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Article

Akilu Yunusa-kaltungo and Jyoti K. Sinha

The purpose of this paper is mainly to highlight how a simplified and streamlined approach to the condition monitoring (CM) of industrial rotating machines through the…

Abstract

Purpose

The purpose of this paper is mainly to highlight how a simplified and streamlined approach to the condition monitoring (CM) of industrial rotating machines through the application of frequency domain data combination can effectively enhance the eMaintenance framework.

Design/methodology/approach

The paper commences by providing an overview to the relevance of maintenance excellence within manufacturing industries, with particular emphasis on the roles that rotating machines CM of rotating machines plays. It then proceeds to provide details of the eMaintenance as well as its possible alignment with the introduced concept of effective vibration-based condition monitoring (eVCM) of rotating machines. The subsequent sections of the paper respectively deal with explanations of data combination approaches, experimental setups used to generate vibration data and the theory of eVCM.

Findings

This paper investigates how a simplified vibration-based rotating machinery faults classification method based on frequency domain data combination can increase the feasibility and practicality of eMaintenance.

Research limitations/implications

The eVCM approach is based on classifying data acquired under several experimentally simulated conditions on two different machines using combined higher order signal processing parameters so as to reduce CM data requirements. Although the current study was solely based on the application of vibration data acquired from rotating machines, the knowledge exchange platform that currently dominates present day scientific research makes it very likely that the lessons learned from the development of eVCM concept can be easily transferred to other scientific domains that involve continuous CM such as medicine.

Practical implications

The concept of eMaintenance as a cost-effective and smart means of increasing the autonomy of maintenance activities within industries is rapidly growing in maintenance-related literatures. As viable as the concept appears, the achievement of its optimum objectives and full deployment to the industry is still subjective due to the complexity and data intensiveness of conventional CM practices. In this paper, an eVCM approach is proposed so that rotating machine faults can be effectively detected and classified without the need for repetitive analysis of measured data.

Social implications

The main strength of eVCM lies in the fact that it permits the sharing of historical vibration data between identical rotating machines irrespective of their foundation structures and speed differences. Since eMaintenance is concerned with driving maintenance excellence, eVCM can potentially contribute towards its optimisation as it cost-effectively streamlines faults diagnosis. This therefore implies that the simplification of vibration-based CM of rotating machines positively impacts the society with regard to the possibility of reducing how much time is actually spent on the accurate detection and classification of faults.

Originality/value

Although the currently existing body of literature already contains studies that have attempted to show how the combination of measured vibration data from several industrial machines can be used to establish a universal vibration-based faults diagnosis benchmark for incorporation into eMaintenance framework, these studies are limited in the scope of faults, severity and rotational speeds considered. In the current study, the concept of multi-faults, multi-sensor, multi-speed and multi-rotating machine data combination approach using frequency domain data fusion and principal components analysis is presented so that faults diagnosis features for identical rotating machines with different foundations can be shared between industrial plants. Hence, the value of the current study particularly lies in the fact that it significantly highlights a new dimension through which the practical implementation and operation of eMaintenance can be realized using big data management and data combination approaches.

Details

Journal of Quality in Maintenance Engineering, vol. 23 no. 3
Type: Research Article
ISSN: 1355-2511

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Article

P.K. Joshi, B. Gupta and P.S. Roy

The selection of wavelength region and number of bands is a research problem for remote sensing experts for utilization of data provided by the sensor system. The present…

Abstract

Purpose

The selection of wavelength region and number of bands is a research problem for remote sensing experts for utilization of data provided by the sensor system. The present study proposes to make an evaluation for optimum band selection and classification accuracy.

Design/methodology/approach

The entropy, brightness value overlap index (BVOI), optimum index factor (OIF) and spectral separability analysis, i.e. Euclidean distance (ED), divergence, transformed divergence (TD) and Jefferies‐Matusita (JM) distance and accuracy of MLC classification were carried out. For the present study Terra ASTER, Landsat ETM+ and IRS 1D LISS III dataset has been used. The first three methods were for the spectral evaluation of the three satellite data used and for determination of information content, variance and spectral overlap among the classes present in the natural and man‐made landscape. The fourth method is for selection of spectral band combinations with highest separability of classes using divergence matrices. These band combinations are selected for the classification and subsequent accuracy assessment.

