=Paper=
{{Paper
|id=Vol-3392/paper11
|storemode=property
|title=Intellectualization Method and Model of Complex Technical System's Failures Risk Estimation and Prediction
|pdfUrl=https://ceur-ws.org/Vol-3392/paper11.pdf
|volume=Vol-3392
|authors=Vladimir Vychuzhanin,Natalia,Alexey Vychuzhanin,Nickolay Rudnichenko
|dblpUrl=https://dblp.org/rec/conf/cmis/VychuzhaninSVR23
}}
==Intellectualization Method and Model of Complex Technical System's Failures Risk Estimation and Prediction==
https://ceur-ws.org/Vol-3392/paper11.pdf
Intellectualization Method and Model of Complex Technical
System’s Failures Risk Estimation and Prediction
Vladimir Vychuzhanin a, Natalia Shibaeva a, Alexey Vychuzhanin a, Nickolay Rudnichenko a
a
Odessa Polytechnic National University, Shevchenko Avenue 1, Odessa, 65001, Ukraine
Abstract
The article describes the developed intellectualization method and model of the complex
system failures risk technical condition assessment and prediction by diagnostic features. The
developed model has a relative insensitivity to incomplete data about the system and is
considered as a conceptual one for an intelligent system for assessing and predicting the
complex technical systems failures risk on network infrastructures. Developed method and
model can take into account the hierarchical levels of subsystems (components), intersystem
(interelement) links when searching for the failures causes. Proposed method and model
allow us to control the risk of failures in complex technical systems when information about
failures in their structures is received. In addition, the application of the method and model
makes it possible to predict trends in the risk of system failures, taking into account changes
in the risk of failures, in order to further choose a strategy for their recovery.
Keywords
Complex technical system, diagnostics, forecasting, model, intelligent system, Bayesian
belief network, performance, failure risk assessment.
1. Introduction
The diversity of the composition and the increase in the number of complex technical systems
(CTS), for example, installed on ships, is accompanied by an increase in the failure rate of such
systems. This leads to the need to repair CTS equipment, which means to ship downtime, so probably
losses associated with the time spent on repairs and the resources used might be increased
significantly. The use of intelligent systems for assessing and predicting the technical condition (TC)
of complex systems can significantly extend the life cycle of CTS, including ships [1]. Estimation,
prediction of the risk of CTS failures involves the development of methods and technologies of
artificial intelligence in order to identify and prevent possible failures in the operation of CTS [2].
This article is devoted to this problem solution.
2. Description of Problem
The operational reliability of recoverable CTSs is effectively achieved by the strategy of operating
systems with TC control based on technical diagnostic systems [3,4]. When designing, manufacturing
and operating CTS for various purposes, the following hierarchical structure of the CTS diagnostic
model is used system (consisting of subsystems); subsystem (functional devices consisting of
components); component (concentrated structure containing elements); intercomponent
communication or links (distributed structure containing links between components).
CMIS-2023: The Six International workshop on Computer Modeling and Intelligent Systems, May 12, 2022, Zaporizhzhia, Ukraine.
EMAIL: v.v.vychuzhanin@op.edu.ua (V.Vychuzhanin); nati.sh@gmail.com (N. Shibaeva); vychuzhanin.o.v@op.edu.ua (A. Vychuzhanin);
nickolay.rud@gmail.com (N.Rudnichenko)
ORCID: 0000-0002-6302-1832 (V.Vychuzhanin); 0000-0002-7869-9953 (N. Shibaeva); 0000-0001-8779-2503 (A. Vychuzhanin); 0000-
0002-7343-8076 (N.Rudnichenko)
© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
Subsystems and components are referred to as functional elements (FE). Intersystem and
intercomponent links form a group of functional inner-connections (FI) or links.
In design, manufacture and operation, the reliability of CTS is ensured by methods and means
specific to each stage of the systems "life cycle". Reliability of ship CTS is estimated based on the
results of FE and FI TC diagnosing and can be assessed in the failures risk form [5,6,7].
