Discovery of Corrosion Patterns using Symbolic Time Series Representation and N-gram Model

There are many factors that can contribute to corrosion in the pipeline. Therefore, it is important for decision makers to analyze and identify the main factor of corrosion in order to take appropriate actions. The factor of corrosion can be analyzed using data mining based on historical datasets collected from monitoring sensors. The purpose of this study is to analyze the trends of corroding agents for pipeline corrosion based on symbolic representation of time series corrosion dataset using Symbolic Aggregation Approximation (SAX). The paper presents the analysis and evaluation of the patterns using N-gram model. Text mining using N-gram model is proposed to mine trend changes from corrosion time series dataset that are transformed as symbolic representation. N-gram was applied for the analysis in order to find significant symbolic patterns that are represented as text. Pattern analysis is performed and the results are discussed according to each environmental factor of pipeline corrosion.

@article{Taib2018,
  title     = {Discovery of Corrosion Patterns using Symbolic Time Series Representation and N-gram Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {12},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Shakirah Mohd Taib and Zahiah Akhma Mohd Zabidi and Izzatdin Abdul Aziz and Farahida Hanim Mousor and Azuraliza Abu Bakar and Ainul Akmar Mokhtar},
  doi       = {10.14569/IJACSA.2018.091278},
  url       = {https://doi.org/10.14569/IJACSA.2018.091278}
}