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A Methodological Framework for the Prediction of Quality and Remaining Useful Life of Industrial Components and Systems
, Ph.D. Dissertation Sharif University of Technology ; Behzad, Mehdi (Supervisor) ; Baraldi, Piero (Supervisor) ; Zio, Enrico (Supervisor)
Abstract
Recent advancements in sensors and network technologies have led to a significant increase in the availability of data, collected during the entire life cycle of industrial components, from the production phase to field operation. This PhD thesis considers time series of measurements of different signal types, such as vibration, temperature, and pressure and other signals, to enhance the reliability of industrial components. Specifically, the research considers the two most critical phases of the component life-cycle: the early-life phase, during which failures are typically due to manufacturing defects caused by low production quality, and the wear-out phase, during which failures are due...
A novel methodology based on long short-term memory stacked autoencoders for unsupervised detection of abnormal working conditions in semiconductor manufacturing systems
, Article Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability ; vol. 239(5) , 2024 , pages 1115-1133 ; 1748006X (ISSN) ; Ahmed, I ; Baraldi, P ; Zio, E ; Behzad, M ; Lewitschnig, H ; Sharif University of Technology
IDEAS
2024
Abstract
The detection of the occurrence of working conditions in semiconductor manufacturing systems is fundamental to minimize yield losses and enhance production quality. The main challenges are the lack of data labeled with the machine state (normal/abnormal) and the nonlinear time evolution of the monitored signals. This work develops a novel methodology for the detection of abnormal conditions that, differently from the existing approaches, do not require the availability of labeled data. It consists of: (a) an approach based on k-fold cross-validation for automatically building a training set which does not contain data collected during abnormal conditions; (b) a signal reconstruction model...