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Article
Publication date: 15 October 2021

Qiang Li, Sifeng Liu and Saad Ahmed Javed

The purpose of this paper is to develop a new approach for equipment states prediction and provide a method for early warning of possible trouble states.

Abstract

Purpose

The purpose of this paper is to develop a new approach for equipment states prediction and provide a method for early warning of possible trouble states.

Design/methodology/approach

A new two-stage multi-level equipment state classification system was proposed to forecast equipment operation status. The first stage involves predicting the equipment's normal state, and the second stage involves forecasting the equipment's abnormal status. Meanwhile, the equipment state classification is done according to the manufacturing company's internal specifications to define various equipment statuses. Then, the trouble state and waiting state were predicted by grey state prediction model.

Findings

A new two-stage multi-level equipment status classification system and a new approach for equipment states prediction has been proposed in this paper.

Practical implications

The application on a real-world case shown that the model is very effective for predicting equipment state. The equipment's major failure risk can be reduced significantly.

Originality/value

The proposed approach can help improve the effective prediction of the equipment's various operation states and reduce the equipment's major failure risk and thus maintenance costs.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 8 September 2021

Odey Alshboul, Ali Shehadeh, Maha Al-Kasasbeh, Rabia Emhamed Al Mamlook, Neda Halalsheh and Muna Alkasasbeh

Heavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours…

Abstract

Purpose

Heavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours and other macroeconomic gauges. The main objective of this study is to predict the residual value of the main types of heavy construction equipment. The residual value of heavy construction equipment is predicted via deep learning (DL) and machine learning (ML) approaches.

Design/methodology/approach

Based on deep and machine learning regression network integrated with data mining, random forest (RF), decision tree (DT), deep neural network (DNN) and linear regression (LR)-based modeling decision support models are developed. This research aims to forecast the residual value for different types of heavy construction equipment. A comprehensive investigation of publicly accessible auction data related to various types and categories of construction equipment was utilized to generate the model's training and testing datasets. In total, four performance metrics (i.e. the mean absolute error (MAE), mean squared error (MSE), the mean absolute percentage error (MAPE) and coefficient of determination (R2)) were used to measure and compare the developed algorithms' accuracy.

Findings

The developed algorithm's efficiency has been demonstrated by comparing the deep and machine learning predictions with real residual value. The accuracy of the results obtained by different proposed modeling techniques was comparable based on the performance evaluation metrics. DT shows the highest accuracy of 0.9111 versus RF with an accuracy of 0.8123, followed by DNN with an accuracy of 0.7755 and the linear regression with an accuracy of 0.5967.

Originality/value

The proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 5 August 2021

Youn Ji Lee, Hyuk Jun Kwon, Yujin Seok and Sang Jeen Hong

The purpose of this paper is to demonstrate industrial Internet of Things (IIoT) solution to improve the equipment condition monitoring with equipment status data and…

Abstract

Purpose

The purpose of this paper is to demonstrate industrial Internet of Things (IIoT) solution to improve the equipment condition monitoring with equipment status data and process condition monitoring with plasma optical emission spectroscopy data, simultaneously. The suggested research contributes e-maintenance capability by remote monitoring in real time.

Design/methodology/approach

Semiconductor processing equipment consists of more than a thousand of components, and unreliable condition of equipment parts leads to the failure of wafer production. This study presents a web-based remote monitoring system for physical vapor deposition (PVD) systems using programmable logic controller (PLC) and Modbus protocol. A method of obtaining electron temperature and electron density in plasma through optical emission spectroscopy (OES) is proposed to monitor the plasma process. Through this system, parts that affect equipment and processes can be controlled and properly managed. It is certainly beneficial to improve the manufacturing yield by reducing errors from equipment parts.