During CTS operation, prediction of their TC based on diagnostics helps to reduce the risk of
system failures [8]. At the same time, the CTS shipboard FE and FI failures risk assessment should
take into account their structure (hierarchy and topology of systems), functional states (operability,
failure) of their subsystems (components), intersystem (intercomponent) connections, as well as
systems data incompleteness.
Studies of ship CTS (energy, electric power, etc.) reliability models show us that the defeat of any
subsystems, components in systems generates a significant number of possible scenarios and options
for the development of such systems emergency states, as a result, it leads to possible marine
accidents.
Available statistics of marine accidents and incidents related to CTS failures are reflected in the
well-known databases Global Integrated Shipping Information System - Marine Casualties and
Incidents [9], Marine Accident Investigation Branch reports [10], Marine Accident Reporting Scheme
reports [11], National Transportation Safety Board NTSB [12], Casualty and Events [13]. Moreover,
according to statistics, one of the main CTS - ship power plant (SPP) accounts for 60-80% of all
failures of ship systems [14].
Improving the strategy for the operation and maintenance of ship equipment can be achieved by
solving the problems of choosing: a rational strategy for servicing CTS equipment; the level of
diagnosis and structural parameters characterizing its TC; diagnostic methods and diagnostic
parameters; algorithms for extracting diagnostic information; forecasting methods.
Currently, the volume of implementation of automation, digitalization and artificial intelligence
(AI) technologies in various industries continues to grow. Intellectualization of estimation and
prediction in complex systems failures risk is a task related to the development of methods and
technologies that allow automatically determining the risk (probability) of failures in CTS and
predicting their possible consequences.
Assessing and predicting the risk of CTS failures is a task that requires the ability to process large
amounts of data, analyze them and find patterns and relationships between various system operation
parameters. In this case, the use of intelligent technologies can greatly simplify and speed up the
process of data analysis.
In accordance with the requirements of the Register of Maritime Navigation, all modern ships
must be equipped with automation systems for technical means using digital technologies, as well as
AI technologies [15,16]. So, for example, an AI system for power plants should monitor the state and
control of engines and their systems, auxiliary mechanisms, power supply systems in accordance with
the plan for their maintenance and inspection.
The conceptual basis for the intellectualization of the solution of interrelated problems of CTS TC
diagnostics and prediction are traditional for the class of unstructured and poorly formalized tasks,
such as the impossibility of obtaining complete and objective information for making adequate
decisions and, due to this circumstance, the need to involve informal (subjective, heuristic)
information; the presence of uncertainty in the initial data, as well as the presence of ambiguity
(multiple options) in the process of finding a solution; the necessity to develop and justify the desired
solutions to the problem under severe time constraints determined by the course of controlled
processes; the necessity to correct and introduce additional information into the process of finding
solutions, the interactive (dialogue, human-machine) nature of the logical inference of solutions.
Taking these factors into account forces us to abandon traditional algorithmic methods and decision-
making models and move on to intelligent system technologies [17].
To successfully solve the problem of ensuring the reliability of ship CTS, it is necessary to remove
a number of uncertainties, each of which is quite complex and significant. Such uncertainties include:
incomplete data on external, internal impacts on systems and on the state of such systems; uncertainty
in the behavior of systems.
The removal of the listed uncertainties can be based on solving the problems of assessing the risk
of CTS failures and its prediction with relative insensitivity to incomplete data on systems [18].
Intellectualization of automated diagnostic systems involves solving a number of interrelated tasks
of a structural, functional, informational and organizational nature, which should be provided at the
design stage of TC CTS diagnostic systems [17].
There are various algorithms, models and methods for predicting TC CTS. AI models are actively
developing, in particular, neural networks. To solve the problem of classifying TC based on
diagnostic results, for example, a probabilistic neural network (PNN network - Probabilistic Neural
Network [19]) is used.
However, the main problem for the productive operation of a neural network is the need for a
significant amount of statistical data, which is difficult to obtain in real conditions due to a number of
reasons (high cost of the systems under study, high costs of testing, limited time, etc.).
Lack of a clear understanding in the choice of neural network architecture for solving various
types of problems (pattern recognition, approximation, forecasting, etc.) and areas of application also
complicates their application.
