Signal discrimination using category-preserving bag-of-words model for condition monitoring
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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.
KeywordsBag-of-words model Category-preserving Signal discrimination Condition monitoring Taguchi’s quality engineering
This study was supported by the Ministry of Science and Technology of Taiwan (Grant No. NSC 102-2410-H-305-062).
Compliance with ethical standards
Conflict of interest
The author declares that he has no conflict of interest.
- 1.Mukhopadhyay S, Biswas S, Roy AB, Dey N (2012) Wavelet based QRS complex detection of ECG signal. Int J Eng Res Appl 2(3):2361–2365Google Scholar
- 2.Bhalke DG, Rao CR, Bormane DS (2014) Hybridization of fractional fourier transform and acoustic features for musical instrument recognition. Int J Signal Process Image Process Pattern Recognit 7(1):275–282Google Scholar
- 15.Zokaee S, Faez K (2012) Human identification based on ECG and palmprint. Int J Electr Comput Eng 2(2):261–266Google Scholar
- 16.Schmitt M, Ringeval F, Schuller BW (2016) At the border of acoustics and linguistics: bag-of-audio-words for the recognition of emotions in speech. In: Interspeech, pp 495–499Google Scholar
- 17.Proakis JG, Manolakis DG (2006) Digital signal processing: principles, algorithms, and applications. Prentice Hall, Englewood CliffsGoogle Scholar
- 28.Beagum S, Ashour A, Dey N (2016) Bag-of features in microscopic images classification. In: Dey N, Ashour A (eds) Classification and clustering in biomedical signal processing. IGI Global, Hershey, pp 1–22Google Scholar
- 30.Lin J, Li Y (2009) Finding structural similarity in time series data using bag-of-patterns representation. In: International conference on scientific and statistical database management, pp 461–477Google Scholar
- 35.Singhal A (2001) Modern information retrieval: a brief overview. Bull IEEE Comput Soc Tech Comm Data Eng 24(4):35–43Google Scholar
- 37.Burden RL, Faires JD (2000) Numerical analysis, 7th Bk&Cdr ed. Brooks/Cole, BostonGoogle Scholar
- 39.Huang A (2008) Similarity measures for text document clustering. In: Proceedings of the sixth New Zealand computer science research student conference, pp 49–56Google Scholar
- 44.Slaney M (1998) Auditory toolbox. Software retrieved August 13, 2017, from http://cobweb.ecn.purdue.edu/~malcolm/interval/1998-010/
- 45.Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines, 2001. Software Retrieved January 21, 2017, from http://www.csie.ntu.edu.tw/~cjlin/libsvm
- 46.University of Iowa Musical Instrument Sample Database. http://theremin.music.uiowa.edu/index.html
- 47.Loparo KA (2013) Bearing Data Center, Case Western Reserve University. http://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website. Accessed 6 Mar 2016
- 48.Zhang S, Li W (2014) Bearing condition recognition and degradation assessment under varying running conditions using NPE and SOM. Math Problems Eng Vol. 2014, Article ID 781583Google Scholar