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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Kannan Murugesan, Kalaichelvan K., M.P. Jenarthanan and Sornakumar T.
The purpose of this paper is to investigate the use of embedded Shape Memory Alloy (SMA) nitinol wire for the enhancement of vibration and damping characteristics of…
Abstract
Purpose
The purpose of this paper is to investigate the use of embedded Shape Memory Alloy (SMA) nitinol wire for the enhancement of vibration and damping characteristics of filament-wound fiber-reinforced plastic composite hollow shafts.
Design/methodology/approach
The plain Glass Fiber-Reinforced Plastic (GFRP) and plain Carbon Fiber-Reinforced Plastic (CFRP) hollow shafts were manufactured by filament winding technique. Experimental modal analysis was conducted for plain hollow shafts of C1045 steel, GFRP and CFRP by subjecting them to flexural vibrations as per ASTM standard C747, with both ends clamped (C-C) end condition to investigate their vibration and damping behavior in terms of first natural frequency, damping time and damping ratio. Nitinol wires pre-stressed at various pre-strains (2, 4 and 6 per cent) were embedded with CFRP hollow shafts following same manufacturing technique, and similar experimental modal analysis was carried out by activating nitinol wires. The first natural frequencies of all the shaft materials were also predicted theoretically and compared with experimental measurements.
Findings
Among the three materials C1045 steel, plain GFRP and plain CFRP, the vibration and damping behavior were found to be the best for plain CFRP. Hence, CFRP shafts were considered for further improvement by embedding nitinol wires at pre-stressed condition. For CFRP shafts embedded with nitinol wires, the damping time decreased; and damping ratio and first natural frequency increased with increase in percentage of pre-strain. In comparison with plain CFRP, 7 per cent increase in first natural frequency and 100 per cent increase in damping ratio were observed for nitinol embedded CFRP shafts with 6 per cent pre-strain. Theoretical predictions of the first natural frequencies agree well with the experimental results for all the shaft materials.
Originality/value
The effect of nitinol on vibration and damping characteristics of filament wound hollow CFRP composite shafts with different pre-strains has not been studied extensively by the previous researchers. This paper addresses the effect of embedded nitinol wires pre-stressed at three varied pre-strains, that is, 2, 4 and 6 per cent on the vibration and damping characteristics of composite hollow CFRP shafts manufactured by filament winding technique.
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To solve the problem that the traditional methods miss key information in the process of bearing fault identification, this paper aims to apply the phase-space reconstruction…
Abstract
Purpose
To solve the problem that the traditional methods miss key information in the process of bearing fault identification, this paper aims to apply the phase-space reconstruction (PSR) theory and intelligent diagnosis techniques to extend the one-dimensional vibration signal to the high-dimensional phase space to reveal the system information implied in the univariate time series of the vibration signal.
Design/methodology/approach
In this paper, a new method based on the PSR technique and convolutional neural network (CNN) is proposed. First, the delay time and the embedding dimension are determined by the C-C method and the false nearest neighbors method, respectively. Through the coordinate delay reconstruction method, the two-dimensional signal is constructed, and this information is saved in a set of gray images. Then, a simple and efficient convolutional network is proposed. Finally, the phase diagrams of different states are used as samples and input into a two-dimensional CNN for learning modeling to construct a PSR-CNN fault diagnosis model.
Findings
The proposed PSR-CNN model is tested on two data sets and compared with support vector machine (SVM), k-nearest neighbor (KNN) and Markov transition field methods, and the comparison results showed that the method proposed in this paper has higher accuracy and better generalization performance.
Originality/value
The method proposed in this paper provides a reliable solution in the field of rolling bearing fault diagnosis.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2023-0113/
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Wei Liu, Hongyan Zhu and Wangzhen Li
The dynamic characteristics prediction and frequency-modulation of pipeline was an important work for the design of aircraft hydraulic structure.
Abstract
Purpose
The dynamic characteristics prediction and frequency-modulation of pipeline was an important work for the design of aircraft hydraulic structure.
Design/methodology/approach
A complex pipeline was deemed as a combination of several segments of straight-pipe-element (SPE). The 3D vibration equations of each SPE were established in their local coordinate system based on Timoshenko-beam model and Euler-beam model, respectively. The dynamic-stiffness-matrixes were deduced from the dispersion relation of these equations. According to the complex pipeline layout in the global coordinate system, a multi dynamic stiffness matrixes assembling (MDSMA) algorithm was carried out to establish the characteristic equations of the whole complex pipeline. The MDSMA solutions were verified to be consistent with experimental results.
Findings
The MDSMA method based on Timoshenko-Beam model was more suitable for the short span aviation pipeline and the vibration at high frequency stage (>350 Hz). The layout affected the pipeline's in-plane stiffness and out-plane stiffness, for the Z-shaped pipe, each order natural mode took place on the ZP and NP alternately. Reasonable designs of bending position and bending radius were effective means for complex pipeline frequency-modulation.
Originality/value
A new dynamic modeling method of aircraft complex pipeline was proposed to obtain the influence of pipeline layout parameters on dynamic characteristics.
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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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Sunghwan Ahn, Nakju Lett Doh, Wan Kyun Chung and Sang Yep Nam
The purpose of this paper is to describe research to enable a robust navigation of guide robots in erratic environments with partial sensor information.
Abstract
Purpose
The purpose of this paper is to describe research to enable a robust navigation of guide robots in erratic environments with partial sensor information.
Design/methodology/approach
Two techniques were developed. One is a robust node discrimination method by using an adaptive sensor matching method. The other is a robot navigation technique with partial sensor information.
Findings
A successful navigation was implemented in erratic environments using partial sensor information.
