His research interests include machinery condition monitoring, fault diagnosis, and prognostics. Representative research works are ratio of different quasi-arithmetic means, generalized Gini indices, impulsive mode decomposition, optimized weights spectrum, and difference mode decomposition. He published over 10 first-authored SCI papers in prestigious journals such as Mechanical Systems and Signal Processing and IEEE/ASME Transactions. Email: bingchanghou@cqu.edu.cn.

Google Scholar: https://scholar.google.com/citations?user=O_tweiYAAAAJ&hl=en&oi=sra

ResearchGate: https://www.researchgate.net/profile/Bingchang-Hou-2

Five representative works:

1. Hou B, Xie M, Yan H, Wang D*. Impulsive mode decomposition[J]. Mechanical Systems and Signal Processing, 2024, 211:111227.

2. Hou B, Wang D*, Peng Z, Tsui K-L. Adaptive fault components extraction by using an optimized weights spectrum based index for machinery fault diagnosis[J]. IEEE Transactions on Industrial Electronics, 2023: 1–10. 

3. Hou B, Wang D*, Kong J, Liu J, Peng Z, Tsui K-L. Understanding importance of positive and negative signs of optimized weights used in the sum of weighted normalized Fourier spectrum/envelope spectrum for machine condition monitoring[J]. Mechanical Systems and Signal Processing, 2022, 174: 109094.

4. Hou B, Wang D*, Yan T, Wang Y, Peng Z, Tsui K-L. Gini Indices II and III: Two new sparsity measures and their applications to machine condition monitoring[J]. IEEE/ASME Transactions on Mechatronics, 2022, 27(3): 1211–1222.

5. Hou B, Wang D*, Xia T, Wang Y, Zhao Y, Tsui K. Investigations on quasi-arithmetic means for machine condition monitoring[J]. Mechanical Systems and Signal Processing, 2021, 151: 107451.

Book:

1. Dong Wang, Bingchang Hou, Sparsity measures and their signal processing applications for machine condition monitoring, Book, Elsevier, 2025, ISBN 978-0-443-33486-3.