These are codes of ratio of different quasi-arithmetic means (RQAM) and AWSPT-based sparsity measures for machine condition monitoring.
The AWSPT-based sparsity measures are inspired by the RQAM, and the AWSPT-based sparsity measures can also be reformulated as specific cases of RQAM.
When the RQAM and AWSPT-based sparsity measures used for machine condition monitoring, the new health indices can simultaneously achieve clearly incipient fault detection and monotonic degradation assessment.
Related papers are:
 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.
 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.
 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.