In AI, knowledge representation models are actively developing - Bayesian networks [20].
Bayesian networks allow us to combine a priori (initial) knowledge about an object with new
(experimental) data to obtain aposteriori (after experimental) estimates. One of the advantages of
using Bayesian belief networks (BBN) for TC diagnostics is their ability to work with uncertain and
incomplete data.
Instead of using rigid rules and thresholds, which may be ineffective in case of complex and
ambiguous situations, the BBN might be useful to provide the possible technique for estimation of the
probability (risk of failures) of various system states based on the available data.
Using BNN for CTS implementation let us to reduce the probability (risk of failures) of false
reactions and improves diagnostics accuracy. In addition, the BBN can be used to analyze not only
individual components of the system with different performance, but also their interactions,
connections, which allows us to identify both individual faults and their interactions, which can be
useful in solving complex problems associated with failures in systems.
To assess the risk of CTS failures based on the BBN, modern and available software technologies
are used (Microsoft Bayesian Network Editor, Bayes Net Toolbox for Matlab, GeNIe, Smile,
AgenaRisk, Analytica, Bayes Server, Hugin Expert). In addition, there are ready-made libraries and
modules for Python, C++, C#, MatLab, R, VB.NET on various operating systems (Windows, Linux,
macOS).
Thus, the problems associated with ensuring the reliable operation of the CTS require
improvement and the search for new methods, models and algorithms implemented in the form of
problem-oriented programs.
They should be aimed at prompt detection of emergency conditions of equipment, at solving
problems of system failures risk assessing and predicting under conditions of relative insensitivity to
incomplete data on FE and FI, which have different operability.
Since all modern ships must be equipped with AI-based technology automation systems, the
implementation of approaches based on such methods, models and algorithms should be aimed at
ensuring the reliable operation of shipboard CTS.
So, taking into account the specifics and existing problems in ensuring reliability during the CTS
shipboard operation, the development and development intellectualization methods, models for
estimating and predicting the risk of failures of complex systems by diagnostic features are important
for the new technologies development performance aimed at ensuring complex systems safety and
reliability.
Statement of the problem: intellectualization of the CTS TC assessment by diagnostic features, to
substantiate the forecast failures risk in subsystems (components), intersystem (intercomponent)
connections of systems with different performance.
Purpose of the work:
ensuring the reliability and safety of work, as well as reducing the risk of CTS failures by
solving causes determining problem of their failures;
formation of principles for the construction and intelligent system operation for assessing and
predicting the risk of CTS failures with different performance, their constituent FE and FI;
intellectualization method and model for the TC estimation and predicting complex systems
failures risk by diagnostic features development. The results might be used relatively insensitive to
incomplete data about systems based the priori information about failures that connects the types
of technical condition FE and FI of complex systems and their diagnostic features.
3. Intellectualization method and model of complex systems failures risk
technical state estimation and prediction by diagnostic features
The proposed method is based on a formalized generalized intellectualization model of TC failures
risk estimation and prediction of complex systems FE and FI by diagnostic features, which can be
described in the following form:
G, S (C ), I S ( I C ), RS (C ) , RI S ( I C ) , L , (1)
where: S (C ) set FE CTS;
I S ( I C ) set FI CTS;
RS (C) , RIS (IC ) set of diagnostic failure risk assessments FE and FI CTS;
G acyclic directed graph;
L mapping relationships between sets S (C ), I S ( I C ) and RS (C ) , RI S ( I C ) , based on the fault tree of
the CTS diagnostic model.
A failures risk diagnostic assessment set of FE and FI CTS
R R S (C ) ,R , (2)
n( m) I S (C ) a ( z )
Rs (c) n ( m) {rs (c ) n ( m) | s (c) 1, S (C ), ns (c ) 1, N S (C ) ,ms (c) 1, M S (C ) },
RI s (c ) {ris (c ) | is (c) 1, I S (C ) , a 1, A, z 1, Z },
a( z) a( z)
where rs (c)n ( m) FE CTS failure risk;
ri s ( c ) FI CTS failure risk;
a(z)
ns (c ) FE CTS number;
ms(c) FE CTS hierarchical level number;
NS (C) FE CTS quantity;
M S (C ) FE CTS hierarchical level quantity;
a intersystem link number;
z interconnect number;
A number of interconnections;
Z number of intercomponent bonds.
