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About 13 to 44 per cent of motor faults are caused by bearing failures in induction motors (IMs), where lubrication plays a significant role in maintaining rotating equipment because it minimizes friction and prevents wear by separating parts that move next to each other, and more than 35 per cent of bearing failures can be attributed to improper lubrication. An excessive amount of grease causes the rollers or balls to slide along the race instead of turning, and the grease will actually churn. This churning action will eventually wear down the base oil of the grease and all that will be left to lubricate the bearing is a thickener system with little or no lubricating properties. The heat generated from the churning, insufficient lubricating oil will begin to harden the grease, and this will prevent any new grease added to the bearing from reaching the rolling elements, with the consequence of bearing failure and equipment downtime. Regarding the case of grease excess in bearings, this case has not been sufficiently studied. This work aims to present an effective methodology applied to the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the Margenau-Hill distribution (MHD) and artificial neural networks (ANNs), where the obtained results demonstrate the correct classification of the studied cases.

This work proposed an effective methodology applied to the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the MHD and ANNs.

In this paper, three cases of study for a bearing in an IM are studied, detected and classified correctly by combining some methods. The marginal frequency is obtained from the MHD, which in turn is achieved from the stator current signal, and a total of six features are estimated from the power spectrum, and these features are forwarded to the designed ANN with three output neurons, where each one represents a condition in the IM: healthy bearing, mechanical bearing fault and excessively lubricated bearing.

The proposed methodology can be applied to other applications; it could be useful to use a time–frequency representation through the MHD for obtaining the energy density distribution of the signal frequency components through time for analysis, evaluation and identification of faults or conditions in the IM for example; therefore, the proposed methodology has a generalized nature that allows its application for detecting other conditions or even multiple conditions under different working conditions by a proper calibration.

The lubrication plays a significant role in maintaining rotating equipment because it minimizes friction and prevents wear by separating parts that move next to each other, and more than 35 per cent of bearing failures can be attributed to improper lubrication and it negatively affects the efficiency of the motor, resulting in higher operating costs. Therefore, in this work, a new methodology is proposed for the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the MHD and ANNs. The proposed methodology uses a total of six features estimated from the power spectrum, and these features are sent to the designed ANN with three output neurons, where each one represents a condition in the IM: healthy bearing, mechanical bearing fault and excessively lubricated bearing. From the obtained results, it was demonstrated that the proposed approach achieves higher classification performance, compared to short-time Fourier transform, Gabor transform and Wigner-Ville distribution methods, allowing to identify mechanical bearing faults and bearing excessively lubricated conditions in an IM, with a remarkable 100 per cent effectiveness during classification for treated cases. Also, the proposed methodology has a generalized nature that allows its application for detecting other conditions or even multiple conditions under different working conditions by a proper calibration.

The purpose of this research is to show that wavelets are mathematical tools capable of separating vibration data into different frequency components, allowing the study of each component with a resolution matched to its specific time scale. Wavelets have advantages over traditional Fourier methods, particularly when a signal contains sharp spikes, discontinuities, and transients.

This paper presents a brief description of wavelets and shows their application to the analysis of typical transient signals due to vibrations in a steel‐reinforced concrete beam.

The research clearly shows that it is possible to evaluate modes and components separately by using the wavelet analysis.

The beam analysed can be used as a simple model of other more complicated structures, such as bridges and other high scale civil engineering constructions, where vibration analysis is a key issue for maintenance and failure assessments, thus representing an alternative mathematical tool for condition monitoring.

In this study, artificial neural networks (ANNs) are constructed and validated by using the bearing data generated numerically from a thermohydrodynamic (THD) lubrication model. In many tribological simulations, a surrogate model (meta-model) for obtaining a fast solution with sufficient accuracy is highly desired.

The THD model is represented by two coupled partial differential equations, a simplified generalized Reynolds equation, considering the viscosity variation across the film thickness direction and a transient energy equation for the 3-D film temperature distribution. The ANNs tested are having a single- or dual-hidden-layer with two inputs and one output. The root-mean-square error and maximum/minimum absolute errors of validation points, when comparing with the THD solutions, were used to evaluate the prediction accuracy of the ANNs.

It is demonstrated that a properly constructed ANN surrogate model can predict the THD lubrication performance almost instantly with accuracy adequately retained.

This study extends the use of ANNs to the applications other than the analyses dealing with experimental data. A similar procedure can be used to build a surrogate model for computationally intensive tribological models to have fast results. One of such applications is conducting extensive optimum design of tribological components or systems.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2020-0109/

The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing.

The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN.

The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models.

The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/