A lower
detection probability corresponds to a higher RPN and higher priority for the
technical system failure mode.
By analogy with the FMECA method, a risk matrix is constructed (Table 1).
Table 1. Matrix of the risk of technical system elements failure
Severity of effects
Failure probability Failure rate Catastrophic effect
Critical effect (2) Slight effect (1)
(3)
(A) – Improbable <10-6 Work Work Work
(B)– Implausible 10-4 – 10-6 Work & Support Work Work
(C) – Unlikely 10-2 – 10-4 Work & Support Work & Support Work
(D) – Random 10-1 – 10-2 Stop Work & Support Work & Support
(E) – Probable 1 – 10-1 Stop Stop Work & Support
(F) – Frequent >1 Stop Stop Stop
In order to determine the points of failure of the technical system elements to
prevent the occurrence of the failure risk, the ranking system for evaluating
parameters is introduced. The following classification of countermeasures (warning)
of situations of failure risk of the technical system elements is used: Stop – stopping
the technical system; Work & Support – continued operation of the technical system
with maintenance (repair, replacement) of system elements; Work – continued
operation of the technical system, if necessary, minor repairs. The severity of the
failure effects for the technical system elements is determined as follows: catastrophic
failure effects provide for the risk of termination of the primary functions of the
technical system elements and cause severe damage to the system, environment and
human resources; critical failure effects provide for the risk of termination of the
primary functions of the technical system elements and cause significant damage to
the system and the environment, but is not a serious threat to human resources; minor
failure effects provide for the risk of deterioration in the performance of the functions
of the technical system elements without noticeable damage or lack of damage to the
system or threat to human resources. The frequency or probability of each mode of
failure should be determined to assess the effects or criticality of the failures. The
criticality value is associated with the conditional failure rate and operating time and
can be used to obtain a more realistic risk assessment corresponding to a specific
failure mode during a given operating time of the technical system. The intensity of
the i-th technical system failure is estimated using the formula:
i = k i i , (14)
where ωk – failure rate of the technical system k-th element; λi – the ratio of the
number of the technical system i-th failure to the total number of failure modes; αi –
conditional probability of the technical system i-th failure effect.
The criticality value for the k-th element of the technical system having n failure
modes is determined by the formula:
n
H k = k i i Tk , (15)
i =1
where Tk – active time of the technical system k-th element.
The criticality value is a measure of the technical system failure risk.
Upon reaching or exceeding the values of the technical system monitored
parameters established as a criterion for the initiation of a malfunction, a warning
signal is triggered to begin the development of a malfunction of this mode. This step
serves as the basis for developing recommendations for the maintenance and repair of
the technical system defective element. Within the framework of FMECA during the
technical system operation, it is possible to determine the points of its bifurcation, that
is, time points when it is necessary to analyze the progress of the technical system and
decide on the feasibility of stopping the system and performing maintenance and
repair of its elements (Fig. 3).
Fig. 3. Technical system bifurcation points: R – risk level; Т – technical system operating time;
А, В, С, D, E, F, G, H, L – bifurcation points
The value of the initial failure risk of the technical system elements is taken as A.
The severity of the effects at the initial stage is Z0. At a certain point in time of the
passage of the AB stream during diagnosis, it was found that the risk level affects the
value of Z with the probability of failure P of the technical system element. In such a
situation, a decision is made to suspend the technical system operation and to carry
out maintenance and repairs at bifurcation point B. As can be seen from the graph, as
a result of the element maintenance and start-up, the risk of its failure at time T = 1
rises to point C. In this case, the severity of the technical system element failure
effects is Z1. After applying the risk management system based on the FMECA
method, the technical system continues to work with the transition from stage C to
stage D with the severity of effects Z2. At the same time, the risk of element failure
does not change, since timely repair and maintenance of the technical system element
was carried out. Continuing the technical system operation at the stages DE – EF –
FG, at some point in time there is a certain probability of the occurrence of a risk
situation of the technical system element failure. The technical system element
continues to function with an increased risk level at stage GH. That is, the failure risk
increases. Next, transition to stage HL takes place. At point L, the failure risk is
maximal for a given period of operation of the technical system. Points A, B, C, D, E,
F, G, H, L are bifurcation points, which can be called decision points on the need for
maintenance or repair of the technical system elements, taking into account the risk
situation. Moreover, for each bifurcation point at certain points in time, it is necessary
to build a separate risk matrix for the technical system element failure. With
increasing the technical system operating time, the risk of failure of its elements
increases. Also, due to the presence of many elements in the system, such matrices are
also constructed for each element separately. Combining the results of these matrices,
the single risk assessment system during the technical system operation is obtained.
5 Conclusions
Based on graph theory, a conceptual model for managing a technical system has been
developed, which serves as the basis for forecasting its operation.
