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Signal discrimination using category-preserving bag-of-words model for condition monitoring

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Abstract

Signal discrimination contributes to the development of machine–machine and human–machine interactive intelligent systems. In this study, a novel framework for signal discrimination was proposed. The proposed framework comprised three phases. In Phase I, a waveform shape-based feature extraction method was used for parameterizing signals. In Phase II, a novel category-preserving bag-of-words (CPBoW) model was proposed. In Phase III, signals were discriminated using a vector space model with term frequency–inverse document frequency. The bag-of-words model generally demonstrated promising performance for signal discrimination. However, the inherent connections among signals of homogeneous categories were considerably lost during signal framing and codebook generation processes. This was because the codebook was simply generated by clustering signal frame samples in the Euclidean space. In the proposed CPBoW model, Taguchi’s quality engineering method was used to develop a category-preserving distance metric for executing a clustering process to generate category-preserving codewords. This preserved category information in the codebook and consequently increased the effectiveness of the discrimination process. The proposed framework was verified through three condition monitoring applications that involved a musical instrument recognition problem, motor bearing fault recognition problem, and heart disease recognition problem. The results indicated the superior performance and effectiveness of the proposed framework.

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Acknowledgements

This study was supported by the Ministry of Science and Technology of Taiwan (Grant No. NSC 102-2410-H-305-062).

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  1. Department of Business Administration, National Taipei University, No. 151, University Rd., San Shia Dist., New Taipei City, 23741, Taiwan

    Yu-Hsiang Hsiao

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Correspondence to Yu-Hsiang Hsiao.

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Hsiao, YH. Signal discrimination using category-preserving bag-of-words model for condition monitoring. Neural Comput & Applic 31, 8615–8630 (2019). https://doi.org/10.1007/s00521-018-3799-0

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