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Intelligent Condition Based Monitoring pp 121–173Cite as

Feature Extraction

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Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 256))

Abstract

As detailed in Chap. 3, pre-processing of data improves the quality of data. Now, this chapter proceeds towards the next step of fault diagnosis framework where key characteristics of the data are found. For this purpose, data is analyzed in different domains and thus we obtain a new set of data which we call that good features are obtained. This chapter details how features can be extracted out from the pre-processed data. When the input data to an algorithm is too large to be processed and it is suspected to be redundant (much data but not much information), then the input data will be transformed into a reduced set of features (also named features vector). Transforming the input data into the set of features is called feature extraction. In order to extract useful information from the captured data, it is necessary to represent the data in a suitable form. Generally, for feature extraction purpose, three forms of signal representation as below are utilized.

  • Time domain

  • Frequency domain

  • Time–frequency/wavelet domain

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Inter-disciplinary Program in Cognitive Science, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Nishchal K. Verma

  2. Boeing Research and Technology, Saint Louis, MO, USA

    Al Salour

Authors
  1. Nishchal K. Verma
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  2. Al Salour
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Correspondence to Nishchal K. Verma .

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Verma, N.K., Salour, A. (2020). Feature Extraction. In: Intelligent Condition Based Monitoring. Studies in Systems, Decision and Control, vol 256. Springer, Singapore. https://doi.org/10.1007/978-981-15-0512-6_4

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  • DOI: https://doi.org/10.1007/978-981-15-0512-6_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0511-9

  • Online ISBN: 978-981-15-0512-6

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