Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis

Cong, Feiyun; Chen, Jin; Dong, Guangming; Pecht, Michael

doi:10.1016/j.jsv.2012.11.029

Cited by 147 publications

(74 citation statements)

References 25 publications

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“…Operating characteristics of rotary systems with bearings of sliding friction and those of roll are compared. Generalization of research results is done and conclusions are formulated, [2], [13], [12].…”

Section: Identification Of the Technical Condition Of Roller Bearingsmentioning

confidence: 99%

Identification of the Technical Condition of Roller Bearings by means of Vibrodiagnostics and Tribodiagnostics

2017

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SummaryThis paper focuses on determining the technical condition of a bearing box in the vehicle by means of vibrodiagnostics. In the next step, we determined the technical condition of roller bearings in the bearing box by measuring the vibration signal. One of the possibilities of determining the technical condition, including the location of accelerometers, the arrangement of relevant measured devices, and the analysis of frequency spectrum, is described in the paper. The aim of the experiment was to measure vibrations of the bearings placed in the vehicle bearing box and then to carry out the analysis of the measured data. The second part of the paper presents the analysis of samples of the plastic lubricant used for lubricating the tested roller bearings. For the analysis, we used an AES Spectroil Q100 device which works on the principle of atomic spectral analysis.

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Section: Identification Of the Technical Condition Of Roller Bearingsmentioning

confidence: 99%

Identification of the Technical Condition of Roller Bearings by means of Vibrodiagnostics and Tribodiagnostics

2017

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“…Because mechanical big data is typically characterized as large-volume, diverse, and of high-velocity [8], methods of extracting features rapidly and accurately from mechanical big data has become an urgent subject of research [9,10]. Existing fault diagnosis methods can be divided into two major categories [11]: physics-based models and data-driven ones [12,13]. Physics-based models overly rely on high-quality domain knowledge and necessitate massive computation costs, which reduces the overall efficiency of fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Fault Diagnosis Approach Considering the Elimination of the Weight Matrix Multi-Correlation

Wang

et al. 2018

Applied Sciences

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Faults in bearings and gearboxes, which are widely used in rotating machines, can lead to heavy investment and productivity loss. Accordingly, a fault diagnosis system is necessary to ensure a high-performance transmission. However, as mechanical fault diagnosis enters the era of big data, it can be difficult to apply traditional fault diagnosis methods because of the massive computation cost and excessive reliance on human labor. Meanwhile, unsupervised learning has been shown to have excellent performance in processing machanical big data. In this paper, an unsupervised learning method known as sparse filtering is applied, the multi-correlation of a weight matrix is investigated, and a method that is more suitable for the feature extraction of signals is proposed. The main contribution of our work is the modification of original method. First, to understand the non-monotonicity testing accuracies of the original method, the physical interpretation of input dimensions is studied. Second, using the physical interpretation, an overfitting phenomenon is discovered and examined. Third, to reduce the overfitting phenomenon, a method which eliminates the multi-correlation of the weight matrix is proposed. Finally, bearing and gear datasets are employed to verify the effectiveness of the proposed method; experimental results show that the proposed method can achieve a superior performance in comparison to the original sparse filtering model.

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“…When a bearing has a localized defect either on its outer race or its inner race, a series of random transients caused by rollers striking a defect surface are generated [8]. It is experimentally proven that wavelet transforms are naturally able to match the transients caused bearing localized faults [9].…”

Section: Introductionmentioning

confidence: 99%

A general sequential Monte Carlo method based optimal wavelet filter: A Bayesian approach for extracting bearing fault features

Wang

Sun

Tse

2015

Mechanical Systems and Signal Processing

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Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis

Cited by 147 publications

References 25 publications

Identification of the Technical Condition of Roller Bearings by means of Vibrodiagnostics and Tribodiagnostics

Identification of the Technical Condition of Roller Bearings by means of Vibrodiagnostics and Tribodiagnostics

An Intelligent Fault Diagnosis Approach Considering the Elimination of the Weight Matrix Multi-Correlation

A general sequential Monte Carlo method based optimal wavelet filter: A Bayesian approach for extracting bearing fault features

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