Originality/value
First robot navigation is addressed along the generalized Voronoi graph (GVG) with partial sensor information. A solution is also provided for a phantom node detection problem, which is one of the main defects in GVG navigation.
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Romeo Danielis and Mariangela Scorrano
The COVID-19 pandemic disrupted travel patterns, use of space and modal choice. Cities took actions in a way they did not before, trying to accommodate economic and travel needs…
Abstract
The COVID-19 pandemic disrupted travel patterns, use of space and modal choice. Cities took actions in a way they did not before, trying to accommodate economic and travel needs with the goal of reducing the spreading of the virus. Active travel (AT) played an important role in accommodating travel needs and in increasing the resilience and environmental friendliness of the urban transport system. As cities gradually return to their normal life, transport planners must decide which role to assign to AT in future urban plans. In particular, whether to confirm the temporary policies incentivising AT enacted to counteract the reduction in the use of public transport or to return to the previous road space allocation that dedicated considerable urban space to motorised vehicular traffic. After reviewing the empirical evidence on the AT evolution during the various pandemic phases and illustrating the main policies planned and implemented at city level in many countries, this chapter summarises the lessons learnt, derives some policy suggestions, and identifies future research needs.
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Selman Demirtas, Hakan Kaleli, Mahdi Khadem and Dae-Eun Kim
This study aims to investigate the tribological characteristics of a Napier-type second piston ring against a cylinder liner in the presence of graphene nano-additives mixed into…
Abstract
Purpose
This study aims to investigate the tribological characteristics of a Napier-type second piston ring against a cylinder liner in the presence of graphene nano-additives mixed into 5W40 fully synthetic engine oil.
Design/methodology/approach
Wear tests were carried out in the boundary lubrication condition using a reciprocating tribometer, and real engine tests were performed using a single spark ignition Honda GX 270 test engine for a duration of 75 h.
Findings
The experimental results of the tribometer tests revealed that the nano-additives formed a layer on the rubbed surfaces of both the piston ring and the cylinder liner. However, this layer was only formed at the top dead center of the cylinder liner during the engine tests. The accumulation of carbon (C) from the graphene was heavily detected on the rubbed surface of piston ring/cylinder liner, mixed with other additive elements such as Ca, Zn, S and P. Overall, the use of graphene nano-additives in engine oil was found to improve the frictional behavior in the boundary and mixed lubrication regimes. Abrasive wear was found to be the main mechanism occurring on the surface of both piston rings and cylinder liners.
Originality/value
Though many researchers have discussed the potential benefits of graphene as a nano-additive in oil to reduce the friction and wear in laboratory tests using tribometers, to date, no actual engine tests have been performed. In this paper, both tribometer and real engine tests were performed on a piston ring and cylinder liner using a fully formulated oil with and without graphene nano-additives in the boundary lubrication condition. It was found that a graphene nano-additive plays an active role in lowering the coefficient of friction and increasing surface protection and lubrication by forming a protective layer on the rubbing surfaces.
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Juan Romero-McCarthy, Antonio Casanueva-Fernández and Erika Daniela Garza-Leal
In implementing strategies to achieve ambitious goals, managers use tools such as performance measurement systems (PMS) for their proven ability to motivate and drive employees’…
Abstract
In implementing strategies to achieve ambitious goals, managers use tools such as performance measurement systems (PMS) for their proven ability to motivate and drive employees’ behaviors. However, many strategies fail during implementation, partly because managers pay insufficient attention to PMS design, tending to devote too little attention to characteristics of the metrics they use to evaluate and provide feedback on their subordinates’ performance. This chapter discusses the management control literature on metrics, the psychology behind the behavioral effects of measurements, typical managerial errors in choosing performance metrics, and previous attempts to define characteristics of good measurements. It suggests that good measurements should exhibit a set of characteristics associated with a novel and easily remembered acronym, STORY, and tests this typology by analyzing empirical data gathered on 1,159 metrics from 293 survey respondents, including characteristics of the people measured (e.g., age, position, and functional department) and the organizations employing them (e.g., firm size, industry, scope, and type of organization).
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Sujan Piya, Ahm Shamsuzzoha, Mohammed Khadem and Mahmoud Al Kindi
The purpose of this paper is to understand the drivers that create complexity in the supply chain and develop a mathematical model to measure the level of supply chain complexity…
Abstract
Purpose
The purpose of this paper is to understand the drivers that create complexity in the supply chain and develop a mathematical model to measure the level of supply chain complexity (SCC).
Design/methodology/approach
Through extensive literature review, the authors discussed various drivers of SCC. These drivers were classified into five dimensions based on expert opinion. Moreover, a novel hybrid mathematical model was developed by integrating analytical hierarchy process (AHP) and grey relational analysis (GRA) methods to measure the level of SCC. A case study was conducted to demonstrate the applicability of the developed model and analyze the SCC level of the company in the study.
Findings
The authors identified 22 drivers of SCC, which were further clustered into five complexity dimensions. The application of the developed model to the company in the case study showed that the SCC level of the company was 0.44, signifying that there was a considerable scope of improvement in terms of minimizing complexity. The company that serves as the focus of this case study mainly needs improvement in tackling issues concerning government regulation, internal communication and information sharing and company culture.
Originality/value
In this paper, the authors propose a model by integrating AHP and GRA methods that can measure the SCC level based on various complexity drivers. The combination of such methods, considering their ability to convert the inheritance and interdependence of drivers into a single mathematical model, is preferred over other techniques. To the best of the authors' knowledge, this is the first attempt at developing a hybrid multicriteria decision-based model to quantify SCC.