Compound faults detection of rolling element bearing based on the generalized demodulation algorithm under time-varying rotational speed

Effective fault detection and diagnosis (FDD) for rotating machinery is always a focus issue in improving the prognostic and health management (PHM) of the equipments. The existing usage of similarity measurement has been widely spread in searching the homologous fault responses from vibration signals, but most of them are just suitable for stable speed and cannot be applied in all variable speed conditions. In order to improve measurement performance, a fast-meshed phase portrait (FMPP) frame combining the phase-space technique and box-scoring calculation is proposed. Firstly, the variable-speed signal is divided into multiple undetermined fragments according to fault characteristic orders (FCOs). Secondly, the undetermined fragments are reconstructed into corresponding phase-space trajectories to overcome the time-delay matching inconsistency of the variable speed fragments. Thirdly, the phase-space trajectories are mapped into meshed phase portraits via box-scoring calculation. Such decisive calculation can effectively transforms the diverse unequal fragments into the phase diagrams with same size, which saves time for the subsequent similarity measurement. Finally, the proposed FMPP is tested both for accuracy and timeliness on a self-built bearing bench, where the order tracking (OT) and dynamic time warping (DTW) methods are used for the comparisons. The experiments proved the effectiveness of the proposed method.

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“…Considering the slip effect of the bearing rolling elements, the fault bearing vibration signal model is as follows [26,27]:…”

Section: Signal Simulation Validity Verificationmentioning

confidence: 99%

A Similarity Comparison Method of Homologous Fault Response Fragments under Variable Rotational Speed

Chen

Wen

2020

Shock and Vibration

Self Cite

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“…Especially with a rapid increase of wind turbine (WT) units, their fault rates and volumes are significantly high due to harsh operational environments, which leads to the high cost of WT maintenance [3][4][5]. To prevent these negative influences, great attention has been paid to developing effective bearing fault diagnosis techniques for many years, which have resulted in many useful tools, such as the most common vibration analysis based approach that is capable of detecting local defects in bearing race ways at early stages [6][7][8][9]. These tools provide good leading time for industries to take necessary and adequate maintenance actions to minimize down time and maintenance costs, consequently avoiding severe consequences and maximizing production.…”

Section: Introductionmentioning

confidence: 99%

Online Bearing Clearance Monitoring Based on an Accurate Vibration Analysis

Wang

Zhang

et al. 2020

Energies

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Accurate diagnosis of incipient faults in wind turbine (WT) assets will provide sufficient lead time to apply an optimal maintenance for the expensive WT assets which often are located in a remote and harsh environment and their maintenance usually needs heavy equipment and highly skilled engineers. This paper presents an online bearing clearance monitoring approach to diagnose the change of bearing clearance, providing an early and interpretable indication of bearing health conditions. A novel dynamic load distribution method is developed to efficiently gain the general characteristics of vibration response of bearings without local defects but with small geometric errors. It shows that the ball pass frequency of outer race (BPFO) is the primary exciting source due to biased load distribution relating to bearing clearance. The geometric errors, including various orders of runouts on different bearing parts, can be the secondary excitation source. Both sources lead to compound modulation responses with very low amplitudes, being more than 20 dB lower than that of a small local defect on raceways and often buried by background noise. Then, Modulation Signal Bispectrum (MSB) is identified to purify the noisy signal and Gini index is introduced to represent the peakness of MSB results, thereby an interpretable indicator bounded between 0 and 1 is established to show bearing clearance status. Datasets from both a dedicated bearing test and a run-to-failure gearbox test are employed to verify the performance and reliability of the proposed approach. Results show that the proposed method is capable to indicate a change of about 20 µm in bearing clearance online, which provides a significantly long lead time compared to the diagnosis method that focuses only on local defects. Therefore, this method provides a big opportunity to implement more cost-effective maintenance works or carry out timely remedial actions to prolong the lifespan of bearings. Obviously, it is applicable to not only WT assets, but also most rotating machines.

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“…Under the actual conditions, the failure of rolling bearing usually manifests itself as compound failure, and due to the influence of operating environment, the interaction between multiple noise source and compound fault is often presented. The separation of compound fault components from strong background noise is a difficult problem in the field of mechanical fault diagnosis [6][7][8]. Hemmati et al proposed a modified and effective signal processing algorithm to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes [9].…”

Section: Introductionmentioning

confidence: 99%

Compound Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by MCDK

Wan

Zhang

Dou

2018

Mathematical Problems in Engineering

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The fast spectrum kurtosis (FSK) algorithm can adaptively identify and select the resonant frequency band and extract the fault feature by the envelope demodulation method. However, in practical applications, the fault source may be located in different resonant frequency bands; plus in noise interference, the weak side of the compound fault is not easy to be identified by the FSK. In order to improve the accuracy of fast spectral kurtosis analysis method, a modified method based on maximum correlation kurtosis deconvolution (MCKD) is proposed. According to the possible fault characteristic frequencies, the period of MCKD is calculated, and the appropriate filter length is selected to filter the original compound fault signal. In this way, the compound fault located in different resonance bands is separated. Then, the signal after MCKD filtering is analyzed by FSK. Through the simulation and experimental analysis, the MCKD can separate the compound fault information in different frequency band and eliminate the noise interference; the FSK can accurately identify the resonance frequency and identify the weak fault characteristics of compound fault.

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Compound faults detection of rolling element bearing based on the generalized demodulation algorithm under time-varying rotational speed

Cited by 78 publications

References 20 publications

A Similarity Comparison Method of Homologous Fault Response Fragments under Variable Rotational Speed

A Similarity Comparison Method of Homologous Fault Response Fragments under Variable Rotational Speed

Online Bearing Clearance Monitoring Based on an Accurate Vibration Analysis

Compound Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by MCDK

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