Created model for FE, FI failures risk determining:
PS (C ) n ( m ) , PI S ( C ) , D S (C ) , D I S (C ) , e s (c ) , eI s(c) , (3)
a( z) n(m) a( z) n(m) a(z)
where PS (C ) n ( m ) , PI S (C ) conditional failure probabilities FE and FI respectively;
a( z)
DS (C ) , DI S (C ) failure damage FE and FI respectively;
n(m) a( z )
es(c) ,e
n(m) I s (c )a ( z ) FE and FI weight given their hierarchy in CTS respectively.
Failure risk for n ( m ) FE CTS:
R S ( C ) n ( m ) D S ( C ) n ( m ) PS ( C ) n ( m ) ( t ) . (4)
Failure risk for a (z ) FI CTS
R I S (C ) D I S (C ) PI S ( C ) (t ) . (5)
a(z) a(z) a(z)
The total failure risk assessment CTS, taking into account the failures risk assessment of FE, FI is
determined
S(C) N ( M ) I S (C ) A( Z )
R (R s(c) n(m) es (c ) n ( m ) ) ( Ris (c)a ( z ) eI s (c)a ( z ) ) . (6)
s(c ) 1 n ( m ) 1 is ( c ) 1,a ( z ) 1
The failure probability of FE and FI is determined by the formulas:
S (C )n( m) exp( S (C )n( m) TS (C )n( m) )
PS (C )n( m) (t ) S (C )n ( m) S (C )n ( m) , (7)
exp( S (C )n ( m) TS (C )n ( m) )
I S (C ) exp( I S (C ) TI S (C ) )
a( z) a( z) a( z)
PI S (C ) I S (C ) (t ) I S (C ) (8)
a( z) a(z) exp( I S (C ) T( I ) a( z)
a( z) S (C ) a ( z )
where failure rate;
distribution parameter, taken according to the test results equal to 1 / Tˆ0 , Tˆ0 mean time to
failure estimate.
Quantifying damage to FE from failure n(m) subsystem (component) to determine the risk of
failure
DS (C ) n ( m ) {d s (c ) n ( m ) | s (c) 1, S (C ), n 1, N ,m 1, M } , (9)
where d s ( c ) n ( m ) damage from subsystem (component) failure CTS.
Quantifying damage FI from failure a (z ) intersystem (intercomponent) communication:
DI s ( c ) {d I s (c ) | is (c) 1, I S (C ) , a 1, A, z 1, Z }, (10)
a( z) a( z )
where d i s ( c ) damage from failure of intersystem (intercomponent) communication.
a(z)
According to the established conditional failure probabilities and damages from failures FE and FI
according to (4), (5), their risk of failures is determined.
The intellectualization model of complex systems TC failures risk estimation and prediction by
diagnostic features using the BBN apparatus is a synthesis of reliability and diagnostic models. In the
technical condition diagnostics model, the BBN is used to assess the risk of system failure
(probability).
To create a diagnostic TC model, it is necessary to determine the risk of failure (conditional
probabilities) for each node in the network. This data is derived from expert knowledge and analysis
of historical data.
After determining the risk of failure (conditional probabilities), the model can be used to estimate
(predict) the CTS state.
To do this, the model determines the risk of failure for system’s each state using information about
the current values of the system’s state and the failures risk determined for each node in the network.
The technique for building a model based on BBN can be represented as follows:
1. Building a BBN:
1.1 Vertices and intersystem (intercomponent) Bayesian networks are created, denote subsystems
(components) of CTS, according to their TC:
1.1.1 Each subsystem (component) can be in the following technical condition:
mS (C )
Work n operational state nS (C ) subsystem (component) m S (C ) level;
S (C )
mS (C )
Not _ work n failure partial (complete) nS (C ) subsystem (component) m S (C ) level.
