Xu Aiqiang

A New Method of Testability Prediction on Model and Probability Analysis

For improving the accuracy of fault-test dependency analysis and test prediction, we introduce two new additional properties into multi-signal model and give a new method of testability prediction on refined multi-signal model. The paper illustrates the method and procedure of testability prediction in two ways. The new method can improve the accuracy of estimating FDR and FIR evidently.

Research on modeling and application of test signal

Aimed at the problems that the model of test signal was difficult to set and the measurement was hard to achieve in signal-oriented ATS, the way modeling test signal based on STD were introduced and the model of three-phase AC signal which had current limit attribute was built. Furthermore, the principle of intrinsic measurement and generic measurement which would be both used in the later test example was analyzed. At last not only the correctness of the three-phase AC signal model was verified, but also the application of test signals in signal-oriented ATS was showed by the test example.

Research on signal-oriented ATS software technology based on ATML

The ATML (Automatic Test Markup Language) family of standards defines an exchange medium for sharing information between components of ATS, using XML (eXtensible Markup Language). In order to develop signal-oriented ATS software based on ATML, there is a requirement of researching on the software technology. First the development and components of ATML were introduced, and the signal-oriented software platform architecture was discussed. Second the realization of run time system and instrument driver were analyzed. At last the simulation of using TPL and its corresponding XML expression to measure the attributes of square wave was done. Doubtless, ATML is rapidly gaining ground in ATS software.

Application of Model-based and Data-driven Techniques in Fault Diagnosis

Model-based fault diagnosis, using statistical techniques, residual generation (by analytical redundancy), and parameter estimation, has been an active area of research for the past four decades. However, these techniques are developed in isolation and generally a single technique can not address the diagnostic problems in complex systems. In this paper, we investigate a hybrid approach, which combines different techniques to obtain better diagnostic performance than the use of a single technique alone, and demonstrate it on a fault detection and diagnosis (FDD) system.