Findings

The OIF values are clearly indicating that the performance of ASTER data is the best, having the lowest correlation between the bands; hence the separability of the feature is also highest, while LISS III have shown high correlation between the bands, with the poor separability of the features. Landsat ETM+ data are in between these two sensors, better than LISS III but poorer than ASTER. The BVOI outputs of the three datasets of man‐made landscape show that band 3 of ASTER has the least overlap of the classes, followed by band 4 of ETM+. Very high overlap of the classes has been found in LISS III data. It has been found from spectral separability analysis of all the three datasets for the man‐made landscape that ASTER data with band combination of spectral bands 123468 contains the highest value of all the measures of spectral separability, i.e. ED (291.72), divergence (2,133.37), TD (2,000.00) and JM distance (1,414.10).

Research limitations/implications

It can be inferred from the present study that spectral resolution plays a very important role in discrimination of vegetation features. ASTER data which are with the highest number of the bands amongst the satellite data used had shown highest classification accuracy, while LISS III data with lowest number of bands had shown lowest accuracy, and Landsat ETM+ stood in between the two sensors.

Practical implications

It is important to evaluate the sensor systems and their spectral regions for discrimination of vegetation features. The number of bands present in a particular sensor and the spectral regions used in it are some of the crucial factors which decide the usefulness of the data for different applications, including vegetation‐related studies. The selection of spectral wavelength region, i.e. spectral bands and the sensor system, presents the research problem for remote sensing experts to suggest the best spectral regions and satellite sensor for the discrimination of the vegetation features in different landscapes, namely man‐made and natural.

Originality/value

In the present study all the three datasets are extensively examined and tested for their vegetation discrimination capabilities using well‐established methodologies. All the parameters applied on the datasets revealed that spectral resolution definitely plays a role in the performance of the data as far as discrimination of features is concerned both in natural and man‐made landscape with desirable accuracy.

Details

Sensor Review, vol. 28 no. 1
Type: Research Article
ISSN: 0260-2288

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Article

Mireille D. Hubers, Cindy L. Poortman, Kim Schildkamp, Jules M. Pieters and Adam Handelzalts

In this study, Nonaka and Takeuchi’s socialization, externalization, combination and internalization (SECI) model of knowledge creation is used to gain insight into the…

Abstract

Purpose

In this study, Nonaka and Takeuchi’s socialization, externalization, combination and internalization (SECI) model of knowledge creation is used to gain insight into the process of knowledge creation in data teams. These teams are composed of school leaders and teachers, who work together to improve the quality of education. They collaboratively create knowledge related to data use and to an educational problem they are studying. The paper aims to discuss these issues.

Design/methodology/approach

A qualitative micro-process case study was conducted for two data teams. The modes, transitions and content of the knowledge creation process were analyzed for all data team meetings over a two-year period. In addition, all team members were interviewed twice to triangulate the findings.

Findings

Results show that the knowledge creation process was cyclical across meetings, but more iterative within meetings. Furthermore, engagement in the socialization and internalization mode provided added value in this process. Finally, the SECI model clearly differentiated between team members’ processes. Team members who engaged more often in the socialization and internalization modes and displayed more personal engagement in those modes gained greater and deeper knowledge.

Research limitations/implications

The SECI model is valuable for understanding how teams gain new knowledge and why they differ in those gains.

Practical implications

Stimulation of active personal engagement in the socialization and internalization mode is needed.

Originality/value

This is one of the first attempts to concretely observe the process of knowledge creation. It provides essential insights into what educators do in professional development contexts, and how support can best be provided.

Details

Journal of Professional Capital and Community, vol. 1 no. 1
Type: Research Article
ISSN: 2056-9548

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Article

Hsu‐Hwa Chang

Robust parameter design is conventionally analyzed by means of statistical techniques. However, the statistical‐based approach is inefficient when optimizing a dynamic…

Abstract

Purpose

Robust parameter design is conventionally analyzed by means of statistical techniques. However, the statistical‐based approach is inefficient when optimizing a dynamic system in regards to quantitative control factors and missing observations. The aim of this paper is to propose an alternative approach based on data mining tools to model and optimize dynamic robust design with missing data.

Design/methodology/approach

A three‐phase approach based on data mining techniques is proposed. First, a back‐propagation network is trained to construct the response model of a dynamic system. Second, three formulas of performance measures are developed to evaluate the predicted responses of the response model. Finally, a genetic algorithm is then performed to obtain the optimal parameter combination via the response model.