Findings

A web-based remote monitoring system provides much of benefits to equipment engineers to provide equipment data for the equipment maintenance even though they are physically away from the equipment side. The usefulness of IIoT for the e-maintenance in semiconductor manufacturing domain with the in situ monitoring of plasma parameters is convinced. The authors found the average electron temperature gradually with the increase of Ar carrier gas flow due to the increased atomic collisions in PVD process. The large amount of carrier gas flow, in this experimental case, was 90 sccm, dramatically decreasing the electron temperature, which represents kinetic energy of electrons.

Research limitations/implications

Semiconductor industries require high level of data security for the protection of their intellectual properties, and it also falls into equipment operational condition; however, data security through the Internet communication is not considered in this research, but it is already existing technology to be easily adopted by add-on feature.

Practical implications

The findings indicate that crucial equipment parameters are the amount of carrier gas flow rate and chamber pressure among the many equipment parameters, and they also affect plasma parameters of electron temperature and electron density, which directly affect the quality of metal deposition process result on wafer. Increasing the gas flow rate beyond a certain limit can yield the electron temperature loss to have undesired process result.

Originality/value

Several research studies on data mining with semiconductor equipment data have been suggested in semiconductor data mining domain, but the actual demonstration of the data acquisition system with real-time plasma monitoring data has not been reported. The suggested research is also valuable in terms of high cost and complicated equipment manufacturing.

Details

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

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Article
Publication date: 1 March 2001

Carol S. Brinkman and Amanda M. Roubieu

In today’s academic library, the reference department relies heavily on computer workstations to provide patrons with access to reference sources in CD‐ROM and Web…

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5288

Abstract

In today’s academic library, the reference department relies heavily on computer workstations to provide patrons with access to reference sources in CD‐ROM and Web formats. Many reference departments also supervise an electronic classroom which is used to provide hands‐on instruction. Planning for the hardware, software, and peripherals necessary to provide patrons with access and training must be an ongoing process in order to keep up with rapid technological changes, both in computer hardware and software applications. Through the maintenance of comprehensive records of existing equipment, including the purpose, capabilities and maintenance of each item, information will be readily available for use in planning for computer equipment. In this article, the authors discuss various types of records that should be kept for computer equipment and how the information contained in these records can be applied to ongoing planning and decision making for management and maintenance.

Details

Reference Services Review, vol. 29 no. 1
Type: Research Article
ISSN: 0090-7324

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Article
Publication date: 8 May 2007

Thanapun Prasertrungruang and B.H.W. Hadikusumo

This study is intended to investigate the current practices and problems in heavy equipment management as well as to identify practices capable of alleviating equipment

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1439

Abstract

Purpose

This study is intended to investigate the current practices and problems in heavy equipment management as well as to identify practices capable of alleviating equipment management problems for highway contractors in Thailand.

Design/methodology/approach

Equipment management practices were identified and analysed by SPSS using a questionnaire survey. ANOVA test was used to reveal significant differences in equipment management practices among different contractor sizes. Relationships between equipment management practices and problems were also revealed.

Findings

The equipment management practices vary, to some extent, among different contractor sizes. While practices of medium and small contractors tend to be similar, practices of large contractors are different from those of smaller contractors. Large contractors often put more emphasis on outsourcing strategy for equipment management. Moreover, large contractors frequently dispose of or replace equipment as soon as the equipment becomes inefficient before incurring high repair costs. Conversely, smaller contractors tend to mainly emphasise on the company finance and the budget availability as they often rely on purchasing strategy, especially buying used machines. Overall, equipment practices of large contractors were found to be more successful than smaller contractors in minimising equipment management problems, including long downtime duration and cost.

Originality/value

This research is of value for better understanding practices and problems relating to heavy equipment management among different contractor sizes. The study also highlights practices that are capable of reducing problems relating to heavy equipment management for highway contractors.

