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Title: Industry 4.0 enabled electrochemical process lines and data analysis for quality control
Authors: Herell, Vincentius Dennis
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Herell, V. D. (2022). Industry 4.0 enabled electrochemical process lines and data analysis for quality control. Final Year Project (FYP), Nanyang Technological University, Singapore.
Abstract: Online machine learning or stream learning is a machine learning algorithm that updates its model with each new data instead of the standard batch learning that need the whole dataset at the time of training. One particular trait of the stream learning algorithm is its robustness against concept drift, which is the change in the input and output relationship within the dataset. In this paper, multiple stream learning algorithms are compared against batch learning algorithms across various datasets. Among these datasets is the parameter optimization of black nickel coating process to test the applicability of stream learning algorithm in electrochemical process. The result of the comparison has shown that batch learning performs better on dataset with minimal concept drifts, but the stream learning algorithm outperform it when the dataset contains significant concept drift. When applied on the black nickel coating dataset, the stream learning algorithm does not perform well due to the small amount of data points. The number of data significantly affect the performance of stream learning algorithm, thus when the data collection process is slow and the expected collected data does not amount to much, a batch learning algorithm is preferred.
Schools: School of Electrical and Electronic Engineering 
Organisations: Singapore Institute of Manufacturing Technology (SIMTech)
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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