" PROGNOSTIC " - ADAPTIVE INTELLIGENT DIAGNOSTIC SYSTEM FOR VEHICLES A. A. Poddubnaya, A. V. Keller FSUE "NAMI", Moscow, Russian Federation E-mail: [email protected] Abstract. The article contains general information about promising vehicle diagnostic systems. Existing diagnostic systems, including those built into modern vehicles (TS), are not able to predict the moment of failure of components and assemblies, but only state the fact of a malfunction. To diagnose the current state and forecast the residual life of the vehicle in motion mode, it is proposed to use a mathematical model based on machine learning technologies and data from standard and additional sensors, vehicle detectors. Using this approach will make it possible to forecast the occurrence of a defect before its actual occurrence. Keywords: advanced diagnostic systems, autonomous vehicle, connected cars, unmanned vehicles, technical condition monitoring, mechanical failure detection, fault prediction, sensors, detectors, digital data processing methods Introduction For autonomous transport and connected vehicles, diagnostic of the vehicle’s technical condition is a basic safety standard. * The issue of determining the mechanical failure of an autonomous vehicle is extremely relevant, due to the lack of a driver who can appreciate uncharacteristic noises or external vibrations. Errors received from the vehicle’s CAN bus are not sufficiently informative in assessing the current state of the vehicle and do not predict a breakdown or a failure. For a driverless vehicle, at the stage of its design, an expanded self-diagnosis system should be laid. During operation, onboard the vehicle, data from sensors and a reliability monitoring system should be processed and further data transferred to the ITS - intelligent transport system, as well as to the servers of owners and manufacturers. (* according to researches of the European Commission.) Main part Almost all modern cars are modified with a variety of full-time detecting devices and sensors, fixing faults and operation errors of some nodes by electrical parameters and fixing “extreme” system states in codes. Error icons appear on the vehicle dashboard when the system diagnoses a fault. If the driver notes the incorrect operation of certain nodes, systems and you need to make sure in what, really technical condition is the transport, then a specialized diagnosis is carried out. To clarify the technical condition, the computer diagnostics of the vehicle is performed by a certified technical specialist: a scanner with software is connected to the on-board systems, through special diagnostic connectors, CAN, which reads all the codes and errors transmitted by the car about possible malfunctions on the main nodes. Error codes are currently vendor specific, are set by OEM and are available for reading and monitoring in a limited list of codes. The received codes are decrypted by specialists, again using special ...

1. " PROGNOSTIC " - ADAPTIVE INTELLIGENT DIAGNOSTIC
SYSTEM FOR VEHICLES
A. A. Poddubnaya, A. V. Keller
FSUE "NAMI", Moscow, Russian Federation
E-mail: [email protected]
Abstract. The article contains general information about
promising vehicle diagnostic systems. Existing diagnostic
systems, including those built into modern vehicles (TS), are
not able to predict the moment of failure of components and
assemblies, but only state the fact of a malfunction. To diagnose
the current state and forecast the residual life of the vehicle in
motion mode, it is proposed to use a mathematical model based
on machine learning technologies and data from standard and
additional sensors, vehicle detectors. Using this approach will
make it possible to forecast the occurrence of a defect before its
actual occurrence.
Keywords: advanced diagnostic systems, autonomous vehicle,
connected cars, unmanned vehicles, technical condition
monitoring, mechanical failure detection, fault prediction,
sensors, detectors, digital data processing methods
Introduction
For autonomous transport and connected vehicles, diagnostic of
the vehicle’s technical condition is a basic safety standard. *
The issue of determining the mechanical failure of an
autonomous vehicle is extremely relevant, due to the lack of a
driver who can appreciate uncharacteristic noises or external
vibrations. Errors received from the vehicle’s CAN bus are not
sufficiently informative in assessing the current state of the
vehicle and do not predict a breakdown or a failure. For a
driverless vehicle, at the stage of its design, an expanded self-
diagnosis system should be laid. During operation, onboard the
vehicle, data from sensors and a reliability monitoring system
should be processed and further data transferred to the ITS -
intelligent transport system, as well as to the servers of owners

