Network Support Data Analysis for Fault Identification Using Machine Learning

2.1. Introduction to Machine Learning

In an attempt to analyse the industrial data/machine produced data research fraternity has done a significant contribution. For industrial data analysis, a strong subject expertise is needed. But, the huge result sets and internal relationships between the workflow is sometimes beyond our subjective knowledge. To overcome this, a more generic framework for processing industrial data is needed. Mr. Mariusz Kamola, in his work (2015) has comes up with a defined set of rules for choosing the most required features for predictive analysis on industrial data. Clearly, the processing framework will differ depending on the use case and type of analysis. So, choice of a suitable Machine Learning algorithm is necessary.

Surya, Nithin, Prasanna, and Venkatesan (2016), gives a brief introduction to machine learning and discusses about various machine learning techniques and pre-processing techniques. The paper discuses about three main topics. They are:

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  Types of machine learning

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  Machine learning techniques

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  Linguistic pre-processing

Types of Machine Learning:

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  Supervised Learning: In this technique, knowledge is referred from training datasets. Example: classification and regression;

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  Unsupervised Learning: In unsupervised learning, there is no training datasets. In this technique, knowledge is inferred from input data that are not tagged. Example: clustering and dimensional Reduction;

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  Reinforcement Learning: A software agent is trained to make suitable decisions to be taken which will be based on the previous experience;

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  Machine Learning: Techniques discussed are, N-Gram and Markov Models, Neural Networks and Decision Tree classifiers;

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  Linguistic Pre-Processing: This step is a preparatory step which prepares the process to take place. This will ensure that the text will be in a form that would be understood by the machine. Here the context of a word is understood.