A. Hajnayeb, S.E. Khadem and M.H. Moradi
This paper aims to improve the performance and speed of artificial neural network (ANN)‐ball‐bearing fault detection expert systems by eliminating unimportant inputs and changing…
Abstract
Purpose
This paper aims to improve the performance and speed of artificial neural network (ANN)‐ball‐bearing fault detection expert systems by eliminating unimportant inputs and changing the ANN structure.
Design/methodology/approach
An algorithm is used to select the best subset of features to boost the success of detecting healthy and faulty ball. Some of the important parameters of the ANN are also optimized to make the classifier achieve the maximum performance.
Findings
It was found that better accuracy can be obtained for ANN with fewer inputs.
Research limitations/implications
The method can be used for other machinery condition‐monitoring systems which are based on ANN.
Practical implications
The results are useful for bearing fault detection systems designers and quality check centers in bearing manufacturing companies.
Originality/value
The algorithm used in this research is faster than in previous studies. Changing ANN parameters improved the results. The system was examined using experimental data of ball‐bearings.
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A major role of facilities management is ensuring the useability, reliability, and safety of the asset being managed. To achieve this management must use a system to control the…
Abstract
Purpose
A major role of facilities management is ensuring the useability, reliability, and safety of the asset being managed. To achieve this management must use a system to control the maintenance function. The purpose of the paper is to identify and describe the various maintenance management models and systems available for facilities managers to consider.
Design/methodology/approach
Two comprehensive reviews of the literature were undertaken to categorise the various maintenance management models and identify popular models in practice.
Findings
The review identified 37 maintenance management models. From these, four were found to be popular: total productive maintenance (TPM), condition-based maintenance (CBM), reliability-centred maintenance (RCM), and condition monitoring (CM). While many thousands of papers can be found of these four models, the support in the literature for the remaining 33 models is very limited.
Research limitations/implications
While providing a sound foundation for future research, the papers findings are based solely on reviewing literature.
Practical implications
For facilities managers seeking to expand their knowledge of a particular model or maintenance management systems in general, the paper provides a practical understanding.
Originality/value
Papers focused solely on identifying and describing maintenance management models are scarce and this paper makes a concerted attempt to link academic research with management practitioners.
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Arnt O. Hopland and Sturla Kvamsdal
There is widespread and long-lasting worry related to the condition of public purpose buildings and public investments. Public buildings make up a huge capital stock and proper…
Abstract
Purpose
There is widespread and long-lasting worry related to the condition of public purpose buildings and public investments. Public buildings make up a huge capital stock and proper maintenance and investments are important for public policy. Notwithstanding, the relevant research literature is fragmented and spread across several fields. The authors take stock of earlier and more recent research and suggest some ideas for future research.
Design/methodology/approach
The authors summarize the relevant literature and discuss implications of various theoretical assumptions and empirical findings for maintenance and investment strategies.
Findings
A better understanding of the role of public facilities in public service provision is important. Relevant topics for further research are the impact of technological changes, both in buildings and service provision, economic issues including macroeconomic shocks and trends that influence public funding and demand for public services, and advancing maintenance scheduling models to consider a portfolio of facilities. Further, the empirical literature suffers from a lack of relevant data to gauge both the condition of public facilities and their impact on public services.
Originality/value
There is widespread worry that poor facilities adversely impact public services, but the size and significance of this impact are an open question. This paper contributes by taking stock of the existing research on public facilities, maintenance, and investments, and suggest ideas for further work.
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Xiaofeng Li, Xiaoxue Liu, Xiangwei Li, Weidong He and Hanfei Guo
The purpose of this paper is to propose an improved method which can shorten the calculation time and improve the calculation efficiency under the premise of ensuring the…
Abstract
Purpose
The purpose of this paper is to propose an improved method which can shorten the calculation time and improve the calculation efficiency under the premise of ensuring the calculation accuracy for calculating the response of dynamic systems with periodic time-varying characteristics.
Design/methodology/approach
An improved method is proposed based on Runge–Kutta method according to the composition characteristics of the state space matrix and the external load vector formed by the reduction of the dynamic equation of the periodic time-varying system. The recursive scheme of the holistic matrix of the system using the Runge–Kutta method is improved to be the sub-block matrix that is divided into the upper and lower parts to reduce the calculation steps and the occupied computer memory.
Findings
The calculation time consumption is reduced to a certain extent about 10–35% by changing the synthesis method of the time-varying matrix of the dynamics system, and the method proposed of paper consumes 43–75% less calculation time in total than the original Runge–Kutta method without affecting the calculation accuracy. When the ode45 command that implements the Runge–Kutta method in the MATLAB software used to solve the system dynamics equation include the time variable which cannot provide its specific analytic function form, so the time variable value corresponding to the solution time needs to be determined by the interpolation method, which causes the calculation efficiency of the ode45 command to be substantially reduced.
