Codes of the sparsity measures and RQAM features

Versión 1.0.4 (77,9 MB) por Bingchang Hou

Codes of many sparsity measures, RQAM features, AWSPT, and AWSPT-based sparsity measures, with the XJTU bearing 2-3 dataset as example.

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Actualizado 9 oct 2023

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Code description: These are codes of sparsity measures, adaptive weighted signal preprocessing technique, adaptive sparsity measures, and RQAM features. An illustration based on XJTU bearing dataset 2-3 is used as an implementation example. I believe one can feel easy when using our codes.

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Related papers are as follows. Work [1] is the theoretical investigations on sparsity measures and the new framework RQAM is given in work [1]. Work [2] is a related new method that enable sparsity measures to simultaneously achieve clearly incipient fault detection and monotonic degradation assessment. Works [3-4] are new sparsity measures generated by using RQAM. I believe one can easily generated new sparsity measures by using the RQAM.

It is worthy pointed out that there are already published new journal papers whose technical routes/basis are highly related to the works [1] and [2]. Some new sparsity measures and RUL prediction methods are developed in these published papers. Therefore, you are welcome to download the codes and conduct the related research. If you think the codes are helpful for your research, please make the relevent citations.

[1] B. Hou, D. Wang, T. Xia, Y. Wang, Y. Zhao, K. Tsui, Investigations on quasi-arithmetic means for machine condition monitoring, Mech. Syst. Signal Process. 151 (2021) 107451. https://doi.org/10.1016/j.ymssp.2020.107451

[2] B. Hou, D. Wang, Y. Wang, T. Yan, Z. Peng, K.-L. Tsui, Adaptive Weighted Signal Preprocessing Technique for Machine Health Monitoring, IEEE Trans. Instrum. Meas. 70 (2021) 1–11. https://doi.org/10.1109/TIM.2020.3033471

[3] B. Hou, D. Wang, T. Yan, Y. Wang, Z. Peng, K.-L. Tsui, Gini Indices Ⅱ and Ⅲ: Two New Sparsity Measures and Their Applications to Machine Condition Monitoring, IEEE/ASME Trans. Mechatronics. 4435 (2021) 1–1. https://doi.org/10.1109/TMECH.2021.3100532

[4] B. Hou, D. Wang, T. Xia, L. Xi, Z. Peng, K. Tsui, Generalized Gini indices: Complementary sparsity measures to Box-Cox sparsity measures for machine condition monitoring, Mech. Syst. Signal Process. 169 (2022) 108751. https://doi.org/10.1016/j.ymssp.2021.108751

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Bingchang Hou (2025). Codes of the sparsity measures and RQAM features (https://www.mathworks.com/matlabcentral/fileexchange/102574-codes-of-the-sparsity-measures-and-rqam-features), MATLAB Central File Exchange. Recuperado 7 marzo, 2025.

Hou, Bingchang, et al. “Investigations on Quasi-Arithmetic Means for Machine Condition Monitoring.” Mechanical Systems and Signal Processing, vol. 151, Elsevier BV, Apr. 2021, p. 107451, doi:10.1016/j.ymssp.2020.107451.

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Hou, Bingchang, et al. “Adaptive Weighted Signal Preprocessing Technique for Machine Health Monitoring.” IEEE Transactions on Instrumentation and Measurement, vol. 70, Institute of Electrical and Electronics Engineers (IEEE), 2021, pp. 1–11, doi:10.1109/tim.2020.3033471.

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Released Codes about sparsity measuresV2

Versión	Publicado	Notas de la versión
1.0.4	9 oct 2023	AWSPT function is revised, because a square operation for variance calculation in previou code is lacking.	Descargar
1.0.3	24 jul 2023	The codes are refined, each sparsity measure has a specific matlab function, which can be easier to use. Moreover, a document is provided for reference.	Descargar
1.0.2	20 jun 2023	Descriptions are updated.	Descargar
1.0.1	22 mar 2022	A related paper is added to the description.	Descargar
1.0.0	25 nov 2021		Descargar

MLA	Hou, Bingchang, et al. “Investigations on Quasi-Arithmetic Means for Machine Condition Monitoring.” Mechanical Systems and Signal Processing, vol. 151, Elsevier BV, Apr. 2021, p. 107451, doi:10.1016/j.ymssp.2020.107451.
APA	Hou, B., Wang, D., Xia, T., Wang, Y., Zhao, Y., & Tsui, K.-L. (2021). Investigations on quasi-arithmetic means for machine condition monitoring. Mechanical Systems and Signal Processing, 151, 107451. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.ymssp.2020.107451
BibTeX	@article{Hou_2021, doi = {10.1016/j.ymssp.2020.107451}, url = {https://doi.org/10.1016%2Fj.ymssp.2020.107451}, year = 2021, month = {apr}, publisher = {Elsevier {BV}}, volume = {151}, pages = {107451}, author = {Bingchang Hou and Dong Wang and Tangbin Xia and Yi Wang and Yang Zhao and Kwok-Leung Tsui}, title = {Investigations on quasi-arithmetic means for machine condition monitoring}, journal = {Mechanical Systems and Signal Processing} }

MLA	Hou, Bingchang, et al. “Adaptive Weighted Signal Preprocessing Technique for Machine Health Monitoring.” IEEE Transactions on Instrumentation and Measurement, vol. 70, Institute of Electrical and Electronics Engineers (IEEE), 2021, pp. 1–11, doi:10.1109/tim.2020.3033471.
APA	Hou, B., Wang, D., Wang, Y., Yan, T., Peng, Z., & Tsui, K.-L. (2021). Adaptive Weighted Signal Preprocessing Technique for Machine Health Monitoring. IEEE Transactions on Instrumentation and Measurement, 70, 1–11. Institute of Electrical and Electronics Engineers (IEEE). Retrieved from https://doi.org/10.1109%2Ftim.2020.3033471
BibTeX	@article{Hou_2021, doi = {10.1109/tim.2020.3033471}, url = {https://doi.org/10.1109%2Ftim.2020.3033471}, year = 2021, publisher = {Institute of Electrical and Electronics Engineers ({IEEE})}, volume = {70}, pages = {1--11}, author = {Bingchang Hou and Dong Wang and Yi Wang and Tongtong Yan and Zhike Peng and Kwok-Leung Tsui}, title = {Adaptive Weighted Signal Preprocessing Technique for Machine Health Monitoring}, journal = {{IEEE} Transactions on Instrumentation and Measurement} }