Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis

Most fault detection methods based on the assumption of working in stationary or approximate stationary conditions are limited under varying operation conditions, for that the frequency aliasing phenomenon is inevitable in the spectrum. Therefore, in order to handle the problem of fault diagnosis under non-stationary conditions, researchers have proposed numerous methods and some achievements have been obtained. In this article, a new feature extraction method is proposed for fault diagnosis of rolling bearings under varying speed conditions. Based on the assumption that the energy will increase when balls cross over fault position, frequency values are divided by instantaneous speed and arranged in the descending order of corresponding amplitude to form a new fault feature array, that is, the ratio of frequency to instantaneous speed reconfiguration arrays. Thereafter, the Euclidean distance classifier is utilized for recognition. The efficacy of the proposed method is demonstrated by simulated and experimental data. Categorized results show that the new approach is capable of handling the bearing fault classification under varying speed conditions.

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“…28 However, due to the disturbance of noise and interferences with higher energy, the assumption mentioned above may not be always correct.…”

Section: Calculate the Mafsmentioning

confidence: 99%

A new fault feature for rolling bearing fault diagnosis under varying speed conditions

Ren

Zhu

et al. 2017

Advances in Mechanical Engineering

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“…The acquisition is made continuously in a circular buffer, but the signal is only recorded once the non-stationarity rate computed on the buffer is lower than a predefined threshold (1% in our implementation). In the case where a rotation speed measurement is also acquired, the threshold is relaxed to a higher value (3%) and the non-stationarities are removed thanks to an angular resampling [20], [21], [22].…”

Section: A Data Validation and Peak Identificationmentioning

confidence: 99%

“…So, the main hypothesis to use the tracking algorithm in a condition monitoring context is to acquire the signals in a "constant machine state", where the operational parameters do not change from one acquisition to the other. For example, the acquisition of signals on a rotating machine should always be done for the same rotational speed, or if not, an angular resampling [20], [21], [22] should be performed as a pre-process. In this case, the characteristic frequencies of the system will remain the same and the tracking will be possible.…”

Section: Time-frequency Tracking Reviewmentioning

confidence: 99%

Time-Frequency Tracking of Spectral Structures Estimated by a Data-Driven Method

Gerber

Martin

Mailhes

2015

IEEE Trans. Ind. Electron.

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Abstract-The installation of a condition monitoring system aims to reduce the operating costs of the monitored system by applying a predictive maintenance strategy. However, a systemdriven configuration of the condition monitoring system requires the knowledge of the system kinematics and could induce lots a false alarms because of predefined thresholds. The purpose of this paper is to propose a complete data-driven method to automatically generate system health indicators without any a priori on the monitored system or the acquired signals. This method is composed of two steps. First, every acquired signal is analysed: the spectral peaks are detected and then grouped in more complex structure as harmonic series or modulation sidebands. Then, a time-frequency tracking operation is applied on all available signals: the spectral peaks and the spectral structures are tracked over time and grouped in trajectories, which will be used to generate the system health indicators. The proposed method is tested on real-world signals coming from a wind turbine test rig. The detection of a harmonic series and a modulation sideband reports the birth of a fault on the main bearing inner ring. The evolution of the fault severity is characterised by three automatically generated health indicators and is confirmed by experts.

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“…Various vibration analysis techniques have been widely investigated in gearbox fault diagnosis and prognosis. For instance, a fault characteristic order (FCO) analysis method was proposed to extract the vibration signal components related to rotational speed from the time-frequency representation (TFR) for gear fault detection under time-varying rotational speed (Wang et al 2014). A modified cantilever beam model was investigated to analytically evaluate the time-varying mesh stiffness of a planetary gear set for the detection of crack severity and location via vibration analysis .…”

Section: Introductionmentioning

confidence: 99%

Virtual sensing for gearbox condition monitoring based on kernel factor analysis

et al. 2017

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Vibration and oil debris analysis are widely used in gearbox condition monitoring as the typical indirect and direct sensing techniques. However, they have their own advantages and disadvantages. To better utilize the sensing information and overcome its shortcomings, this paper presents a virtual sensing technique based on artificial intelligence by fusing low-cost online vibration measurements to derive a gearbox condition indictor, and its performance is comparable to the costly offline oil debris measurements. Firstly, the representative features are extracted from the noisy vibration measurements to characterize the gearbox degradation conditions. However, the extracted features of high dimensionality present nonlinearity and uncertainty in the machinery degradation process. A new nonlinear feature selection and fusion method, named kernel factor analysis, is proposed to mitigate the aforementioned challenge. Then the virtual sensing model is constructed by incorporating the fused vibration features and offline oil debris measurements based on support vector regression. The developed virtual sensing technique is experimentally evaluated in spiral bevel gear wear tests, and the results show that the developed kernel factor analysis method outperforms the state-of-the-art feature selection techniques in terms of virtual sensing model accuracy.

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Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis

Cited by 182 publications

References 20 publications

A new fault feature for rolling bearing fault diagnosis under varying speed conditions

A new fault feature for rolling bearing fault diagnosis under varying speed conditions

Time-Frequency Tracking of Spectral Structures Estimated by a Data-Driven Method

Virtual sensing for gearbox condition monitoring based on kernel factor analysis

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