S (C )
1.1.2. Each intersystem (intercomponent) connection is in the states:
Work b , q operational state a ( z ) I S ( C ) intersystem (intercomponent) communication b(q )
a ( z ) I S (C )
level;
Not _ work a(bz,)q failure partial (complete) a ( z ) I S ( C ) intersystem (intercomponent)
I S (C )
communication b(q ) level, where b number of hierarchical level of system interconnection; q
number of hierarchical level of component interconnection.
1.2. The connections between the nodes of the Bayesian network are indicated, denoting
subsystems (components), intersystem (interelement) CTS connections and diagnostic appropriate
assessments R.
2. BBN parameters are specified:
2.1. Initial failure risk for FE and FI CTS, assuming that they are all operational before the start of
the CTS
m S (C ) m S (C )
R (Work n ) t 0 F ( P (Work n )t 0 ) 0 ; (11)
S (C ) S (C )
R (Work a(bz,)q ) t 0 F ( P (Work a(bz, )q )t 0 ) 0
I S (C ) I S (C )
.
2.2. Initial failure risk for FE and FI CTS, assuming that they are all inoperable before the start of
the CTS
mS ( C ) mS ( C )
R( Not _ workn )t 0 F ( P( Not _ workn )t 0 ) 1 ; (12)
S (C ) S (C )
R ( Not _ work a(bz,)q ) t 0 F ( P ( Not _ work a(bz,)q )t 0 ) 1 .
I S (C ) I S (C )
2.3. Risk of failure of FE and FI CTS at the current moment of time, provided that some FE and FI
failed at the previous moment of time
mS ( C ) mS ( C )
R (( Not _ work n )t /( Not _ work n )t 1) 1 ; (13)
S (C ) S (C )
R (( Not _ work a(, bz ,)q ) t /( Not _ work a(bz, )q ) t 1 )) 1 .
I S (C ) I S (C )
2.4. FE and FI CTS failure risk at the current moment of time, while they are in a working state at
the current moment of time, which also were operable at the previous moment of time
m
n S ( C ) t jm S ( C )
m m e S (C ) n
R ((Work n S (C ) )t /(Work n S (C ) )t 1 ) m
e S (C )
0 ; (14)
S (C ) S (C ) n S (C ) (t 1)
S (C )
e
a b( z, q) t
I S (C ) b ,q
e
R ((Work a(bz,)q ) t /(Work a(bz,)q
a ( z )I
S (C )
) t 1 ) e 0.
I S (C ) IS 9C 0 a b( z, q) ( t 1)
I S (C )
e
2.5. FE and FI CTS failure risk at the current moment of time, subject to failure of FE and FI at the
current moment of time, provided that it were operational at the previous moment of time
mS ( C )
m m n
R (( Not _ work n S ( C ) )t /(Work n S ( C ) )t 1 ) (1 e S ( C ) ) DS (C ) n ( m ) ; (15)
S (C ) S (C )
b ,q
R (( Not _ work a(bz,)q )t /(Work a(bz,)q )t 1 ) (1 e
a( z) I S (C )
) DI S (C )
I S (C ) I S (C ) a( z)
4. Experiments and results analysis
As an example, when modeling the BBN SPP, various failure risk values input element were
selected and the predicted failures risk values and operability of the FE and FI of the power plant for
20,000 hours of installation operation were determined. Symbols of subsystems, components of the
ECS are given in Table 1.
The purpose of using BBN in assessing how the risk of loss of performance due to the risk of
failures FE and FI CTS is posteriori.
The a priori data are dynamically recalculated and form a posterior failure risk estimate, which is a
priori information, to process the new information. Post hoc inference is based on procedures for
analyzing data obtained from the use of BBN.
Failure risk value predicted distributions for blocks and links of the multilevel structure diagnostic
model (shown in Fig. 1) in BBN with a serial connection, for example, SPP subsystems IE - CAS -
SPP and interconnections IE-CAS, CAS - SPP and SPP operation for 20,000 hours for SPP shown in
Fig.2.
