” PROGNOSTIC ” – ADAPTIVE INTELLIGENT DIAGNOSTIC SYSTEM FOR VEHICLES

A. A. Poddubnaya, A. V. Keller

FSUE “NAMI”, Moscow, Russian Federation

E-mail: poddubnaya575@gmail.com

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 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 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 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.Just need to proof read the paper, make sure grammar is correct