Key Takeaways & Executive Findings
- •• Proposes a novel rolling bearing early fault detection method using a feature clustering fusion degradation index, which integrates multidomain statistical features to improve detection accuracy. • Introduces an improved hierarchical clustering algorithm combined with feature evaluation indices to select a preferred feature subset, reducing redundancy while preserving information richness. • Designs an improved exponential weighted moving average (EWMA) control chart with accurate parameter evaluation and anomaly determination strategy, enhancing sensitivity to small fluctuations from early faults. • Validates the method on public datasets, demonstrating its effectiveness in providing comprehensive degradation indicators and eliminating interference from random anomalies and running-in periods.
Abstract
The research on rolling bearing early fault detection is mainly focused on degradation index extraction and adaptive setting of alarm threshold. The mainstream methods are to extract degradation indicators based on adaptive features and set adaptive alarm thresholds based on the Shewhart control chart. However, the adaptive feature extraction method does not consider the correlation between features, and the Shewhart control chart is not sensitive to small fluctuations caused by early faults. In this study, a rolling bearing early fault detection method based on a feature clustering fusion degradation index is proposed. The multidomain statistical features are extracted to form the initial feature set, and the improved hierarchical clustering algorithm is combined with the feature evaluation index to select features to form a preferred feature subset, to ensure the richness of index information and reduce redundancy. After the construction of the degradation index, to suppress the interference caused by nonstationary and abnormal shocks in early fault detection, the accurate evaluation method and anomaly determination strategy of control chart parameters are studied, and an improved exponential weighted move average control chart is designed to monitor the degradation index. The effectiveness and superiority of the proposed method are verified by public data sets. This research provides a rolling bearing early fault detection method, which can provide comprehensive degradation indicators, eliminate interference caused by random anomalies and running in periods, and achieve an accurate detection of early bearing failures.
1. Introduction
As a key component of large rotating machinery equipment, it is necessary to monitor the performance degradation of rolling bearings effectively and accurately. However, in specific equipment such as aero-engines and large submersible motors, limited by the structural layout and working environment, only one vibration sensor is usually installed near the bearing [1]. Owing to the long transmission path, serious attenuation, and complex frequency components of the collected unidirectional vibration signals, it is very difficult to mine the bearing performance degradation information and identify the weak characteristic signals of early faults, which poses higher requirements for the analysis and fault detection methods of vibration signals. Therefore, it is of significance to carry out accurate evaluation and fault detection of online performance degradation of rolling bearings based on unidirectional vibration signals [2].
In the bearing early fault detection, it is crucial to construct degradation indicators that accurately characterize the bearing condition and quantify its degradation trend. Physical degradation indicators typically consist of time- and frequency-domain statistical features, which are of significance. However, these features are limited in their ability to produce optimal evaluation results, as they are strongly dependent on specific failure and operating conditions. Virtual degradation metrics are obtained by fusing multiple features. Scholars mostly use machine learning and deep learning methods to construct degradation models by adaptively extracted features [3–7]. However, adaptively extracted features are poorly interpretable and lack explicit expressions, and thus it is challenging to understand the features in depth. Compared to adaptively extracted features, statistical features can be used to analyze the signal in multiple domains, have physical meaning, and are easy to understand and use. Meddour et al. [8] used correlation, monotonicity, average increase rate, and robustness to select features, and adaptive-network-based fuzzy inference system (ANFIS) to construct degradation indicators to achieve bearing remaining useful life prediction. Zhou et al. [9] extracted time-domain features and energy entropy, used three features of correlation, monotonicity, an
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Xiangyang Xu, Haotian Wang, Xihui Liang, Chuan Zhao, Ziyuan Ren (2025). Rolling Bearing Early Fault Detection Method Based on Feature Clustering Fusion Degradation Index. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01263-1
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a rolling bearing early fault detection method based on a feature clustering fusion degradation index, which improves detection accuracy by considering feature correlation and using an improved EWMA control chart sensitive to small fluctuations.
How does the proposed method select features?
The method extracts multidomain statistical features and uses an improved hierarchical clustering algorithm combined with feature evaluation indices to select a preferred feature subset, ensuring information richness and reducing redundancy.
What is the role of the improved EWMA control chart?
The improved EWMA control chart is designed to monitor the degradation index, with accurate parameter evaluation and anomaly determination strategy, to suppress interference from nonstationary and abnormal shocks and detect early faults accurately.
How is the method validated?
The effectiveness and superiority of the proposed method are verified using public datasets, demonstrating its ability to provide comprehensive degradation indicators and eliminate interference from random anomalies and running-in periods.
What are the limitations of existing methods addressed by this study?
Existing adaptive feature extraction methods often ignore feature correlation, and Shewhart control charts are not sensitive to small fluctuations from early faults. This study addresses these issues by using feature clustering fusion and an improved EWMA chart.
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