2. and manufacturers. (* according to researches of the European
Commission.)
Main part
Almost all modern cars are modified with a variety of full-time
detecting devices and sensors, fixing faults and operation errors
of some nodes by electrical parameters and fixing “extreme”
system states in codes. Error icons appear on the vehicle
dashboard when the system diagnoses a fault. If the driver notes
the incorrect operation of certain nodes, systems and you need
to make sure in what, really technical condition is the transport,
then a specialized diagnosis is carried out. To clarify the
technical condition, the computer diagnostics of the vehicle is
performed by a certified technical specialist: a scanner with
software is connected to the on-board systems, through special
diagnostic connectors, CAN, which reads all the codes and
errors transmitted by the car about possible malfunctions on the
main nodes. Error codes are currently vendor specific, are set
by OEM and are available for reading and monitoring in a
limited list of codes. The received codes are decrypted by
specialists, again using special programs, and based on the
information received, a conclusion is made about the presence
of certain failures or malfunctions. On-board data consists of
thousands of signals from sensors and ECUs that are transmitted
through the CAN network.
They are sent repeatedly with a certain frequency and form
continuous data streams that can be used both for driving a
vehicle and for signaling the status of various components of
the vehicle. Research on monitoring and signal analysis of
standard sensors is carried out by automakers and thiere are
used to date, continuous registration on board vehicles has been
limited, on equipment during the testing period when
developing new models or modify equipment. These systems are
expensive and designed for product development.
There are not many researches in the automotive industry about
resource forecasting using on-board data from standard sensors.
Currently, such methods either require the participation of a

3. person for an expert assessment or the technical condition of the
car is assessed by monitoring signals and comparing them with
a model of a perfect process. In the review of the development
of the problem, the main approaches to the solution are
formulated in the following methods: the Model Based
Diagnostics (MBD) method and the Condition Based
Maintenance - CBM method [1] . There are effective studies
based on on-board forecasting methods:
- D’Silva carried out for a complex stationary system based on a
method based on the formation of a cross-correlation matrix,
including pairwise correlations between signals, where the
Mahalanobis distance is used as an assessment scale to search
for deviations and malfunctions [2]. The full correlation matrix
is used to determine vehicle status. Normal workspace is
determined from experimental data. The system works on board
mods with saved normal operation models, and this was
demonstrated on simulated data.
- stationary signals for finding damage were used by Vachkov
[3] and Kargupta et al. [4]. Their systems consist of an onboard
part that continuously monitors the vehicle and loads models
into the onboard analogue of OEM monitoring systems. An
autonomous system includes a database in which data models,
faulty and faultless systems are stored [5]. Also, for embedded
on-board systems with limited resources, methods are developed
in which sudden changes in the correlation matrix are signs of
wear or failure [6].
Automakers until recently were not interested in developing
technologies for monitoring operation, but in the present, due to
the emergence of contractual relations on the principle of “full
cycle service” related to the development of rental, commercial
and unmanned vehicles, the topics of reliability forecasting are
updated. Commercial vehicle manufacturers have not yet
released any advanced forecasting solutions to the market.
There are simple preventative maintenance solutions that track
wear and the use of brake pads, clutches, and similar wear
equipment and are predicted for the future. All of them are

