Knowledge-Driven Board-Level Functional Fault Diagnosis by Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu

By Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu

This ebook offers a finished set of characterization, prediction, optimization, review, and evolution ideas for a prognosis process for fault isolation in huge digital platforms. Readers with a historical past in electronics layout or approach engineering can use this ebook as a connection with derive insightful wisdom from info research and use this data as assistance for designing reasoning-based prognosis platforms. furthermore, readers with a historical past in records or facts analytics can use this booklet as a realistic case research for adapting information mining and desktop studying concepts to digital process layout and prognosis. This booklet identifies the major demanding situations in reasoning-based, board-level analysis process layout and provides the options and corresponding effects that experience emerged from modern learn during this area. It covers issues starting from hugely actual fault isolation, adaptive fault isolation, diagnosis-system robustness overview, to procedure functionality research and evaluate, wisdom discovery and data move. With its emphasis at the above subject matters, the e-book presents an in-depth and huge view of reasoning-based fault prognosis procedure design.
• Explains and applies optimized recommendations from the machine-learning area to resolve the fault analysis challenge within the realm of digital procedure layout and manufacturing;• Demonstrates innovations in accordance with commercial info and suggestions from an exact production line;• Discusses functional difficulties, together with prognosis accuracy, prognosis time expense, assessment of prognosis method, dealing with of lacking syndromes in prognosis, and want for quick diagnosis-system development.

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For example, for Board 2, each board instance is iteratively selected to be the test case. Classification models are based on the remaining 155 training cases. In LOO estimation, the total number of cases in the testing set is same as the total number of available successfully repaired boards. To ensure real-time diagnosis and repair, we assume that we are allowed at most three attempts to replace the potential failing components. Success ratio (SR) is the ratio of the number of correctly diagnosed cases to the total number of cases in the testing set.

Here, we merge the syndromes and the known root causes into one matrix A = [B |C ], where the left (B ) side refers to syndromes, while the right side (C ) refers to the corresponding fault classes. This matrix represents the training information for the SVM. 5). 00 by solving Eqs. 7). 00) Next, suppose a new failing board is received and it has the syndrome [1 1 0], which corresponds to the first row (case) of A in Eq. 8). The function y is evaluated using Eq. 00) is positive, y = 1. Thus the root cause for this failing board is determined to be A.

Two basic network architectures are feedforward and recurrent. In the feedforward architecture, there is no feedback between layers as the network 46 3 Diagnosis Using Multiple Classifiers and Majority-Weighted Voting (WMV) Hidden layer Input1 Input layer Output layer Weight1 Inputi Weight i Neurons Output f Weighted connections Fig. 1 A simple feedforward neural network and the computation in a neuron [2] shown in Fig. 1. In the recurrent architecture, there is feedback between layers, thus these networks can remember prior inputs.

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