Emphasising bearing tones for prognostics

Klein, Renata; Rudyk, Eduard; Masad, Eyal; Issacharoff, M

doi:10.1784/204764211798303823

Cited by 15 publications

(14 citation statements)

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“…The strain signal in the time domain was synchronized with the rotational speed signal, by angular resampling, and was transformed to the cycle domain. A de-phase algorithm (Klein et al, 2011) was applied to the strain in the cycle domain to remove the synchronous elements, e.g. shaft, that were masking the effect of the bearing in the signal.…”

Section: Discussionmentioning

confidence: 99%

Severity Estimation of Faulty Bearings Based on Strain Signals From Physical Models and FBG Measurements

2022

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Condition based maintenance (CBM) is the preferred approach in rotating machinery and aim to replace the commonly used approach of maintenance based on service time. To achieve an effective CBM, different types of sensors should be placed in the system for condition monitoring to detect the location of the fault and its severity. In this research, a Fiber Bragg Grating (FBG) has been used for condition monitoring on spalls in deep grove ball bearings. The motivation for using these sensors is the ability to get a high-noise signal (SNR) ratio. The usage of FBG sensors is relatively new for health monitoring systems of rotating machinery. Therefore, there is not enough understanding of the strain signature measured by the FBG. To examine the phenomena in the strain signals, a physics-based model of the strain signature has been developed. In this model, two complementary models were integrated, a finite element (FE) model and a dynamic model . The strain model describes the interaction between the rolling elements (REs) and the bearing housing and simulates the strain behavior measured on the bearing housing. The simulation results are validated with strain signals measured by the FBG sensor at different stages of an endurance test. The model allows simulation of a wide range of spall lengths and describes the behavior of the strain signals for different levels of misalignment. The insights from the model enabled the development of an automatic algorithm that assess the severity of the defect and to track spall length during bearing operation, based on strain signals.

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Section: Discussionmentioning

confidence: 99%

Severity Estimation of Faulty Bearings Based on Strain Signals From Physical Models and FBG Measurements

2022

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“…Then, this resampled signal minus the equal length of time synchronous averaging signal will result in the residual signal. The detailed process can be seen in [23]. Next, the post processing is based on the residual signal.…”

Section: Experimental Gearbox Bearing Outer Race Fault Studymentioning

confidence: 99%

A New Gearbox Fault Diagnosis Method Based on Lucy–richardson Deconvolution

Zhang

Kang

Teng

et al. 2015

Transactions of the Canadian Society for Mechanical Engineering

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Gear and bearing faults are the main causes of gearbox failure. Till now, incipient fault diagnosis of these two components has been a problem and needs further research. In this context, it is found that Lucy–Richardson deconvolution (LRD) proved to be an excellent tool to enhance fault diagnosis in rolling element bearings and gears. LRD’s good identification capabilities of fault frequencies are presented which outperform envelope analysis. This is very critical for early fault diagnosis. The case studies were carried out to evaluate the effectiveness of the proposed method. The results of simulated and experimental studies show that LRD is efficient in alleviating the negative effect of noise and transmission path. The results of simulation and experimental tests demonstrated outperformance of LRD compared to classical envelope analysis for fault diagnosis in rolling element bearings and gears, especially when it is applied to the processing of signals with strong background noise.

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“…In order to solve this problem, linear prediction, adaptive noise cancellation, and discrete/random separation were used to separate the bearing signals from the signals of other components. 11 Klein et al 12 proposed an effective method which could separate bearing signals from those of shafts, gears, rotors, pumps, and other discrete frequencies. The technique is based on few stages of resampling and removal of the time synchronous average (TSA) signal.…”

Section: Introductionmentioning

confidence: 99%

“…11 Klein et al. 12 proposed an effective method which could separate bearing signals from those of shafts, gears, rotors, pumps, and other discrete frequencies. The technique is based on few stages of resampling and removal of the time synchronous average (TSA) signal.…”

Section: Introductionmentioning

confidence: 99%

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A heterogeneous fault diagnosis method for bearings in gearbox

Chen

Xiao

Zhang³

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part C:

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Bearing failure is one of the most important causes of breakdown of rotating machinery. These failures can lead to catastrophic disasters or result in costly downtime. One of the key problems in bearing fault diagnosis is to detect the bearing fault as early as possible. This capability enables the operator to have enough time to do some preventive maintenance. Most papers investigate the bearing faults under rational assumption that bearings work individually. However, bearings are usually working as a part of complex systems like a gearbox. The fault signal of bearings can be easily masked by other vibration generated from gears and shafts. The proposed method separates bearing signals from other signals, and then the optimum frequency band which the bearing fault signal is prominent is determined by mean envelope Kurtosis. Subsequently, the envelope analysis is used to detect the bearing faults. Finally, two bearing fault experiments are used to validate the proposed method. Each experiment contains two bearing fault modes, inner race fault and outer race fault. The results demonstrate that the proposed method can detect the bearing fault easier than spectral Kurtosis and envelope Kurtosis.

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Emphasising bearing tones for prognostics

Cited by 15 publications

References 0 publications

Severity Estimation of Faulty Bearings Based on Strain Signals From Physical Models and FBG Measurements

Severity Estimation of Faulty Bearings Based on Strain Signals From Physical Models and FBG Measurements

A New Gearbox Fault Diagnosis Method Based on Lucy–richardson Deconvolution

A heterogeneous fault diagnosis method for bearings in gearbox

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