The ability to build up a film thickness separating the contacts in the bearing is essential to assure long service life of rolling element bearings. Rolling element bearings used…
Abstract
Purpose
The ability to build up a film thickness separating the contacts in the bearing is essential to assure long service life of rolling element bearings. Rolling element bearings used in refrigeration suffer from poor film thickness due to decreased viscosity of the lubricant by the dilution of refrigerant in the oil. The purpose of this paper is to redesign a bearing test apparatus equipped with a capacitance measuring device able to monitor the lubrication status in the bearing online and include experiments to verify the capacitance measuring technique.
Design/methodology/approach
The objective is to design a complete system to study film build up in rolling element bearings in a refrigerant environment and to use and evaluate the capacitance/resistance measuring technique.
Findings
The investigation shows good correlation between denting on tested bearings and the identified contacts by the capacitance measuring apparatus. The method is also useful when studying lift‐off or run‐in of a bearing.
Originality/value
In this paper, a bearing test apparatus is redesigned and equipped with a capacitance measuring device able to monitor the lubrication status in the bearing on‐line. The paper includes experiments to verify the capacitance measuring technique.
Details
Keywords
Misael Lopez-Ramirez, Rene J. Romero-Troncoso, Daniel Moriningo-Sotelo, Oscar Duque-Perez, David Camarena-Martinez and Arturo Garcia-Perez
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Originality/value
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.
Details
Keywords
Giulia Leoni, Alessandro Lai, Riccardo Stacchezzini, Ileana Steccolini, Stephen Brammer, Martina Linnenluecke and Istemi Demirag
This paper introduces the second part of a AAAJ special issue on accounting, accountability and management during the COVID-19 emergency. The authors analyse the themes that…
Abstract
Purpose
This paper introduces the second part of a AAAJ special issue on accounting, accountability and management during the COVID-19 emergency. The authors analyse the themes that emerge from the second part of the special issue, which allows us to identify the diverse accounting and accountability practices across different geographical and organisational contexts. The authors also provide an overall picture of the contributions of the special issue, with insights into avenues of future research.
Design/methodology/approach
Building on the first part of the AAAJ special issue, the paper draws together and identifies additional emerging themes related to research into the COVID-19 pandemic and how it impacts accounting, accountability and management practices. The authors reflect on the contributions of the special issue to the interdisciplinary accounting research project.
Findings
The authors identify two macro-themes and outline their contributions to the accounting literature. The first deals with the changes and dangers of accounting and accountability practices during the pandemic. The second considers accountability practices in a broader sense, including reporting, disclosure and rhetorical practices in the management of COVID-19.
Practical implications
The paper shows the pervasive role of accounting and accountability in the unprecedented and indiscriminate health crisis of COVID-19. It highlights the important role of special issues in producing timely research that responds to unfolding events.
Originality/value
This paper contributes to current debates on the roles of accounting and accountability during COVID-19 by drawing together the themes of the special issue and identifying future interdisciplinary accounting research on the pandemic's aftermath.