Modeling the Relationship between Vibration Features and Condition Parameters Using Relevance Vector Machines for Health Monitoring of Rolling Element Bearings under Varying Operation Conditions

Hu, Lei; Hu, Niao Qing; Fan, Bin; Gu, Fengshou; Zhang, Xiang Yi

doi:10.1155/2015/123730

Cited by 13 publications

(10 citation statements)

References 17 publications

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“…The following Refs. [165,166] used the statistical features extracted from time-domain signals to realize the RUL prediction. In addition, the degradation information in the frequency domain signal, such as the power density, the average amplitude, and so forth, could also be used as a single HI to predict the RUL of gears, thrust bearings, and pumps [167][168][169].…”

Section: Single Himentioning

confidence: 99%

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

et al. 2022

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Prognostics and health management (PHM) is an essential means to optimize resource allocation and improve the intelligent operation and maintenance (O&M) efficiency of marine systems and equipment (MSAE). PHM generally consists of four technical processes, namely health condition motoring (HCM), fault diagnosis (FD), health prognosis (HP), and maintenance decision (MD). In recent years, a large amount of research has been implemented in each process. However, there is not any systematic review that covers the technical framework comprehensively. This article presents a review of the framework of PHM in the marine field to fill the gap. First, the essential HCM methods, which are widely observed in the academic literature, are introduced systematically. Then, the commonly used FD approaches and their applications in MSAE are summarized, and the implementation process of intelligent methods is systematically introduced. After that, the technologies of HP have been reviewed, including the construction of health indicator (HI), health stage (HS) division, and popular remaining useful life (RUL) prediction approaches. Afterwards, the evolution of maintenance strategy in the maritime field is reviewed. Finally, the challenges of implementing PHM for intelligent ships are put forward.

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Section: Single Himentioning

confidence: 99%

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

et al. 2022

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“…We, thus, constructed a health indicator based on the RMS of the measurements [10,42,43]. A health indicator based on the RMS of measurements was often employed in the literature [2,42], for example for the health monitoring of gearboxes [44], turbine-cutting tools [43], and rolling element bearings [45]. The RMS of sensor s of CU i during flight f is:…”

Section: Health Indicator For Cusmentioning

confidence: 99%

Online Model-Based Remaining-Useful-Life Prognostics for Aircraft Cooling Units Using Time-Warping Degradation Clustering

Mitici

Pater

2021

Aerospace

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Remaining-useful-life prognostics for aircraft components are central for efficient and robust aircraft maintenance. In this paper, we propose an end-to-end approach to obtain online, model-based remaining-useful-life prognostics by learning from clusters of components with similar degradation trends. Time-series degradation measurements are first clustered using dynamic time-warping. For each cluster, a degradation model and a corresponding failure threshold are proposed. These cluster-specific degradation models, together with a particle filtering algorithm, are further used to obtain online remaining-useful-life prognostics. As a case study, we consider the operational data of several cooling units originating from a fleet of aircraft. The cooling units are clustered based on their degradation trends and remaining-useful-life prognostics are obtained in an online manner. In general, this approach provides support for intelligent aircraft maintenance where the analysis of cluster-specific component degradation models is integrated into the predictive maintenance process.

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“…Rolling element bearings play a vital role in various industrial fields such as aviation, energy, and metallurgy. They generally work under varying speed conditions, which brings great challenges to the health monitoring and performance degradation evaluation of bearings [ 1 ]. For instance, the rotating speed of wind turbines changes because of time-varying wind speed and grid control over the long term.…”

Section: Introductionmentioning

confidence: 99%

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

Xue

Lei

2017

Materials

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Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

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Modeling the Relationship between Vibration Features and Condition Parameters Using Relevance Vector Machines for Health Monitoring of Rolling Element Bearings under Varying Operation Conditions

Cited by 13 publications

References 17 publications

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

Online Model-Based Remaining-Useful-Life Prognostics for Aircraft Cooling Units Using Time-Warping Degradation Clustering

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

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