Multi-band identification for enhancing bearing fault detection in variable speed conditions

Klausen, Andreas; Khang, Huynh Van; Robbersmyr, Kjell G.

doi:10.1016/j.ymssp.2019.106422

Cited by 29 publications

(11 citation statements)

References 26 publications

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“…Currently, condition monitoring of machines in non-stationary conditions is the most common application field in journal publications on machine diagnostics (examples in [14][15][16]). A lot of techniques have been successfully proposed in the literature [17], and the term non-stationary can refer to variable speed applications [18] or variable loads like in mining machines [19]. Indeed, all manufacturing processes that exhibit variable working conditions can benefit from results in the non-stationary diagnostics field.…”

Section: Critical Issues and Future Prospectsmentioning

confidence: 99%

Future Challenges in Condition Monitoring from an Industrial Perspective: The Case of the Independent Carts Systems

Borghi¹,

Cocconcelli

2021

Acoust Aust

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In this paper, the authors discuss about the future challenges of condition monitoring in an industrial context. One of the authors is Line Manager at Data Processing & Analytics for Equipment, Industry 4.0 & MES Products division of Tetra Pak Packaging Solutions S.p.A., a multinational company that approached condition monitoring and diagnostics fifteen years ago. So far, they have gained experience and have a clear idea of what the Industrial field expects in the coming years. In this paper, the analysis of a specific case study is an opportunity to suggest more general research themes on condition monitoring.

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Section: Critical Issues and Future Prospectsmentioning

confidence: 99%

Future Challenges in Condition Monitoring from an Industrial Perspective: The Case of the Independent Carts Systems

Borghi¹,

Cocconcelli

2021

Acoust Aust

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“…Klausen et al proposed a new multi-resonant region identification technique. This method combines computational order tracking with cepstral prewhitening in a new way to make the resonant frequencies in the signal emphasized [ 10 ]. These methods and systems cannot achieve the expected results and cannot provide timely early warning of failures, and deep learning can solve these problems well.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Detection System and Experiment Based on Deep Learning

Zhang

2022

Computational Intelligence and Neuroscience

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The current situation of frequent small-scale accidents shows that the existing methods have not completely solved the problem of bearing failures, and new research methods need to be used to complete the study of bearing failures. To prevent the failure of rolling bearings and meet the need for timely detection of faults, this research is based on deep learning. Using the combination of deep transfer learning and metric learning methods, the identification and analysis of bearing multi-state vibration signals under different working conditions are carried out. The combination of SSAE-based similarity measurement criteria and deep transfer learning can reduce the differences between different domains. It is difficult to distinguish the data samples at the boundary and diagnose the problems that the physical meaning is difficult to understand. Through the bearing fault diagnosis analysis, the validity of the deep learning diagnosis model proposed in this paper is verified. The results show that the detection accuracy of the rolling bearing fault detection method based on LCM-SSAE is 0.6 percentage points higher than that of the rolling bearing fault detection method based on SSAE, which proves that the method is suitable for the fault detection of rolling bearing, and it also shows the effectiveness and robustness of the fault detection system of rolling bearing.

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“…e key to fault diagnosis of rolling bearings is to extract effective feature information from vibration signals containing complex frequencies [9]. Vibration analysis and fault diagnosis have received considerable attention [10][11][12][13][14] and have been adopted to process nonstationary and nonlinear vibration signals. Among them, signal decomposition methods contribute much.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Component Screening Vector Local Characteristic-Scale Decomposition

Guan

Liu

et al. 2022

Shock and Vibration

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The fault vibration signal of a bearing has nonstationary and nonlinear characteristics and can be regarded as the combination of multiple amplitude- and frequency-modulation components. The envelope of a single component contains the fault characteristics of a bearing. Local characteristic-scale decomposition (LCD) can decompose the vibration signal into a series of multiple intrinsic scale components. Some components can clearly reflect the running state of a bearing, and fault diagnosis is conducted according to the envelope spectrum. However, the conventional LCD takes a single-channel signal as the research object, which cannot fully reflect the characteristic information of the rotor, and the analysis results based on different channel signals of the same section will be inconsistent. To solve this problem, based on full vector spectrum technology, the homologous dual-channel information is fused. A vector LCD method based on cross-correlation coefficient component selection is given, and a simulation analysis is completed. The effectiveness of the proposed method is verified by simulated signals and experimental signals of a bearing, which provides a method for bearing feature extraction and fault diagnosis.

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Multi-band identification for enhancing bearing fault detection in variable speed conditions

Cited by 29 publications

References 26 publications

Future Challenges in Condition Monitoring from an Industrial Perspective: The Case of the Independent Carts Systems

Future Challenges in Condition Monitoring from an Industrial Perspective: The Case of the Independent Carts Systems

Rolling Bearing Fault Detection System and Experiment Based on Deep Learning

Rolling Bearing Fault Diagnosis Based on Component Screening Vector Local Characteristic-Scale Decomposition

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