The matrix of failure of the technical system elements is constructed on the basis
of the ranking system for evaluating the parameters and determining the severity of
the failure effects for the technical system elements; a risk management system for the
failure of the technical system elements was developed based on the method of
Failure Modes, Effects and Criticality Analysis. It is established that the result of
forecasting is the probability of the technical system element malfunction within a
given period of time. The construction of the conceptual model for managing a
technical system based on FMECA, taking into account the failure risks, will allow
identifying failures that, when they appear one at a time and in combination, have
unacceptable or significant effects, and determine the failure risks that can have
serious effects for the expected or required function of the technical system. The use
of FMECA also eliminates costly modifications due to early identification of
deficiencies in the technical system modules. The determination of the technical
system bifurcation points will make it possible to determine the time points for the
occurrence of the failure risks of the technical system elements and to make a
decision about stopping the system and conducting maintenance and repair of the
technical system elements depending on the severity of the failure effects.
References
1. ISO 13381-1:2015 Condition monitoring and diagnostics of machines – Prognostics –
Part 1: General guidelines (IDT) (2015).
2. IEC 60812:2006 Analysis techniques for system reliability – Procedure for failure mode
and effects analysis (FMEA) (MOD) (2006).
3. Shakhov, A.V.: Designing the life cycle of maintainable technical systems. ONMU, pp.
112–153 (2005).
4. Bushuev, S.D., Bushueva, N.S.: Creation and development of competitive project-
oriented high-tech enterprises. Nikolaev, Torubary E.S., pp. 125–187 (2011).
5. Bushuev, S., Bushueva, N., Jaroshenko, R.: Garmonization models property development
programme in turbulence environment. Management of Development of Complex
Systems 10, 9–13 (2012).
6. Barzilovich, E.: Maintenance models for complex systems. pp 131–152 (1985).
7. Bushuyeva, N.: Models and methods of proactive management of organizational
development programs, Кiev, Naukovii svit, 270 p. (2007).
8. Bushuyev, S., Verenych, O.: Organizational maturity and project: Program and portfolio
success (Book Chapter), Developing Organizational Maturity for Effective Project
Management, pp. 2–24 (2018).
9. Piterska, V., Lohinov, O., Lohinova, L.: Portfolio method of scientific activity
management of higher education institutions, Innovative technologies and scientific
solutions for industries, 2 (8), 86–96 (2019). DOI: 10.30837/2522-9818.2019.8.086.
10. Kononenko, I.: Optimization of the project content according to the criteria of profit,
time, cost, quality, risks. Eastern–European Journal of Enterprise Technologies 1/10 (55),
13–15 (2012).
11. Bushuyev, S., Murzabekova, A., Murzabekova, S., Khusainova, M.: Develop
breakthrough competence of project managers based on entrepreneurship energy. In:
proceedings of the 12th International Scientific and Technical Conference on Computer
Sciences and Information Technologies, CSIT 2017, pp. 11–16 (2017). DOI:
10.1109/STC-CSIT.2017.8099420.
12. Bushuyev, S., Bushuev, D., Bushuyeva, N., Kozyr, B.: Information technologies for
project management competences development on the basis of global trend. Information
technology and learning tools 68 (6), 218–234 (2018). DOI:
https://doi.org/10.33407/itlt.v68i6.2684.
13. Bodnarchuk, I., Duda, O., Kharchenko, A., Kunanets, N., Matsiuk, O., Pasichnyk, V.:
Multicriteria choice of software architecture using dynamic correction of quality
attributes. Advances in Intelligent Systems and Computing ІІ (AISC) 938, 419–427
(2019).
14. Kononenko, I.V., Kharazii, A.V.: Solving a task of the project management methodology
selection based on the project scope optimization. Eastern–European Journal of
Enterprise Technologies 4/3 (76), 43–52 (2015).
15. Chumachenko, I., Docenko, N., Kosenko, N., Sabadosh, L.: Formation of an adaptive
project team. Management of the development of complex systems (38), 67–71 (2011).
16. Kononenko, Y.V., Bukreeva, K.S., Sukach, S.A.: Study of the stability of the solution of
the problem by optimizing the enterprise’s project portfolio for the planning period.
Bulletin of the Kharkov Polytechnic Institute. 32, 3–7 (2011).
17. Shakhov, A., Piterska, V.: The development of the risk management mechanism for
innovation project. EUREKA: Physics and Engineering, Company "Scientific Route" 3,
12–20 (2018).
18. Piterska, V., Kolesnikov, O., Lukianov, D., Kolesnikova, K., Gogunskii, V., Olekh, T.,
Shakhov, A., Rudenko, S.: Development of the Markovian model for the life cycle of a
project’s benefits. Eastern–European Journal of Enterprise Technologies 5/4 (95), 30–39
(2018).
19. Kosenko, V., Persiyanova, E., Belotskyy, O. Malyeyeva, O.: Methods of managing traffic
distribution in information and communication networks of critical infrastructure
systems. Innovative technologies and scientific solutions for industries 2 (2), 48–55
(2017). DOI: https://doi.org/10.30837/2522-9818.2017.2.048.
20. Kononenko, I.: Model and method for optimizing enterprise project portfolios for the
planning period. Eastern–European Journal of Enterprise Technologies 1/2 (43), 9–11
(2010).