Details

Engineering, Construction and Architectural Management, vol. 14 no. 3
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 9 May 2016

Anil Rana

The purpose of the paper is to provide a method for selection of an optimum level of repair by replacement of an equipment based on its cost. In a ship where the engineer…

Abstract

Purpose

The purpose of the paper is to provide a method for selection of an optimum level of repair by replacement of an equipment based on its cost. In a ship where the engineer has a vast variety of equipment and systems to operate and maintain within limited time frames and availability of human resources, it is often difficult to disassemble a whole equipment to replace a faulty component. It is instead a lot easier to just replace the faulty equipment with whole new equipment. However, such a decision comes at an enormous capital cost. Therefore, the key question is, can we have a model to help us arrive at a decision on the correct level of carrying out repairs?

Design/methodology/approach

The paper uses a model based on cost and convolution of failure distributions of critical sub-components of an equipment. Necessary assumptions based on real life experience have been incorporated in the model.

Findings

The paper used an example of a particular type of motor driven sea water centrifugal pump which was commonly used in main engine sea water system, firefighting system, air conditioning system, etc. The pump had one of the highest failure rates in the ship (approximately one failure per 150 days) and the engineers found it cost and time effective to replace the entire pump on failure rather than carrying out replacement of the failed components. The model analyzed that the engineer’s hunch was not off the mark.

Research limitations/implications

The implication of the work presented in the paper will be savings in maintenance cost and downtime due to optimal level of repairs on a multi-component equipment. The limitations of the work are assumption of independence of failures of components. This may not be true in all the cases. Further, opportunity based maintenance has also not been considered.

Originality/value

The originality of the paper lies in the presentation of a method for selection of an optimum level of maintenance for a multi-component equipment

Details

Journal of Quality in Maintenance Engineering, vol. 22 no. 2
Type: Research Article
ISSN: 1355-2511

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Article
Publication date: 18 September 2007

Sri Beldona and Vernon E. Francis

To develop, test and implement a sampling strategy for equipment auditing for a Fortune 100 company.

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3542

Abstract

Purpose

To develop, test and implement a sampling strategy for equipment auditing for a Fortune 100 company.

Design/methodology/approach

Regression analysis is applied to auditing of equipment for a large US corporation. Empirical data and test data sets are used to evaluate the efficacy of using regression for auditing and to determine reasonable and efficient sample sizes to be employed across more than 5,000 locations.

Findings

Regression is a viable and useful method for equipment auditing when there is anticipated high correlation between pre‐ and post‐audit equipment value. Recommended sample size is dependent upon the size of the location as measured by total pieces of equipment. Decision rules combining acceptable tolerance limits, desired confidence level and sample size are provided.

Research limitations/implications

The method, recommended sample sizes and decision rules are particularly applicable to instances where high correlation is expected between pre‐ and post‐audit equipment values. Standard regression assumptions are not all met in all instances, especially with small sample sizes.

Practical implications

The regression approach and model, sample size recommendations and decision rules for passing or failing an equipment audit described herein have been implemented at a Fortune 100 company, and are generally applicable to equipment and inventory auditing when high correlation between pre‐ and post‐audit equipment is expected.

Originality/value

This paper provides a practical and useful regression‐based approach to sampling for equipment auditing. Recommended sample sizes and decision rules for passing or failing the audit are explicitly defined.

Details

Managerial Auditing Journal, vol. 22 no. 8
Type: Research Article
ISSN: 0268-6902

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Article
Publication date: 26 October 2012

Hong‐fa Ke, Hong‐Mei Du, Ke He and Xiao‐Hong Yu

The purpose of this paper is to solve the comprehensive evaluation of the equipment maintainability level based on grey system theory, and make an analysis of the…

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320

Abstract

Purpose

The purpose of this paper is to solve the comprehensive evaluation of the equipment maintainability level based on grey system theory, and make an analysis of the corresponding influencing factors and their prioritization process.

