A framework and methods for on-board network level fault diagnostics in automobiles

A significant number of electronic control units (ECUs) are nowadays networked in automotive vehicles to help achieve advanced vehicle control and eliminate bulky electrical wiring. This, however, inevitably leads to increased complexity in vehicle fault diagnostics. Traditional off-board fault diagnostics and repair at service centres, by using only diagnostic trouble codes logged by conventional onboard diagnostics, can become unwieldy especially when dealing with intermittent faults in complex networked electronic systems. This can result in inaccurate and time consuming diagnostics due to lack of real-time fault information of the interaction among ECUs in the network-wide perspective. This thesis proposes a new framework for on-board knowledge-based diagnostics focusing on network level faults, and presents an implementation of a real-time in-vehicle network diagnostic system, using case-based reasoning. A newly developed fault detection technique and the results from several practical experiments with the diagnostic system using a network simulation tool, a hardware- in-the- loop simulator, a disturbance simulator, simulated ECUs and real ECUs networked on a test rig are also presented. The results show that the new vehicle diagnostics scheme, based on the proposed new framework, can provide more real-time network level diagnostic data, and more detailed and self-explanatory diagnostic outcomes. This new system can provide increased diagnostic capability when compared with conventional diagnostic methods in terms of detecting message communication faults. In particular, the underlying incipient network problems that are ignored by the conventional on-board diagnostics are picked up for thorough fault diagnostics and prognostics which can be carried out by a whole-vehicle fault management system, contributing to the further development of intelligent and fault-tolerant vehicles

A framework and method for on -board network level fault diagnostics in automobiles

A significant number of electronic control units (ECUs) are nowadays networked in automotive vehicles to help achieve advanced vehicle control and eliminate bulky electrical wiring. This, however, inevitably leads to increased complexity in vehicle fault diagnostics. This thesis proposes a new framework for on-board knowledge-based diagnostics focusing on network level faults, and presents an implementation of a real-time in-vehicle network diagnostic system, using case-based reasoning.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

In-vehicle network level fault diagnostics using fuzzy inference systems

This paper presents an application of an Adaptive-Network-based Fuzzy Inference System (ANFIS) for pre-diagnosing incipient underlying in-vehicle network problems which possibly could cause further failures. An experiment on ANFIS-based pre-diagnosis of network level faults on Controller Area Network (CAN) by utilising available network protocol signals, such as error frames, is reported. The experimental results show that the pre-diagnostic system can efficiently classify causes of error frames transmitted on a CAN bus, and identify "network health" which indicates healthiness of the network when being used for message communication. The potential causes of the faults can be narrowed down, and further network diagnostics and prognostics can be performed. (C) 2011 Elsevier B.V. All rights reserved

Adaptive OSEK Network Management for in-vehicle network fault detection

Rapid growth in the deployment of networked electronic control units (ECUs) and enhanced software features within automotive vehicles has occurred over the past two decades. This inevitably results in difficulties and complexity in in-vehicle network fault diagnostics. To overcome these problems, a framework for on-board in-vehicle network diagnostics has been proposed and its concept has previously been demonstrated through experiments. This paper presents a further implementation of network fault detection within the framework Adaptive OSEK Network Management, a new technique for detecting network level faults, is presented. It is demonstrated in this paper that this technique provides more accurate fault detection and the capability to cover more fault scenarios