4. based on data streams that are aggregated on board and
transmitted to a remote office. Mercedes and MAN, among other
things, offer direct customer solutions for proactive service
recommendations and remote monitoring. Volvo for commercial
vehicles includes forecasting systems offered with maintenance
contracts. Volkswagen, BMW [7] and GM [8] have methods for
predicting future service needs based on telematics solutions
and on-board data. VW and BMW offer preventative
maintenance as a maintenance solution for the owner, and GM
publishes recommended repairs through the OnStar portal.
Developed embedded on-board solutions have unlimited access
to real-time data streams. This provides fast detection, since the
detection algorithms are located close to the data source. On-
board solutions usually have limited computing and storage
capabilities, since the hardware must be automotive-grade, for
example, resistant to water, shock and electromagnetic
interference, as well as inexpensive. Typically, automotive
electronics are usually two to three generations behind the
consumer market.
The conceptual model developed by the authors is planned for
use in terms of the implementation of remote diagnostics
services of the intelligent transport system (ITS) for the
connected transport. This service is assumed to be mandatory
for the purpose of ensuring the operation of connected highly
automated vehicles. The service will provide feedback to
vehicle manufacturers on the organization of the full product
life cycle.
The direction of the ongoing research coincides with the
direction of work within the framework of the European
Commission C-ITS. According to the act of which (Fig. 1)
(Cooperative Intelligent Transport System) Delegated Act,
adopted and agreed by key stakeholders from the automotive,
motorcycle, agricultural, and telecommunications industries;
international technical organizations are developing integrated
solutions for the priority of road safety.
Figure 1. Scheme of interaction of International Technical

5. organizations, which are making standards for the development
of ITS
The 3GPP international consortium develops technical
specifications and technical reports in the field of network
technologies in mobile systems together with ETSI, the
European Telecommunications Standardization Institute, which
transfers the developed documentation on communication
standards and ITS services to the ITU (International
Telecommunication Union), which is a specialized institution
within the United Nations (UN) and is responsible for issues
related to information and communication technology. ITU
transfers data to the United Sustainable Nations Development
Group, the United Nations Development Group, which approves
international standards at the ISO site.
In international concepts for the development of ITS, special
attention is paid to the issues of diagnostics and monitoring the
technical condition of connected and unmanned vehicles. The
3GPP international consortium is developing a standard for
connected vehicles * 3GPP 22.885 p. 5.27 “Remote diagnosis
and just-in-time repair notification” - Remote diagnosis and just
in time repair notification, which provides for the installation of
devices with a hardware-software complex that support
interaction on a connected vehicle V2X (Vehicle-to-everything)
and collect diagnostic data from sensors inside the vehicle.
Diagnostics of the technical condition of the vehicle is solved
by developing a prognostic model for monitoring electrical,
mechanical and hydraulic failures of the vehicle components
and assemblies.
Figure 2. The relationship of databases in the developed system
The novelty of the idea and its advantages lie in the creation of
a model for processing data obtained from various monitoring
and diagnostic systems, using a neural network to predict the
operating time to failure of a node, taking into account the
optimal load mode.
Figure 2 shows the relationship of the databases of the
described system, which may be applicable for various TS.

6. Information is collected as follows: additional sensors are
installed on the vehicle, data from which together with data
from the CAN bus and theoretical and statistical results are
received and processed on the on-board AIC by a neural
network, which after initial data conversion and predictive
analysis sends it to a remote ITS AIC. The mathematical models
used in the agro-industrial complex rank incoming data by the
integrated indicators for assessing the residual life of the
diagnosed nodes. The analytical system of the ITS AIC during
operation and taking into account the assessment of the impact
of current loads, as well as modeling past and future operating
parameters, creates a virtual dynamic operational model for a
particular vehicle.
Information from sensors and detectors, based on
measurements of vibration, acoustic emission and the intensity
of the generated heat, determines the technical condition of the
hydraulic or mechanical components of the vehicle, and is used
in the construction of a virtual mathematical model on the ITS
AIC. The used complex energy parameter of acoustic emission
adequately estimates the change in the friction coefficient in the
kinematic pair. The complex energy parameter of acoustic
emission, in Fig. 3 - parameter D, is calculated based on the
analysis of acoustic emission signals of a working friction pair
at ultrasonic frequencies of 20-300 KHz, with ranges of
operating units from 10 to 10,000 rpm. The sources of acoustic-
emission signal formation in the ultrasonic frequency range are
elastic deformation waves formed by the dissipation of fracture
energy in the structure of materials [9]. Picture 3 shows the
confirmed results obtained using an acoustic emission analyzer
in experimental modeling of a friction pair.
The transition to the evaluation of the signal by acoustic
emission makes it possible to evaluate the magnitude and nature
of the change in energy dissipation that occurs when objects
interact in the ultra-acoustic range, to obtain information on
friction in the assembly and to diagnose this process with an
assessment of the state of the friction pair, landing quality,

