Detection and Characterization of Bearing Faults from the Frequency Domain Features of Vibration

Arun, P.; Lincon, S. Abraham; Prabhakaran, N.

doi:10.1080/03772063.2017.1369369

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…We calculated the HIs summarized in Table 1 for all measurements of one sensor. These HIs were derived from several review papers regarding gearbox and bearing CM [37][38][39][40] and the publications mentioned in Section 1. Therefore, the HI calculation was based either on the raw acceleration signal, an enveloped signal as described in [41] or the residual signal as suggested by [4].…”

Section: Varying Fault Detection Analysismentioning

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: Varying Fault Detection Analysismentioning

confidence: 99%

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

Nentwich

Reinhart

2021

Robotics

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“…At first, many signal analysis-based methods were widely used for fault feature extraction whereas signal analysis-based methods without achieving fault diagnosis [3][4][5]. Next, the machine learning-based fault diagnosis method become a focus.…”

Section: Introductionmentioning

confidence: 99%

Time graph sub-domain adaption adversarial for fault diagnosis

Sun,

Yin,

et al. 2024

Meas. Sci. Technol.

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Graph Neural Network (GNN)-based fault diagnosis has attracted widespread attention. However, the existing measure metrics of domain distribution discrepancy always is single, the weight of different domains is unknown, and the scale of GNN-based feature extractor is unknown. To address these issues, a Time Graph Sub-Domain Adaption Adversarial for Fault Diagnosis (TGSDAA) is proposed in this paper. Firstly, a multi-scale time connection layer is proposed to extract the feature of the signal. Specifically, an Encode layer is proposed to construct the undirected graph. Next, a multi-receptive field cluster-graph convolution neural network is proposed to extract features of the graph. Finally, a sub-domain alignment with adversarial adaption is proposed to align different domains and achieve fault diagnosis. Two different datasets are used to verify the effectiveness of TGSDAA. The experimental results show that the average diagnosis accuracy of TGSDAA can improve 4% than other methods.

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“…For instance, changes in the median frequency and the spectral centroid are related to the condition of inner races and roller elements in bearings, whereas the latter indicator can be also used for evaluating the technical state of outer races. A more detailed study on the benefits of specific indicators in the diagnostics of bearings is presented in [18]. It should be emphasized that changes in the distribution of vibroacoustic energy, are also good diagnostic indicators because bearing failures affect the level of this particular energy [19].…”

mentioning

confidence: 99%

A Method of Comparative Evaluation of the Drive Units of Two Bucket Elevators—A Case Study

Sokolski

Sokolska

2021

Energies

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Bucket elevators are applied in many industries for bulk material handling. One of the main requirements for these devices is their high operational reliability. This applies in particular to power units that must operate continuously without failure. This article presents a comparative assessment of the drive units of two bucket elevators. The vibration intensity of their bearing units was used as the basis for the comparison. The evaluation was carried out using three independent methods based on the vibration velocity analysis: in the time domain, in the frequency domain and using the probabilistic approach.

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Detection and Characterization of Bearing Faults from the Frequency Domain Features of Vibration

Cited by 11 publications

References 27 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

Time graph sub-domain adaption adversarial for fault diagnosis

A Method of Comparative Evaluation of the Drive Units of Two Bucket Elevators—A Case Study

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