Design/methodology/approach

Considering the diversity, uncertainty and small sample size of the influencing factors of the equipment maintainability level, a multilayer evaluation attribute system is set up, and the grey relational method is utilized to assess the equipment's comprehensive maintainability. First, the bottom layer relational coefficient and weighted relational degree are analyzed, and, by means of the focus of relational degree through the bottom layer to top layer, the general evaluation of the equipment maintainability is carried out. Second, the equipment maintainability level and its influencing factors model, i.e. GM(1,N) model are set up, and the prioritization of the influencing factors is achieved through the comparison of the size of the model drive coefficients. Finally, the practical example calculation results show that this method has not only realized a sensible and effective evaluation of the equipment maintainability level, but also provided a prioritization of the influencing factors, which helps to focus attention on the major influencing factors and make this method of significant engineering application value in the improvement of the equipment maintainability level.

Findings

The modeling of electronic equipment maintainability level and analysis of its corresponding practical example prove that grey system theory could not only perform a comprehensive evaluation of the equipment maintainability level, but also provide a quantitative analysis of its various influencing factors, whereas, other methods such as fuzzy mathematics, etc. can only make a general evaluation of the equipment maintainability level.

Practical implications

This paper has realized an integral evaluation of the equipment maintainability level and has made an analysis of the prioritization of its various influencing factors. These investigation results could be introduced as a promising innovative idea in the evaluation of the equipments' other performances and the prioritization of its various corresponding influencing factors.

Originality/value

Considering the diversity and uncertainty of influencing factors of the equipment maintainability level, this paper has realized a multilayer evaluation attribute system to perform a comprehensive evaluation of equipment maintainability level by means of weighted grey relational degree model. Furthermore, the prioritization of its various influencing factors is achieved based on the GM(1,N) model.

Details

Grey Systems: Theory and Application, vol. 2 no. 3
Type: Research Article
ISSN: 2043-9377

Keywords

Content available
Article
Publication date: 24 June 2021

Haosen Liu, Youwei Wang, Xiabing Zhou, Zhengzheng Lou and Yangdong Ye

The railway signal equipment failure diagnosis is a vital element to keep the railway system operating safely. One of the most difficulties in signal equipment failure…

Abstract

Purpose

The railway signal equipment failure diagnosis is a vital element to keep the railway system operating safely. One of the most difficulties in signal equipment failure diagnosis is the uncertainty of causality between the consequence and cause for the accident. The traditional method to solve this problem is based on Bayesian Network, which needs a rigid and independent assumption basis and prior probability knowledge but ignoring the semantic relationship in causality analysis. This paper aims to perform the uncertainty of causality in signal equipment failure diagnosis through a new way that emphasis on mining semantic relationships.

Design/methodology/approach

This study proposes a deterministic failure diagnosis (DFD) model based on the question answering system to implement railway signal equipment failure diagnosis. It includes the failure diagnosis module and deterministic diagnosis module. In the failure diagnosis module, this paper exploits the question answering system to recognise the cause of failure consequences. The question answering is composed of multi-layer neural networks, which extracts the position and part of speech features of text data from lower layers and acquires contextual features and interactive features of text data by Bi-LSTM and Match-LSTM, respectively, from high layers, subsequently generates the candidate failure cause set by proposed the enhanced boundary unit. In the second module, this study ranks the candidate failure cause set in the semantic matching mechanism (SMM), choosing the top 1st semantic matching degree as the deterministic failure causative factor.

Findings

Experiments on real data set railway maintenance signal equipment show that the proposed DFD model can implement the deterministic diagnosis of railway signal equipment failure. Comparing massive existing methods, the model achieves the state of art in the natural understanding semantic of railway signal equipment diagnosis domain.

Originality/value

It is the first time to use a question answering system executing signal equipment failure diagnoses, which makes failure diagnosis more intelligent than before. The EMU enables the DFD model to understand the natural semantic in long sequence contexture. Then, the SMM makes the DFD model acquire the certainty failure cause in the failure diagnosis of railway signal equipment.

Details

Smart and Resilient Transport, vol. 3 no. 2
Type: Research Article
ISSN: 2632-0487

Keywords

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Abstract

Details

The Handbook of Road Safety Measures
Type: Book
ISBN: 978-1-84855-250-0

1 – 10 of over 85000