7. nature and lubrication conditions and a number of other
parameters in contact. So, when changing the level of the
acoustic emission signal in the friction unit, it is possible to
evaluate the lubricant quality of the controlled unit. Constant
monitoring of the quality of the lubricant will reduce the wear
rate and the development of defects.
To assess energy losses by the acoustic emission method, a
diagnostic tool is used, which fixes the integral indicator of the
complex energy parameter of acoustic emission. According to
the results of previous studies, RF patent No. 2427815 -
G01M13 / 02 test transmission mechanisms "Method for the
diagnosis of mechanical transmissions" [10].
Figure 3. The ratio of the complex energy parameter D to the
force at the contact spot of the simulated kinematic pair, N
Calculation methods have objectively established the
proportionality of the integral indicator of the acoustic emission
sensor signal and the friction coefficient, both for gears of gears
and bearings. It is this area that has expanded the capabilities of
existing non-destructive testing methods, will allow solving
practical problems of monitoring and forecasting the state of
technology, rationally distributing forces and means during
repair and inspection of the technical condition of a vehicle.
The resource forecasting in the traditional approach is based on
the design and construction parameters of the elements of the
nodes, and failure statistics and does not give a real result on
operational reliability. It is proposed to change the approach to
data processing methods using machine learning. In this case,
node element failures are considered as some abstract random
events of the multifactor process, and the diverse physical
conditions of products are reduced to two states: serviceability
and malfunction. Prediction problems must be considered with
errors in the initial and boundary conditions even when the non-
stationary process can be considered as a strictly deterministic
process, that is, its outcome is completely determined by the
algorithm, the values of the input variables and the initial state
of the system. Based on mathematical models of vehicle

8. components and the dependences of changes in the data of
sensors, detectors when compared with operating conditions, as
well as data on the CAN bus, it is proposed to create an
analytical complex applicable to a wide range of equipment and
vehicles.
The resource forecasting in the traditional approach is based on
the design and construction parameters of the elements of the
nodes, and failure statistics and does not give a real result on
operational reliability. It is proposed to change the approach to
probabilistic data processing methods. The calculation is built
on the probability of a node element failure depending on the
current state of the interface and loads. In this case, node
element failures are considered as some abstract random events
of the multifactor process, and the diverse physical conditions
of products are reduced to two states: serviceability and
malfunction. Prediction problems must be considered with
errors in the initial and boundary conditions even when the non-
stationary process can be considered as a strictly deterministic
process, that is, its outcome is completely determined by the
algorithm, the values of the input variables and the initial state
of the system. Based on mathematical models of vehicle
components and the dependencies of changes in sensor data,
when compared with operating conditions, as well as CAN
network data, it will be possible to create an analytical complex
applicable to a wide range of vehicles and vehicles.
The complexity of the implementation of the diagnostic
complex lies not only in the need to take into account the
operating conditions of a particular vehicle, but also to carry
out maintenance in accordance with these conditions. A modern
service system should interconnect the data of continuous
monitoring of the technical condition of individual vehicles;
planning of their operation by the trucking company and the
willingness of the service department to fulfill the required list
of maintenance and repair of vehicles.
A common problem that the operating organization faces when
using service schedules and planned site replacements is the

9. performance of an inappropriate amount of asset maintenance.
Since calendar-based maintenance does not take into account
the asset’s performance, the frequency of maintenance work can
often be either too high or too low. These problems can be
prevented by optimizing and improving preventative
maintenance programs.
The system of preventive diagnostics based on artificial
intelligence will make it possible in the future to abandon the
system of scheduled preventive repairs and switch to
maintenance by actual condition [11], which experts estimate
will reduce maintenance costs by 75%, the number of services
by more than 50% reduction in the number of failures by 70%
for the first year of operation.
Conclusions
Advantages of maintenance using the developed system:
· The system can be applicable in various industries, the system
is adaptable;
· Registration of signs of fracture appearances and analysis of
their variations is performed. Assessment of the probability of
failure of both the system as a whole and its individual nodes;
· Failures of nodes are transferred from the sudden category to
the predicted category, due to early detection and notification of
personnel about a developing malfunction;
· Spare parts logistics is being optimized - timely ordering and
delivery.
The expected economic effect from the introduction of the
developed system of preventive diagnostics is possible due to
reduced maintenance costs, reduced downtime due to
malfunctions, and repair costs will be reduced. The developed
system solves the problems of insufficient competence of
employees in assessing the technical condition, and will also
help maintain the working condition of aging equipment in a
limited budget.
Realized analogues in the aspect of objective resource
forecasting do not exist. The advantage is the prospect of
integration into the ITS (Intelligent Transport System), and the

