Combining Ai, Fdi, and Statistical Hypothesis-testing in a Framework for Diagnosis
نویسندگان
چکیده
A new framework for model based diagnosis is presented using ideas from AI, FDI, and statistical hypothesis testing. The isolation mechanism is based on AI methods, and the main advantage is that multiple faults are handled implicitly. Thus, no special care for isolation of multiple faults is needed. The methods for residual generation, developed in the field of control theory (FDI), can within the framework be fully utilized. Since the framework is also based upon statistical hypothesis testing, it is suitable for problems including noise.
منابع مشابه
A General Framework for Fault Diagnosis Based on Statistical Hypothesis Testing
A framework for fault diagnosis, called structured hypothesis tests, is presented. It has earlier been developed within the area of automatic control, but is in fact very much inspired by the ideas developed in the AI area. The motivation was originally to handle dynamic systems with noise. However, it is here shown that also the noise-free case can be perfectly handled. The system to be diagno...
متن کاملThe Polybox Example using the Framework of Structured Hypothesis Tests
The POLYBOX example is a standard example within the AI-field of modelbased diagnosis research. Here, this example is discussed in the perspective of structured hypothesis tests (SHT). Even though the SHT framework was primarily developed for handling systems with noise, it has here been shown that it can perform very well in also noise-free systems. In the POLYBOX example, it manage to always ...
متن کاملA New Method for Root Detection in Minirhizotron Images: Hypothesis Testing Based on Entropy-Based Geometric Level Set Decision
In this paper a new method is introduced for root detection in minirhizotron images for root investigation. In this method firstly a hypothesis testing framework is defined to separate roots from background and noise. Then the correct roots are extracted by using an entropy-based geometric level set decision function. Performance of the proposed method is evaluated on real captured images in tw...
متن کاملTESTING STATISTICAL HYPOTHESES UNDER FUZZY DATA AND BASED ON A NEW SIGNED DISTANCE
This paper deals with the problem of testing statisticalhypotheses when the available data are fuzzy. In this approach, wefirst obtain a fuzzy test statistic based on fuzzy data, and then,based on a new signed distance between fuzzy numbers, we introducea new decision rule to accept/reject the hypothesis of interest.The proposed approach is investigated for two cases: the casewithout nuisance p...
متن کاملThe Impact of Human Capital on FDI with New Evidence from Bootstrap Panel Granger Causality Analysis
T his study evaluates the causality relationship between human capital and foreign direct investment inflow in twenty-six OIC (the Organization of Islamic Cooperation) countries over the period 1970–2014. We employed the panel Granger non-causality testing approach of Kònya (2006) that is based on seemingly unrelated regression (SUR) systems, and Wald tests with country specific boot...
متن کامل