Short review of dimensionality reduction methods for failure detection

Agnieszka Pocha,

Krzysztof Misztal,

Paweł Morkisz


Size of the data is often a challenge in real-life applications. Especially when working with time series data, when next sample is produced every few milliseconds and can include measurement from hundreds of sensors, one has to take the dimensionality of the data into consideration. In this work, we compare various dimensionality reduction methods for time series data and check their performance on failure detection task. We work on sensory data coming from existing machines.

Słowa kluczowe: dimensionality reduction, time series, failure detection

[1] Grand View Research, I., Industrial internet of things (iiot) market analysis by component (solution, services, platform), by end-use (manufacturing, energy & power, oil & gas, healthcare, logistics & transport, agriculture), and segment forecasts, 2018–2025. Market Research Report, 2017.

[2] Van Der Maaten L., Postma E., Van den Herik J., Dimensionality reduction: a comparative. J Mach Learn Res, 2009, 10, pp. 66–71.

[3] Jolliffe I., Principal component analysis. Wiley Online Library, 2002.

[4] Howland P., Park H., Generalizing discriminant analysis using the generalized singular value decomposition. IEEE transactions on pattern analysis and machine intelligence, 2004, 26(8), pp. 995–1006.

[5] Hyv¨arinen A., Karhunen J., Oja E., Independent component analysis. vol. 46. John Wiley & Sons, 2004.

[6] Blei D.M., Ng A.Y., Jordan M.I., Latent dirichlet allocation. Journal of machine Learning research, 2003, 3 (Jan), pp. 993–1022.

[7] Landauer T.K., Latent semantic analysis. Wiley Online Library, 2006.

[8] Chua L., Deng A.C., Canonical piecewise-linear representation. IEEE Transactions on Circuits and Systems, 1988, 35 (1), pp. 101–111.

[9] Yang J., Honavar V., Feature subset selection using a genetic algorithm. In: Feature extraction, construction and selection. Springer 1998 pp. 117–136.

[10] Monahan A.H., Nonlinear principal component analysis by neural networks: theory and application to the lorenz system. Journal of Climate, 2000, 13 (4), pp. 821–835.

[11] Sch¨olkopf B., Smola A., M¨uller, K.R., Kernel principal component analysis. In: International Conference on Artificial Neural Networks, Springer, 1997, pp. 583–588.

[12] Mika S., Ratsch G., Weston J., Scholkopf B., Mullers K.R., Fisher discriminant analysis with kernels. In: Neural networks for signal processing IX, 1999. Proceedings of the 1999 IEEE signal processing society workshop., IEEE, 1999, pp. 41–48.

[13] Venna, J., Kaski, S., Local multidimensional scaling. Neural Networks, 2006, 19 (6), pp. 889–899.

[14] Verikas, A., Bacauskiene, M., Feature selection with neural networks. Pattern Recognition Letters, 2002, 23 (11), pp. 1323–1335.

[15] Wu, Y.L., Tang, C.Y., Hor, M.K., Wu, P.F., Feature selection using genetic algorithm and cluster validation. Expert Systems with Applications, 2011, 38 (3), pp. 2727–2732.

[16] Hochreiter, S., Schmidhuber, J., Long short-term memory. Neural computation, 1997, 9 (8), pp. 1735–1780.

[17] Cho K., Van Merri¨enboer B., Gulcehre C., Bahdanau D., Bougares F., Schwenk H., Bengio Y., Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014.

[18] Cochran W.G., Cox G.M., Experimental designs