10. creation of a predictive service for any type of vehicle, and
most importantly this is the only way to diagnose unmanned
vehicles in the field.
References
[1] Fault-Diagnosis Systems - An Introduction from Fault
Detection to Fault Tolerance, Authors: Isermann, Rolf - ISBN
978-3-540-30368-8
[2] S. H. D’Silva. Diagnostics based on the statistical
correlation of sensors. Technicalpaper 2008-01-0129, Society of
Automotive Engineers (SAE), 2008
[3] G. Vachkov, “Intelligent data analysis for performance
evaluation and fault diagnosis in complex systems,” in IEEE
International Conference on Fuzzy Systems, - July 2006, pp.
6322-6329
[4] H. Kargupta et al., “VEDAS: A mobile and distributed data
stream mining system for real-time vehicle monitoring,” in Int.
SIAM Data Mining Conference, 2003.
[5] Mohammad MesgarpourDario Landa-SilvaIan Dickinson -
Overview of Telematics-Based Prognostics and Health
Management Systems for Commercial Vehicles - 13th
International Conference on Transport Systems Telematics, TST
2013, pp 123-130
[6] Hillol Kargupta, Michael Gilligan, Vasundhara Puttagunta,
Kakali Sarkar, Martin Klein, Nick Lenzi, and Derek Johnson.
MineFleet®: The Vehicle Data Stream Mining System for
Ubiquitous Environments, volume 6202 of Lecture Notes in
Computer Science, pages 235-254. Springer, 2010.
[7] Von Harald Weiss. Ingenieur.de predictive maintenance:
Vorhersagemodelle krempeln die wartung um.
https://www.ingenieur.de/technik/forschung/predictive-
maintenance-vorhersagemodelle-krempeln-wartung-um/, 2019-
08-23.
[8] OnStar. On Star on-star services. https://www.onstar.com,
2014, 2018-08-23.
[9] - Assessment of the lubricity of gear oils of mining
machines -A. A. Poddubnaya, A. S. Fokin, S. L. Ivanov, E. A.

11. Kremcheev, V. S. Potapenko - Notes of the Mining Institute,
vol. 178 - http://pmi.spmi.ru/index.php/ pmi / article / view /
2176
[10] - RF patent No. 2427815 - G01M13 / 02 test transmission
mechanisms "Method for the diagnosis of mechanical
transmissions." http://www.freepatent.ru/patents/2427815
[11] - Management of equipment maintenance and repair:
automation capabilities - I.N. Antonenko NPP SpetsTek 2019-
05-20,
http://trim.ru/sites/default/files/files/pdf/maintenance_food_ind
ustry.pdf
7
ENG 112
Week 13 Pre-Conference Questions: Status of Research Process
In addition to bringing this sheet filled out, you also need to
bring two of your secondary sources to our conference. You can
bring hard copies or a tablet/laptop (but no phones) for digital
format sources.
1) Explain your research topic as precisely as you can in 2-4
sentences. Include your two primary sources in your
description.
My topic is about Asian American College students who
are more likely to have suicidal thoughts compared to others
and least likely to seek for help? On my primary sources
“Everything I never told you” by Ng, Linda’s character died at
the very beginning of the story, her death remains questionable
till the later part when police and media get involved and that
her death was likely a suicide. My other primary source “Why
are Asian American kids killing themselves” is about a
commentary of Luke Tang’s documentary “Looking for Luke” a
Harvard sophomore who hang himself at dormitory. Both story
was tragic but for any suicidal ideation there is always a reason
and that’s why I choose to explore this topic to further enhance

