This chapter presents the existing techniques for data acquisition and data preprocessing in general; while the adoption of selected artificial intelligence models for solving the problems in different industries, such as thin-film-transistor liquid-crystal display, solar cell, semiconductor, automotive, aerospace, chemical, and bottle industries. The analog-to-digital converter connects to various sensors installed on the equipment side to convert analog sensing signals into digital signals via its analog input/output ports. The chapter presents various sensing techniques and the issues of sensor selection and installation. The goal of data preprocessing is to eliminate the noise imbedded in the signal and extract key-feature related information. Data preprocessing consists of three steps: segmentation, cleaning, and feature extraction. The chapter demonstrates four practical examples using real-world data to validate techniques of data acquisition and data preprocessing. These include: detrending of the thermal effect in strain gauge data, automated segmentation of signal data, tool state diagnosis, and tool diagnosis using loading data.
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