A Novel Condition Indicator for Bearing Fault Detection Within Helicopter Transmission

Elasha, Faris; Li, Xiaochuan; Mba, David; Ogundare, A. A.; Ojolo, S. J.

doi:10.1007/s42417-020-00220-7

Cited by 17 publications

(7 citation statements)

References 17 publications

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“…The permutation entropy derived from flexible analytical wavelet transform was used as a feature for an SVM for bearing fault classification [23]. Impulse factor, Kurtosis and RMS based on WT coefficients were used for bearing fault detection of helicopters [24]. Statistical features and Hoelder's exponent were derived from WT coefficients for milling tool health state monitoring.…”

Section: Time-frequency-based Health Indicatorsmentioning

confidence: 99%

A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

Nentwich

Reinhart

2021

Robotics

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Condition monitoring of industrial robots has the potential to decrease downtimes in highly automated production systems. In this context, we propose a new method to evaluate health indicators for this application and suggest a new health indicator (HI) based on vibration data measurements, Short-time Fourier transform and Z-scores. By executing the method, we find that the proposed health indicator can detect varying faults better, has lower temperature sensitivity and works better in instationary velocity regimes compared to several state-of-the-art HIs. A discussion of the validity of the results concludes our contribution.

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Section: Time-frequency-based Health Indicatorsmentioning

confidence: 99%

A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

Nentwich

Reinhart

2021

Robotics

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“…Rolling bearing is an important part widely used in rotating machinery, such as wind turbines [ 1 ], high-speed railways [ 2 ], helicopters [ 3 ] and electric vehicles [ 4 ]. As rolling bearing always operates under harsh working conditions, ranging from high speed and alternating speed to heavy load and alternating load, its inner race, outer race, and balls are prone to suffer from various kinds of damages, including fatigue pitting, wear, spalling, cracking, etc.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index

Guo

Yang

Jiang

et al. 2022

Sensors

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Rolling bearing is an important part guaranteeing the normal operation of rotating machinery, which is also prone to various damages due to severe running conditions. However, it is usually difficult to extract the weak fault characteristic information from rolling bearing vibration signals and to realize a rolling bearing fault diagnosis. Hence, this paper offers a rolling bearing fault diagnosis method based on successive variational mode decomposition (SVMD) and the energy concentration and position accuracy (EP) index. Since SVMD decomposes a vibration signal of a rolling bearing into a number of modes, it is difficult to select the target mode with the ideal fault characteristic information. Comprehensively considering the energy concentration degree and frequency position accuracy of the fault characteristic component, the EP index is proposed to indicate the target mode. As the balancing parameter is crucial to the performance of SVMD and must be set properly, the line search method guided by the EP index is introduced to determine an optimal value for the balancing parameter of SVMD. The simulation and experiment results demonstrate that the proposed SVMD method is effective for rolling bearing fault diagnosis and superior to the variational mode decomposition (VMD) method.

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“…Another approach that is based on the adaptive regression splines method and trend change detection was presented in [27]. In [28], the authors proposed a new impulse energy indicator. They utilized an adaptive filter for signal separation, wavelet packet decomposition, and the combination of RMS and kurtosis to select the optimum filter band which indicates the fault in the bearing of the gearbox.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Gearbox System Fault Diagnosis

Vrba

Cejnek

Steinbach

et al. 2021

Entropy

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This study proposes a fully automated gearbox fault diagnosis approach that does not require knowledge about the specific gearbox construction and its load. The proposed approach is based on evaluating an adaptive filter’s prediction error. The obtained prediction error’s standard deviation is further processed with a support-vector machine to classify the gearbox’s condition. The proposed method was cross-validated on a public dataset, segmented into 1760 test samples, against two other reference methods. The accuracy achieved by the proposed method was better than the accuracies of the reference methods. The accuracy of the proposed method was on average 9% higher compared to both reference methods for different support vector settings.

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A Novel Condition Indicator for Bearing Fault Detection Within Helicopter Transmission

Cited by 17 publications

References 17 publications

A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index

A Machine Learning Approach for Gearbox System Fault Diagnosis

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