Findings

The proposed approach is capable of dealing with both qualitative and quantitative control factors for dynamic systems as well as static systems. In addition, the proposed approach can efficiently treat parameter experiments with missing data. The proposed approach is demonstrated with a numerical example. Results show that this three‐phase data mining approach outperforms the conventional statistic‐based approaches.

Originality/value

This work provides a relatively effective approach to optimize the three types of dynamic robust parameter design. Performing the approach, practitioners do not require much background in statistics but instead rely on their knowledge of engineering.

Details

International Journal of Quality & Reliability Management, vol. 24 no. 6
Type: Research Article
ISSN: 0265-671X

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Article

Pin Shen Teh, Ning Zhang, Andrew Beng Jin Teoh and Ke Chen

The use of mobile devices in handling our daily activities that involve the storage or access of sensitive data (e.g. on-line banking, paperless prescription services…

Abstract

Purpose

The use of mobile devices in handling our daily activities that involve the storage or access of sensitive data (e.g. on-line banking, paperless prescription services, etc.) is becoming very common. These mobile electronic services typically use a knowledge-based authentication method to authenticate a user (claimed identity). However, this authentication method is vulnerable to several security attacks. To counter the attacks and to make the authentication process more secure, this paper aims to investigate the use of touch dynamics biometrics in conjunction with a personal identification number (PIN)-based authentication method, and demonstrate its benefits in terms of strengthening the security of authentication services for mobile devices.

Design/methodology/approach

The investigation has made use of three light-weighted matching functions and a comprehensive reference data set collected from 150 subjects.

Findings

The investigative results show that, with this multi-factor authentication approach, even when the PIN is exposed, as much as nine out of ten impersonation attempts can be successfully identified. It has also been discovered that the accuracy performance can be increased by combining different feature data types and by increasing the input string length.

Originality/value

The novel contributions of this paper are twofold. Firstly, it describes how a comprehensive experiment is set up to collect touch dynamics biometrics data, and the set of collected data is being made publically available, which may facilitate further research in the problem domain. Secondly, the paper demonstrates how the data set may be used to strengthen the protection of resources that are accessible via mobile devices.

Details

International Journal of Pervasive Computing and Communications, vol. 12 no. 1
Type: Research Article
ISSN: 1742-7371

Keywords

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Article

T.P. Williams

Competitively bid construction projects are often completed for amounts greater than the original low‐bid amount. Improved methods of predicting a projects potential to…

Abstract

Purpose

Competitively bid construction projects are often completed for amounts greater than the original low‐bid amount. Improved methods of predicting a projects potential to experience large cost overruns would be useful to government agencies for planning capital budgets. The purpose of this paper is to explore the application of treesmaps to the analysis of competitively bid project cost overruns.

Design/methodology/approach

In this research a data visualization technique called treemaps was explored as a method of describing the nature of the bids for highway construction projects, and as a method of relating the characteristics of a projects bids to the observed increase in completed project cost. Ratios were calculated that measured bid characteristics for projects from Texas and California. Treemaps were constructed that related the level of the bidding ratios to the level of the difference between the completed project and the original low‐bid amount

Findings

It was found that the treemaps indicated that projects with high‐ratio values typically experienced a larger weighted average percentage difference between the low bid and completed project cost than projects with low ratio values.

Research limitations/implications

Future research should include consideration of other project factors in the treemaps, and testing of the treemaps to determine their usefulness to practitioners.

Practical implications

Treemaps present a way of understanding complex relationships between construction bidding data and the cost increases that occur during construction without the need to construct complex mathematical models.

Originality/value

The paper shows that bidding ratios can be used as indicators of a projects potential for cost overruns. The paper presents a new way of visualizing the construction bidding ratio data.

Details

Construction Innovation, vol. 7 no. 4
Type: Research Article
ISSN: 1471-4175

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Article

Taija Turunen, Ville Eloranta and Esko Hakanen

This paper aims to analyze the debate related to the strategic role of information in the industrial service business, that is, whether information is a resource that…

Abstract

Purpose

This paper aims to analyze the debate related to the strategic role of information in the industrial service business, that is, whether information is a resource that could and should be protected. The connection between manufacturers’ servitization and management strategy literature is used in the analysis.

Design/methodology/approach

A qualitative case study on five new entrants to the industrial service market.

Findings

The results of the study provide new insights on both the characteristics and boundary conditions of new entrants’ approaches to strategically benefitting from information resources. Instead of aiming to possess and control data, the case companies prefer the access to large data volumes over exclusive access, render the question of data ownership to be largely irrelevant and perceive that the strategic relevance of information lies in novel data combinations.

