Methodology and Framework for Predicting Helicopter Rolling Element Bearing Failure

Siegel, David; Ly, Canh; Lee, Jay

doi:10.1109/tr.2012.2220697

Cited by 67 publications

(28 citation statements)

References 23 publications

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“…RUL of the bearings can be estimated using vibration behaviors and running time of the bearings. Although various algorithms exist for providing RUL, considering the limited number of run-to-failure data sets, the maturation of the prognostic techniques has not been achieved [118]. A general methodology to perform rolling element bearing prognostics using a robust regression curve fitting approach was proposed in [118].…”

Section: Prediction Of the Remaining Useful Lifementioning

confidence: 99%

“…Although various algorithms exist for providing RUL, considering the limited number of run-to-failure data sets, the maturation of the prognostic techniques has not been achieved [118]. A general methodology to perform rolling element bearing prognostics using a robust regression curve fitting approach was proposed in [118]. A moving average filter was applied to the time series kurtosis to identify trends over time, and then Bayesian Monte Carlo was used to estimate the RUL of ball bearing [119].…”

Section: Prediction Of the Remaining Useful Lifementioning

confidence: 99%

See 1 more Smart Citation

Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

Wang

Xiang

Markert

et al. 2016

Mechanical Systems and Signal Processing

408

142

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Section: Prediction Of the Remaining Useful Lifementioning

confidence: 99%

Section: Prediction Of the Remaining Useful Lifementioning

confidence: 99%

Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

Wang

Xiang

Markert

et al. 2016

Mechanical Systems and Signal Processing

408

142

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“…A smaller value of MI indicates the similarity index with better monotonicity. Given the RBF kernel function, the optimal kernel parameters, denoted by , are chosen as the ones that minimize the objective function (31) The two parameters are viewed as combined parameters. With this objective function, the optimal parameters for the RBF kernel are searched in the given ranges with fixed step lengths by the ergodic search method.…”

Section: F Selection Of the Kernel Function And Its Parametersmentioning

confidence: 99%

“…Yan and Gao used advanced signal processing techniques, such as Hilbert-Huang transform, and wavelet transform, to extract fault-related features hidden in the condition monitoring information of machines [29], [30]. Siegel and Lee designed a framework for predicting helicopter rolling element bearing failure by means of the acceleration signals [31]. Shen and Sun proposed bearing degradation assessment methods based on vibration signal analysis [32], [33].…”

Section: Introductionmentioning

confidence: 99%

A Non-Probabilistic Metric Derived From Condition Information for Operational Reliability Assessment of Aero-Engines

Sun

Cao

et al. 2015

IEEE Trans. Rel.

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The aero-engine is the heart of an airplane. Operational reliability assessment that aims to identify the reliability level of the aero-engine in the service phase is of great significance for improving flight safety. Traditionally, reliability assessment is carried out by statistical analysis on large failure samples. Because the operational reliability of a specific aero-engine is an individual problem lacking statistical sample data, traditional reliability assessment methods may be insufficient to assess the operational reliability of an individual aero-engine. The operational states of the aero-engine can be identified by its condition information. Changes in the condition information reflect the performance degradation of the aero-engine. Aiming at the assessment of the operational reliability of individual aero-engines, a novel similarity index (SI) is proposed by analyzing the condition information from the fault-free state, and the current state. A condition subspace is first obtained by kernel principal component analysis (KPCA). Subspace similarity is then represented by subspace angles, i.e., kernel principal angles (KPAs). The cosine function is finally utilized as a mapping function to transform the subspace angles into a similarity index. The index can be used as a non-probabilistic metric for operational reliability assessment. Only the condition information is needed for computation of the similarity index,thus it can be performed conveniently for online assessment. The effectiveness of the proposed method is validated by three case studies regarding the health assessment of aero-engines subjected to system-level and component-level degradation. The positive results demonstrate that the proposed SI is an effective metric for operational reliability assessment of individual aero-engines.Index Terms-Condition information, individual aero-engine, non-probabilistic metric, operational reliability assessment, similarity index. ACRONYMS AND ABBREVIATIONS: CMCondition monitoring. DivKullback-Leibler divergence of the signal energy.

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“…Considering the sensitivity to impact signal, which is the main characteristic of faulty REB vibrations, one of the time domain higher-order statistics, the kurtosis that is very sensitive for impact signal [8,9,10], is introduced here. It was first studied by Dyer et al and they suggested that a kurtosis greater than 3 could indicate the emergence of bearing faults [11].…”

Section: Introductionmentioning

confidence: 99%

The Shock Pulse Index and Its Application in the Fault Diagnosis of Rolling Element Bearings

Sun

Liao

Lin

2017

Sensors

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The properties of the time domain parameters of vibration signals have been extensively studied for the fault diagnosis of rolling element bearings (REBs). Parameters like kurtosis and Envelope Harmonic-to-Noise Ratio are the most widely applied in this field and some important progress has been made. However, since only one-sided information is contained in these parameters, problems still exist in practice when the signals collected are of complicated structure and/or contaminated by strong background noises. A new parameter, named Shock Pulse Index (SPI), is proposed in this paper. It integrates the mutual advantages of both the parameters mentioned above and can help effectively identify fault-related impulse components under conditions of interference of strong background noises, unrelated harmonic components and random impulses. The SPI optimizes the parameters of Maximum Correlated Kurtosis Deconvolution (MCKD), which is used to filter the signals under consideration. Finally, the transient information of interest contained in the filtered signal can be highlighted through demodulation with the Teager Energy Operator (TEO). Fault-related impulse components can therefore be extracted accurately. Simulations show the SPI can correctly indicate the fault impulses under the influence of strong background noises, other harmonic components and aperiodic impulse and experiment analyses verify the effectiveness and correctness of the proposed method.

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Methodology and Framework for Predicting Helicopter Rolling Element Bearing Failure

Cited by 67 publications

References 23 publications

Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

A Non-Probabilistic Metric Derived From Condition Information for Operational Reliability Assessment of Aero-Engines

The Shock Pulse Index and Its Application in the Fault Diagnosis of Rolling Element Bearings

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