Originality/value
The proposed method can be applied to solve dynamic systems with periodic time-varying characteristics, and can consume less calculation time than the original Runge–Kutta method without affecting the calculation accuracy, especially the superiority of the improved method of this paper can be better demonstrated when the degree of freedom of the periodic time-varying dynamics system is greater.
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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.
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Soumava Boral, Sanjay Kumar Chaturvedi and V.N.A. Naikan
Usually, the machinery in process plants is exposed to harsh and uncontrolled environmental conditions. Even after taking different types of preventive measures to detect and…
Abstract
Purpose
Usually, the machinery in process plants is exposed to harsh and uncontrolled environmental conditions. Even after taking different types of preventive measures to detect and isolate the faults at the earliest possible opportunity becomes a complex decision-making process that often requires experts’ opinions and judicious decisions. The purpose of this paper is to propose a framework to detect, isolate and to suggest appropriate maintenance tasks for large-scale complex machinery (i.e. gearboxes of steel processing plant) in a simplified and structured manner by utilizing the prior fault histories available with the organization in conjunction with case-based reasoning (CBR) approach. It is also demonstrated that the proposed framework can easily be implemented by using today’s graphical user interface enabled tools such as Microsoft Visual Basic and similar.
Design/methodology/approach
CBR, an amalgamated domain of artificial intelligence and human cognitive process, has been applied to carry out the task of fault detection and isolation (FDI).
Findings
The equipment failure history and actions taken along with the pertinent health indicators are sufficient to detect and isolate the existing fault(s) and to suggest proper maintenance actions to minimize associated losses. The complex decision-making process of maintaining such equipment can exploit the principle of CBR and overcome the limitations of the techniques such as artificial neural networks and expert systems. The proposed CBR-based framework is able to provide inference with minimum or even with some missing information to take appropriate actions. This proposed framework would alleviate from the frequent requirement of expert’s interventions and in-depth knowledge of various analysis techniques expected to be known to process engineers.
Originality/value
The CBR approach has demonstrated its usefulness in many areas of practical applications. The authors perceive its application potentiality to FDI with suggested maintenance actions to alleviate an end-user from the frequent requirement of an expert for diagnosis or inference. The proposed framework can serve as a useful tool/aid to the process engineers to detect and isolate the fault of large-scale complex machinery with suggested actions in a simplified way.
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Muhammad Imran Qureshi, Mehwish Iftikhar, Yasmine Muhammad Javaid Iqbal, Chaudry Bilal Ahmad Khan and Jia Liu
Despite the growing interest in closed-loop manufacturing, there is a lack of comprehensive frameworks that integrate product development, production processes, people and…
Abstract
Purpose
Despite the growing interest in closed-loop manufacturing, there is a lack of comprehensive frameworks that integrate product development, production processes, people and policies (4Ps) to optimize sustainable manufacturing performance. This study investigates the influence of the four Ps of closed-loop manufacturing systems (product development, production processes, people and policies) on sustainable manufacturing performance (SMP).
Design/methodology/approach
To investigate the influence of the four Ps on SMP, a hybrid analytical model was employed, combining structural equation modeling (SEM) with artificial neural networks (ANN). Data were collected through a structured survey administered to 353 manufacturing firms in Malaysia. SEM was used to assess the relationships between the variables, while ANN was employed to capture nonlinear relationships and improve prediction accuracy.
Findings
The research findings demonstrate that product development practices, including eco-design, life cycle assessment and resource planning, exert the most significant influence on SMP. Furthermore, implementing green and lean manufacturing techniques, energy modeling and material utilization/toxicity planning significantly enhances sustainability outcomes. While the social setting (employee motivation, turnover and work–life quality) does not directly impact SMP, it plays a pivotal role in facilitating the implementation of internal environmental policies. Moreover, environmental management practices, both mandatory and voluntary, serve as intermediaries between the four Ps and SMP within closed-loop manufacturing systems.
Practical implications
The findings offer valuable insights for policymakers, industry leaders and manufacturing organizations. By prioritizing product development, implementing green and lean manufacturing practices and fostering a positive social setting, organizations can significantly enhance their sustainable performance. Additionally, the study highlights the importance of effective environmental management practices in mediating the relationship between other factors and SMP.
Originality/value
This study contributes to the literature by providing a comprehensive framework for understanding the factors that drive sustainable manufacturing performance. The hybrid SEM-ANN model offers a robust and innovative approach to analyzing the complex relationships between the four Ps and SMP.