From the given a priori and a posteriori data based on the SPP study results included in the SPP
multi-level structure, for 20,000 hours of operation for a conditionally accepted SPP failure risk value
input component of 0.26 and possible failures of IE, CAS and interconnections IE - CAS, CAS - SPP
according to posterior data, the predicted risk of SPP failure will change from 0.25 to 0.68.
Table 1
Symbols of subsystems, components of the SPP
Sub‐ Hierar‐ Subsystem (component) name Symbol Subsystem
system chical (component)
(compo‐ number weight
nent) subsystem es(c)
n ( m)
number (compo‐
ns(c) nent) level
( ms (c ) )
1 1 Input element IE 0,26
2 2 Firefighting system FFS 0,01
3 2 Compressed air system CАS 0,047
4 2 Main engine manual control MCME 0,035
5 3 Control system CS 0,081
6 3 Main engine remote automated control system RACSME 0,01
7 3 Intermediate component Р1 0,01
8 3 Ship power plant SPP 0,09
9 4 Main engine ME 0,16
10 4 Ballast drainage system BDS 0,019
11 4 Emergency drive propulsion and steering ED PSC 0,01
complex
12 4 Propulsion and steering complex control system CSPSC 0,081
13 4 Boiler plant BP 0,13
14 5 Power transfer from main engine to propeller TPMEP 0,003
15 5 Intermediate component Р2 0,01
16 6 Propulsion and steering complex PSC 0,01
17 7 Output component EXIT 0,26
Similarly, from a retrospective results analysis obtained by modeling SPP, FE and FI are identified
that affect the overall system performance.
From the research results, as reflected in the diagnostic model, it follows that the maximum non-
operating state during the operation of the SPP 20000 hours corresponds to the CSPSC subsystem
directly.
Since the CSPSC subsystem is dependent in the level of the SPP layered structure, then in the
future it is necessary to check the subsystem in order to find its failure cause.
The implementation of the intellectualization method and model for estimation and prediction CTS
failures risk in the conducted studies, carried out by modeling on a priori and a posteriori data,
determines the FE and FI SPP that have the greatest impact on the performance of the main engine
and the operation of the entire system at various points in time.
Carrying out studies of emergency situations, analysis of incidents in the CTS will determine the
causes of the accident FE and FI SPP.
Thus, based on the intellectualization of FE and FI CTS TC estimation by diagnostic features, it is
possible to substantiate the failure risk prediction for FE and FI systems with a large number of
variable parameters and with different operability.
The considered principle of intelligent system functioning (due to its structure) in terms of the
technical and technological foundations of construction on the SPP example reflected in the method
and model for assessing and predicting the FE and FI CTS risk failures.
Method and model implementation with different operability and incomplete data let us to assess
and predict the risk of CTS failures on network infrastructures with relative insensitivity to
incomplete data.
Figure 1: Diagnostic model of the technical condition of the SPP using the BBN in the GeNIe
environment
Figure 2: A posteriori and a priori estimates of the risk SPP for the input component failure risk of
the ECS 0.26
5. Conclusion
Intellectualization of the CTS TC assessment by diagnostic features, the failures risk predicted
values in subsystems (components), intersystem (intercomponent) connections of systems with
different operability and incomplete data are substantiated.
In order to ensure the reliability and safety of work, as well as reduce CTS failures risk the all our
main tasks were solved. It was determining the causes of their failures, we formated of principles for
the construction and operation of an intelligent system for assessing and predicting CTS risk failures
with different performance, their constituent FE and FI; development of a method and model for the
intellectualization TC estimation and predicting complex systems failures risk by diagnostic features
that are relatively insensitive to incomplete data about systems, based on the use of a priori
information about failures, linking the types of technical condition FE and FI of complex systems and
their diagnostic features in the bounce risk form.
The use of the developed method and model, which takes into account the hierarchical levels of
subsystems (components), intersystem (interelement) links when searching for the causes of failures
in complex technical systems, allows us to control the risk of failures in systems when information
about failures in their structures is received. The application of the method and model makes it
possible to predict trends in the risk of system failures, with updated changes in the risk of failures of
individual subsystems (components), intersystem (interelement) links in order to further choose a
strategy for their recovery.
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