Synthesis of multiple fault oriented test groups from single fault test sets

Abstract

A novel approach for testing and diagnosing of multiple faults is discussed. A definition of a test group is introdueed to eope with the problem of fault masking. The eonditions are introduced to prove that a test group is sufficient to avoid fault masking. A method is presented for generating test groups regarding fault masking. Unlike the traditional test approaches, we do not target the faults as test objectives. The goal is to verify the correctness of a part of the circuit. The whole test sequence is presented as a set of test groups where each group has the goal to identify the correctness of a selected part of a circuit. The method facilitates fault diagnosis in the presence of multiple faults. Experimental research investigates the feasibility of the method and shows a way for tradeoffs between the test cost and robustness of the test regarding multiple faults.

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

“…However, an in-depth review exclusively on the state-of-the-art ML-based fault detec- Recently, Hu et al [30] and Tran et al [19] presented an overview of different fault diagnosis algorithms for the LIBs. The concept of dependable graph-based fault tolerance and diagnostics has been explained by Ubar et al in references [31][32][33]. Reza [34] conducted a review study focusing on the application of ML approaches in the BMS of LIBs.…”