By Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu
This e-book presents a complete set of characterization, prediction, optimization, review, and evolution options for a analysis procedure for fault isolation in huge digital platforms. Readers with a history in electronics layout or procedure engineering can use this booklet as a connection with derive insightful wisdom from facts research and use this data as advice for designing reasoning-based analysis platforms. furthermore, readers with a historical past in information or info analytics can use this e-book as a realistic case examine for adapting facts mining and desktop studying suggestions to digital process layout and analysis. This booklet identifies the most important demanding situations in reasoning-based, board-level analysis method layout and provides the suggestions and corresponding effects that experience emerged from modern examine during this area. It covers issues starting from hugely exact fault isolation, adaptive fault isolation, diagnosis-system robustness evaluate, to process functionality research and review, wisdom discovery and data move. With its emphasis at the above themes, the ebook presents an in-depth and extensive view of reasoning-based fault prognosis method design.
• Explains and applies optimized ideas from the machine-learning area to unravel the fault prognosis challenge within the realm of digital method layout and manufacturing;• Demonstrates suggestions in keeping with commercial facts and suggestions from an exact production line;• Discusses sensible difficulties, together with prognosis accuracy, analysis time expense, overview of prognosis process, dealing with of lacking syndromes in prognosis, and wish for speedy diagnosis-system development.
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11 for Board 2. When we increase the size of the training set, the success ratios of up to three attempts for both Board 1 and Board 2 increase. This observation supports the positive correlation found in [1] between the number of failing boards available for training and the diagnosis accuracy on new boards. 7 %, respectively, when 1300 training cases are used in the iMK-SVM model for Board 2. The training time results are shown in Fig. 12 for Board 1 and in Fig. 13 for Board 2. The training time of incremental learning MK-SVMs in each epoch is much smaller than that for nonincremental learning MK-SVMs.
IEEE international conference on systems, man, and cybernetics, vol 2, pp 1874–1879 References 21 54. net/ 55. Lin Y-T (2005) Economic designs for manufacturing system test and field maintenance. In: Proceedings Southeastern symposium on system theory, pp 40–44 56. org/wiki/Xbox_360_ technical_problems 57. html 58. html 59. org/node/2151 Chapter 2 Diagnosis Using Support Vector Machines (SVM) Diagnosis of functional failures at the board level is critical for improving product yield and reducing manufacturing cost.
10 Success ratio of using incremental multi-kernel SVMs for Board 1 SVM model success ratio (percentage) Incremental learning can also be applied to MK-SVM training. The success ratio results are shown in Fig. 10 for Board 1 and in Fig. 11 for Board 2. When we increase the size of the training set, the success ratios of up to three attempts for both Board 1 and Board 2 increase. This observation supports the positive correlation found in [1] between the number of failing boards available for training and the diagnosis accuracy on new boards.