A Probabilistic Vehicle Diagnostic System Using Multiple Models
نویسندگان
چکیده
In addition to being accurate, it is important that diagnostic systems for use in automobiles also have low development and hardware costs. Model-based methods have shown promise at reducing hardware costs since they use analytical redundancy to reduce physical redundancy. In addition to requiring no extra sensors, the diagnostic system presented in this paper also allows for high accuracy and low development costs by using information from multiple simple models. This is made possible by the use of a Bayesian network to process model residuals. A hybrid, dynamic Bayesian network is used to model the temporal behavior of the faults and determine fault probabilities. A prototype of the system has been implemented and tested on a Mercedes-Benz E320 sedan. This paper describes the prototype system and presents results demonstrating the system’s advantages over traditional residual threshold techniques.
منابع مشابه
Monte Carlo Simulation to Compare Markovian and Neural Network Models for Reliability Assessment in Multiple AGV Manufacturing System
We compare two approaches for a Markovian model in flexible manufacturing systems (FMSs) using Monte Carlo simulation. The model which is a development of Fazlollahtabar and Saidi-Mehrabad (2013), considers two features of automated flexible manufacturing systems equipped with automated guided vehicle (AGV) namely, the reliability of machines and the reliability of AGVs in a multiple AGV jobsho...
متن کاملA Probabilistic Approach to Residual Processing for Vehicle Fault Detection
This paper presents a probabilistic method for processing and analyzing residuals for the purpose of fault detection. The method incorporates residuals from multiple models using a hybrid dynamic Bayesian network in order to yield a low-cost, complete, diagnostic system. Continuous residuals are used as evidence directly in the network, and this paper discusses options for representing their pr...
متن کاملUsing Probabilistic-Risky Programming Models in Identifying Optimized Pattern of Cultivation under Risk Conditions (Case Study: Shoshtar Region)
Using Telser and Kataoka models of probabilistic-risky mathematical programming, the present research is to determine the optimized pattern of cultivating the agricultural products of Shoshtar region under risky conditions. In order to consider the risk in the mentioned models, time period of agricultural years 1996-1997 till 2004-2005 was taken into account. Results from Telser and Kataoka mod...
متن کاملMultiple Response Optimization with Probabilistic Covariates Using Simultaneous Equation Systems (Quality Engineering Conference Paper)
control of production processes in an industrial environment needs the correct setting of input factors, so that output products with desirable characteristics will be resulted at minimum cost. Moreover, such systems havetomeetset of qualitycharacteristicstosatisfycustomer requirements.Identifyingthemosteffectivefactorsindesignoftheprocesswhichsupportcontinuousandcontinualimprovement isrecently...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2003