Practical implications

The study provides a contemporary perspective on the prevailing information resource protection doctrine in the context of industrial services. Most importantly, the results challenge the hitherto unquestionable strategic relevance of customer relationships in Internet of Things (IoT)-driven service businesses. Furthermore, the results identify the need for flexible organizational structures that aim to leverage the complexity of the market environment.

Originality/value

Through providing a theoretically grounded and empirically backed contemporary perspective on the role of information in IoT-driven service businesses, the study expands the strategic understanding of industrial service providers.

Details

Journal of Business & Industrial Marketing, vol. 33 no. 6
Type: Research Article
ISSN: 0885-8624

Keywords

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Article

Joseph Cooper, Carl Zulauf, Michael Langemeier and Gary Schnitkey

Farm level data are essential to accurate setting of crop insurance premium rates, but their time series tends to be too short to allow them to be the sole data source…

Abstract

Purpose

Farm level data are essential to accurate setting of crop insurance premium rates, but their time series tends to be too short to allow them to be the sole data source. County level data are available in longer time series, however. The purpose of this paper is to present a methodology to make full use of the information inherent in each of these data sets.

Design/methodology/approach

The paper uses a novel application of statistical tools for using farm and county level yield data to generate farm level yield densities that explicitly incorporate within county yield heterogeneity while accounting for systemic risk and other spatial or intertemporal correlations among farms within the county.

Findings

The empirical analysis shows that current approaches used by the Risk Management Agency to individualize premiums for a farm result in substantial mispricing of crop insurance premiums because they do not adequately capture farm yield variability and yield correlations between farms. The new premium setting method is empirically shown to substantially reduce government subsidies for crop insurance premiums.

Originality/value

The paper demonstrates how to extract more information from available data when setting crop insurance premiums, which allows the government to more closely tailor premiums to the farm than do current approaches.

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Book part

Caroline Bayart, Patrick Bonnel and Catherine Morency

Data fusion and the combination of multiple data sources have been part of travel survey processes for some time. In the current context, where technologies and…

Abstract

Data fusion and the combination of multiple data sources have been part of travel survey processes for some time. In the current context, where technologies and information systems spread and become more and more diverse, the transportation community is getting more and more interested in the potential of data fusion processes to help gather more complete datasets and help give additional utility to available data sources. Research is looking for ways to enhance the available information by using both various data collection methods and data from various sources, surveys or observation systems. Survey response rates are decreasing over the world, and combining survey modes appears to be an interesting way to address this problem. Letting interviewees choose their survey mode allows increasing response rates, but survey mode could impact the data collected. This paper first discusses issues rising when combining survey modes within the same survey and presents a method to merge the data coming from different survey modes, in order to consolidate the database. Then, it defines and describes the data fusion process and discusses how it can be relevant for transportation analysis and modelling purposes. Benefiting from the availability of various datasets from the Greater Montréal Area and the Greater Lyon Area, some applications of data fusion are constructed and/or reproduced to illustrate and test some of the methods described in the literature.

Details

Transport Survey Methods
Type: Book
ISBN: 978-1-84-855844-1

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Article

Jodi Vosburg and Anil Kumar

The integrity of the data used to operate and make decisions about a business affects the relative efficiency of operations and quality of decisions made. Protecting that…

Abstract

The integrity of the data used to operate and make decisions about a business affects the relative efficiency of operations and quality of decisions made. Protecting that integrity can be difficult and becomes more difficult as the size and complexity of the business and its systems increase. Recovering data integrity may be impossible once it is compromised. Stewards of transactional and planning systems must therefore employ a combination of procedures including systematic safeguards and user‐training programs to counteract and prevent dirty data in those systems. Users of transactional and planning systems must understand the origins and effects of dirty data and the importance of and means of guarding against it. This requires a shared understanding within the context of the business of the meaning, uses, and value of data across functional entities. In this paper, we discuss issues related to the origin of dirty data, associated problems and costs of using dirty data in an organization, the process of dealing with dirty data in a migration to a new system: enterprise resource planning (ERP), and the benefits of an ERP in managing dirty data. These issues are explored in the paper using a case study.

Details

Industrial Management & Data Systems, vol. 101 no. 1
Type: Research Article
ISSN: 0263-5577

Keywords

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