Xihui Liang, University of Manitoba

Research

Machine learning Rotating machinery Signal processing Condition monitoring Dynamic modeling Fault diagnostics and prognositics Health management bearings

Latest papers

An Equivalent Structural Stress-Based Frequency-Domain Fatigue Assessment Approach for Welded Structures under Random Loading.
Materials (Basel, Switzerland) · 2023 · senior author
Coordinated control for path-following of an autonomous four in-wheel motor drive electric vehicle.
Proceedings of the Institution of Mechanical Engineers. Part C. Journal of mechanical engineering science · 2022 · senior author
A new deep domain adaptation method with joint adversarial training for online detection of bearing early fault.
ISA transactions · 2022 · senior author

All 5 publications

Latest funding

Advanced Methods for Bearing Condition Monitoring Under Varying Working Conditions
NSERC · 2024 · Principal investigator
$30,000
Development of Advanced Condition Monitoring Techniques for Gearboxes and Bearings
NSERC · 2019 · Principal investigator
$12,500
Development of Advanced Condition Monitoring Techniques for Gearboxes and Bearings
NSERC · 2019 · Principal investigator
$224,000

All 3 grants and awards

5 publications.

01

An Equivalent Structural Stress-Based Frequency-Domain Fatigue Assessment Approach for Welded Structures under Random Loading.

Kalu U, Liang X

Materials (Basel, Switzerland)2023Senior author

DOI: 10.3390/ma16237420 PMID: 38068164

02

Coordinated control for path-following of an autonomous four in-wheel motor drive electric vehicle.

Barari A, Saraygord Afshari S, Liang X

Proceedings of the Institution of Mechanical Engineers. Part C. Journal of mechanical engineering science2022Senior author

DOI: 10.1177/09544062211064797 PMID: 35719241

03

A new deep domain adaptation method with joint adversarial training for online detection of bearing early fault.

Mao W, Ding L, Liu Y, Afshari SS, Liang X

ISA transactions2022Senior author

DOI: 10.1016/j.isatra.2021.04.026 PMID: 33958191

04

Information Theory and Its Application in Machine Condition Monitoring.

Li Y, Gu F, Liang X

Entropy (Basel, Switzerland)2022Senior author

DOI: 10.3390/e24020206 PMID: 35205501

05

Health condition identification of planetary gearboxes based on variational mode decomposition and generalized composite multi-scale symbolic dynamic entropy.

Li Y, Li G, Wei Y, Liu B, Liang X

ISA transactions2018Senior author

DOI: 10.1016/j.isatra.2018.06.001 PMID: 29903426

Advanced Methods for Bearing Condition Monitoring Under Varying Working Conditions

Principal investigatorNSERC2024Alliance Grants

Principal investigators: Liang, Xihui X

Keywords: bearings; machine learning; optimization; predictive maintenance; rotating machinery; signal processing

$30,000

Development of Advanced Condition Monitoring Techniques for Gearboxes and Bearings

Principal investigatorNSERC2019Discovery Launch Supplement

Principal investigators: Liang, Xihui

$12,500

Development of Advanced Condition Monitoring Techniques for Gearboxes and Bearings

Principal investigatorNSERC2019Discovery Grants Program - Individual

Principal investigators: Liang, Xihui

Keywords: condition monitoring; fault diagnostics and prognositics; health management; signal processing; dynamic modeling; rotating machinery; machine learning

$224,000

From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.

Write to this professor

A short, specific email works best. This draft uses one of their recent papers; replace the parts in brackets with your own details before sending.

Applying for

Paper to mentionMention their NSERC grant from 2024

Subject: Prospective PhD student: Machine learning Dear Professor Liang, My name is [your name], and I am a [your current degree or role] at [your institution]. I am applying for a PhD position starting in [term and year]. I read your paper "An Equivalent Structural Stress-Based Frequency-Domain Fatigue Assessment Approach for Welded Structures under Random Loading." (2023), and it connects closely to my own work on [your research topic]. [One or two sentences on what you did and what you learned.] I also saw your NSERC-funded project "Advanced Methods for Bearing Condition Monitoring Under Varying Working Conditions" (2024), and I would be glad to contribute to work in that direction. Are you accepting new PhD students for [term and year]? I have attached my CV and transcripts, and I would be glad to talk at a time that suits you. Thank you for your time. Kind regards, [your name]