Analytical Model for Separated Frequency Signature of Outer Race Bearing Fault From Static Eccentricity

The present paper addresses a precise and an accurate mathematical model for three-phase squirrel cage induction motors, based on winding function theory. Through an analytical development, a comparative way is presented to separate the signature between the existence of the outer race bearing fault and the static eccentricity concerning the asymmetry of the air gap between the stator and the rotor. This analytical model proposes an effective signature of outer race defect separately from other signatures of static eccentricity. Simulation and experimental results are presented to validate the proposed analytical model.

Express method for the testing of tribotechnical properties of lubricants

The paper presents the results of experimental study of the tribotechnical properties of lubricants on a unit that simulates the geometric, kinematic and force similarity of well drilling conditions. Bearings with different radial clearances and the same chemical-thermal treatment were investigated. Data registration was carried out on cathode, loop oscilloscopes and electronic recorders. The load on the bearing, the moment of rolling resistance on the journal, and the angular speed of rotation of the outer race were recorded. The temperature was registered using artificial and semiartificial thermocouples. A strobotachometer was used to determine the portable speed of the rolling bodies. The external appearance of all rolling elements was investigated, metallographic analysis of thin surface layers of all rolling elements was carried out, mathematical processing of test results was carried out. It is shown that for the express assessment of the tribotechnical properties of lubricants, the amplitude value of the oscillation of the rolling resistance moment can be used. Keywords: friction; lubrication; tribotechnical Properties; drilling.

An approach to investigating rolling element bearing damage under impact loading

This paper presents an approach to studying rolling element bearing damage under the interference of impact loading. In the experimental part, a series of bearing tests was performed by using the twin-disc test device with artificially damaged bearings. This was followed by analysis of the measured acceleration response data in impact-free condition as well as under the influence of the impact loading. The results showed successful detection of the bearing outer race damage by using typical bearing damage detection approaches regardless whether the impact loading was applied to the system or not. In turn, recognition of the bearing rolling element damage required specific signal processing.

Vibration response of defect-ball-defect of rolling bearing with compound defects on both inner and outer races

Aiming at the problem that the existing compound defects model of rolling bearings under radial load is difficult to reflect the actual contact between rolling elements and defects. A new model is proposed to accurately reflect the simultaneous or sequential contact between inner and outer race defects and rolling elements. Considering the coupled excitation between shaft and bearing and pedestal, time-varying displacement excitation, and radial clearance, a four degree-of-freedom vibration model of rolling bearing with compound faults on both inner and outer races is built. The vibration equations are calculated by the method of numerical way, and the model is verified by experiment. The vibration response characteristics of the Defect-Ball-Defect model are studied, which renders a theoretical criterion for bearing fault diagnosis.

Sensors Applied to Bearing Fault Detection in Three-Phase Induction Motors

Three-Phase Induction Motors (TIMs) are widely applied in industries. Therefore, there is a need to reduce operational and maintenance costs since their stoppages can impair production lines and lead to financial losses. Among all the TIM components, bearings are crucial in the machine operation once they couple rotor to the motor frame. Furthermore, they are constantly subjected to friction and mechanical wearing. Consequently, they represent around 41% of the motor fault, according to IEEE. In this context, several studies have sought to develop monitoring systems based on different types of sensors. Therefore, considering the high demand, this article aims to present the state of the art of the past five years concerning the sensing techniques based on current, vibration, and infra-red analysis, which are characterized as promising tools to perform bearing fault detection. The current and vibration analysis are powerful tools to assess damages in the inner race, outer race, cages, and rolling elements of the bearings. These sensing techniques use current sensors like hall effect-based, Rogowski coils, and current transformers, or vibration sensors such as accelerometers. The effectiveness of these techniques is due to the previously developed models, which relate the current and vibration frequencies to the origin of the fault. Therefore, this article also presents the bearing fault mathematical modeling for these techniques. The infra-red technique is based on heat emission, and several image processing techniques were developed to optimize bearing fault detection, which is presented in this review. Finally, this work is a contribution to pushing the frontiers of the bearing fault diagnosis area.

Traction effects on the dynamics of ball bearings

In the paper, a combined model of traction and ball bearings dynamics is established, considering the interaction among the cage, races, and balls. A curve-fitted traction model, which can be used to analyze the effect of the geometry of the bearing and parameters of the lubricant, is applied. The traction that is affected by certain parameters is analyzed; then, the effect of the traction on ball bearings is investigated. The displacements of balls and contact angles are different under different surface roughness. The changes in displacements of balls and contact angles are obtained with different roughness, load, and velocities. The amplitudes of axial displacement of balls are bigger with the roughness being higher. The amplitudes of ball-inner contact angles increase with greater roughness, and the amplitudes of ball-outer race contact angles decrease with greater roughness. The simulated vibration results of ball bearings under different levels agree with the actual by the model.

Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network

Bearings are the elements that allow the rotatory movement in induction motors, and the fault occurrence in these elements is due to excessive working conditions. In induction motors, electrical erosion remains the most common phenomenon that damages bearings, leading to incipient faults that gradually increase to irreparable damages. Thus, condition monitoring strategies capable of assessing bearing fault severities are mandatory to overcome this critical issue. The contribution of this work lies in the proposal of a condition monitoring strategy that is focused on the analysis and identification of different fault severities of the outer race bearing fault in an induction motor. The proposed approach is supported by fusion information of different physical magnitudes and the use of Machine Learning and Artificial Intelligence. An important aspect of this proposal is the calculation of a hybrid-set of statistical features that are obtained to characterize vibration and stator current signals by its processing through domain analysis, i.e., time-domain and frequency-domain; also, the fusion of information of both signals by means of the Linear Discriminant Analysis is important due to the most discriminative and meaningful information is retained resulting in a high-performance condition characterization. Besides, a Neural Network-based classifier allows validating the effectiveness of fusion information from different physical magnitudes to face the diagnosis of multiple fault severities that appear in the bearing outer race. The method is validated under an experimental data set that includes information related to a healthy condition and five different severities that appear in the outer race of bearings.

Analytical model and spectral characteristics of acoustic emission signal produced by localized fault of rolling element bearing

The accurate description of acoustic emission signals produced by the localized fault of a rolling element bearing plays an important role in its feature extraction and analysis. This paper analyzes the excitation mechanisms and develops the analytical model of acoustic emission signals produced when the rolling element bearing passes across the localized fault on the inner or outer race. Based on the analytical model, the spectral characteristics are discussed substantially. Simulations and experiments are carried out to validate the efficacy of the model developed in the paper. The experimental results show that the response signal thus produced has two parts. The first one is produced by the entry of the rolling element bearing, while the other is produced by the departure of the rolling element bearing. The energy of both parts is concentrated around the resonance frequency of the acoustic emission transducer. Generally, the interval of adjacent acoustic emission events is not equivalent to each other and the corresponding spectrum is continuous in the high frequency band.