RESEARCH PAPER
A Time-Weighted Fusion Waveform Factor Based Health Indicator for Condition Monitoring of Rotating Machinery
1
Guangdong Provincial Key Laboratory of Petrochemical Equipment and Fault Diagnosis,
Guangdong University of Petrochemical Technology, Maoming 525000, China, China
Submission date: 2025-06-28
Final revision date: 2025-08-26
Acceptance date: 2025-11-16
Online publication date: 2025-12-15
Publication date: 2025-12-15
Corresponding author
Liu Xuebin
Guangdong Provincial Key Laboratory of Petrochemical Equipment and Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming 525000, China, China
Guangdong Provincial Key Laboratory of Petrochemical Equipment and Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming 525000, China, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2026;28(2):214295
HIGHLIGHTS
- A new health indicator is proposed for monitoring rotating machinery.
- An optimization algorithm is used to tune key parameters and analyze sensitivity.
- Tested on a public bearing dataset to detect faults earlier than other methods.
- Further tested on industrial equipment data to demonstrate the method’s effectiveness.
KEYWORDS
health indicatoroptimization algorithmcondition monitoring of rotating machinerytime-weighted fusion waveform factor
TOPICS
ABSTRACT
In rotating machinery condition monitoring, traditional health indicators (HI), including quantitative and dimensionless forms, are widely used for health assessment. However, such indicators often respond slowly to early faults or display decreasing and oscillatory trends after fault onset, causing delayed monitoring and economic losses. To address these issues, this study proposes a novel HI termed the time-weighted fusion waveform factor (T2WF). Derived from the conventional waveform factor (WF), T2WF introduces the concept of periods, computes dimensionless values in proximal and distal periods, and combines them through a weighting parameter. The Northern Goshawk Optimization Algorithm (NGOA) is employed to optimize the key parameters. Validation is conducted on the public XJTU-SY dataset and on rotating machinery datasets from two petrochemical enterprises. Experimental results show that T2WF achieves earlier fault detection and superior diagnostic performance compared with traditional HI, international standards, and deep learning methods.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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