Model‐based validation of diagnostic software with application in automotive systems

Abstract

Software validation aims to ensure that a particular software product fulfils its intended purpose, and needs to be performed against both software requirement as well as its implementation (i.e. product). However, for diagnostic software (i.e. a diagnoser) performing online diagnosis against certain fault models and reports diagnosis decision, the underlying fault models are usually not explicitly specified, neither by formal language nor by descriptive language. The lack of formal representation of fault models leaves the intended purpose of the diagnostic software vague, making its validation difficult. To address this issue, the authors propose various model-based techniques that can generate concrete examples of the diagnoser's key properties. Such examples are represented in an intuitive and possibly visualised way, facilitating the designers/users to approve or disapprove the conformance of the diagnoser to the intended purpose. The proposed techniques work for validation of both the requirement and implementation that can be modelled as finite state machine, and are illustrated through applications on vehicle onboard diagnostic requirement.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Fault detection and separation of hybrid electric vehicles based on kernel orthogonal subspace analysis

“…− The instrument-based FDD method: Maintenance workers detect and locate faults using various instruments to measure parameters, waveforms, curves, and other relevant data. However, this approach incurs significant maintenance costs [7,8].…”