12. my knowledge and illustrate that mental health issues cannot be
ignored.
2) Write three of your central research questions.
1: Why Asian American College students killing
themselves?
2: Why is it hard for them to seek mental health care or
counseling?
3: How the stereotypes and discrimination of Asian
American lead to mental health issues and stress?
3) Briefly explain the two secondary sources you brought.
What are they saying? How do they fit within your research
project? What kind of perspective do they give about your
topic?
1: Tang and Masicampo believes that suicidal ideation for
Asian American college students are linked to interpersonal
issues like family struggles, culture adaptation and coping with
racism that is intertwined between the feeling of hopelessness,
loneliness and a sense of being a liability to others. They both
gave me relative information about how a behavior can lead to
suicidal ideation among Asian American students.
2: Kim and Omizo emphasizes on the concept of adherence to
cultural values and their relation to stress, feeling of
marginality and alienation and identity confusion. They examine
the relations between Asian American college students
adherence to Asian and European American cultural values in
particular to coping with cultural differences. This article fit in
my research topic because biculturalism is one of the reasons
why Asian American students consider themselves as an
outsider.
4) Tell me about how your research project is going. You can
write as little or as much as you want. What are you anxious
about at this point? How has your project changed since you

13. started? What kind of difficulties have you encountered? How
confident are you about making your argument?
At this moment I am still gathering some information to further
justify and support the argument. Since I started researching
about this topic, I become more knowledgeable about the mental
health illness and even get certification as mental health first
aider accredited by National Council for Mental Health. This
topic made me realize that suicide is a serious problem in our
society especially for young generations.
Please focus on the argument between the two primary sources
and use the secondary sources to back up the idea of the thesis
statement:
example: Asian American college students face a lot of mental
health issues compared to others and often times they don’t seek
help. Based on my research Asian American college
students…… for this reason…
PRIMARY SOURCES:
Ng, Celeste. Everything I Never Told You. Abacus, 2018.
Qiao, George. “Why Are Asian American Kids Killing
Themselves?” Plan A Magazine, 8 Oct.
2017, https://planamag.com/why-are-asian-american-kids-
killing-themselves-477a3f6ea3f2.
SECONDARY SOURCES:
Kim, Paul Youngbin, and Donghun Lee. “Internalized Model
Minority Myth, Asian Values, and Help-Seeking Attitudes
among Asian American Students.” Cultural Diversity and Ethnic

14. Minority Psychology, vol. 20, no. 1, 2014, pp. 98–106.,
doi:10.1037/a0033351.
Tang, Yun, and E. J. Masicampo. “Asian American College
Students, Perceived
Burdensomeness, and Willingness to Seek Help.” Asian
American Journal of Psychology, vol. 9, no. 4, 2018, pp. 344–
349., doi:10.1037/aap0000137.
Kim, Bryan S. K., and Michael M. Omizo. “Asian and European
American Cultural Values,
Collective Self-Esteem, Acculturative Stress, Cognitive
Flexibility, and General Self-Efficacy Among Asian American
College Students.” Journal of Counseling Psychology, vol. 52,
no. 3, 2005, pp. 412–419., doi:10.1037/0022-0167.52.3.412.
Mccarron, Graziella Pagliarulo, and Karen Kurotsuchi Inkelas.
“The Gap between Educational
Aspirations and Attainment for First-Generation College
Students and the Role of Parental Involvement.” Journal of
College Student Development, vol. 47, no. 5, 2006, pp. 534–
549., doi:10.1353/csd.2006.0059.
McKim, Jenifer B. “As Student Suicides Rise, A Harvard Case
Opens New Questions About Schools' Responsibility.” News,
WGBH, 17 Sept. 2019,
https://www.wgbh.org/news/education/2019/09/16/as-student-
suicides-rise-a-harvard-case-opens-new-questions-about-
schools-responsibility.