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Article
Publication date: 5 June 2007

Kondo H. Adjallah

288

Abstract

Details

Journal of Quality in Maintenance Engineering, vol. 13 no. 2
Type: Research Article
ISSN: 1355-2511

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Article
Publication date: 5 June 2007

Yury V. Kolokolov, Anna V. Monovskaya and Kondo Hloindo Adjallah

The paper aims to present a new approach for safe operation, and maintenance cost reduction, regarding electrical and electromechanical systems of power production, power…

438

Abstract

Purpose

The paper aims to present a new approach for safe operation, and maintenance cost reduction, regarding electrical and electromechanical systems of power production, power conversion and power transmission, primarily in industrial units.

Design/methodology/approach

The paper adapts a theoretical approach to real‐time monitoring of pulse energy conversion systems (PECSs), and prediction of abnormal dynamics incipient and developing failure. The approach utilizes the preliminary bifurcation analysis results and the geometrical interpretation of the fractal regularities in PECS dynamics, to reveal degradation development.

Findings

It turns out that this new approach enables one to fill the joint requirements of real‐time failure prediction of the high frequency power control devices, and of the relating failure symptoms to cause parameters. Discussions are led on the fundamental outcomes of numerical and experimental investigations of a DC‐DC buck voltage converter with pulse‐width‐modulation (PWM) control.

Practical implications

The real‐time monitoring of incipient abnormal dynamics in key nonlinear devices of electrical and electromechanical systems constitutes a mean to predict and prevent failures. It provides invaluable information for deciding and planning predictive maintenance actions, from the insurance of optimal operating conditions to abnormal operating prevention, either by means of modification of controlled parameters and control laws or, in the worst case, by change of the power components' structure.

Originality/value

About “failure prediction”, this paper proposed to pay attention, not to identification of the dynamics evolution specific reason, but real‐time monitoring of this reason consequence – incipient abnormal dynamics in the electrical and electromechanical systems – that can lead to failure. The advantage of this approach consists in the possibility of taking into account PECS operating conditions of models ambiguity of both disturbing parameter changes and PECS behavior.

Details

Journal of Quality in Maintenance Engineering, vol. 13 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

Available. Content available
Article
Publication date: 30 October 2007

Kondo H. Adjallah

318

Abstract

Details

British Food Journal, vol. 109 no. 11
Type: Research Article
ISSN: 0007-070X

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Article
Publication date: 5 June 2007

Adolfo Crespo Marquez and Benoît Iung

This paper proposes a method to model and assess the availability and reliability of a system when numerous factors such as system complexity, wide range of failure modes…

1760

Abstract

Purpose

This paper proposes a method to model and assess the availability and reliability of a system when numerous factors such as system complexity, wide range of failure modes, environment, and sustainability may influence system behaviour.

Design/methodology/approach

The approach for reliability/availability study is using continuous time stochastic simulation (Monte Carlo simulation) and is based on seven steps for covering logical phases from system description to simulation result discussion. The feasibility and benefits of this approach are shown in a case study on cogeneration plant.

Findings

Owing to the factors influencing the system behaviour, the opportunity to carry out system availability/reliability assessment through analytical models will be many times very restrictive. Thus a general approach to this problem is proposed based on Monte Carlo (stochastic) simulation. The simulation of the system's life process will be carried out in the computer, and estimates will be made for the desired measures of performance. The simulation will then be treated as a series of real experiments, and statistical inference will then be used to estimate confidence intervals for the performance metrics.

Practical implications

Individuals, companies as well as society in general are becoming more and more dependent on increasingly complex technical systems. Moreover, failure of these complex systems often causes a major loss of service with potentially serious consequences (i.e. critical risk). Thus their dependability with its facets such as reliability, availability, safety has become an important issue. For example, the ability of reliability/availability assessment of such systems is invaluable in industrial domains. Indeed reliability/availability assessment is used for various purposes such as maintenance strategy selection, maintenance planning, production planning, risk and cost evaluations. To face with this complexity, the existing analytical models are not well adapted to carry out system modelling and assessment due mainly to assumptions that are difficult to validate. This paper looks into this issue by proposing a generic approach based on Monte Carlo (stochastic) simulation.

Originality/value

The Monte Carlo simulation method allows one to consider various relevant aspects of systems operation that cannot be easily captured by analytical models. The utilisation of this method is growing for the assessment of overall plants availability and the monetary value of plant operation.

Details

Journal of Quality in Maintenance Engineering, vol. 13 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

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Article
Publication date: 5 June 2007

Hongyu Yang, Joseph Mathew and Lin Ma

The purpose of this article is to present a new application of pursuit‐based analysis for diagnosing rolling element bearing faults.

849

Abstract

Purpose

The purpose of this article is to present a new application of pursuit‐based analysis for diagnosing rolling element bearing faults.

Design/methodology/approach

Intelligent diagnosis of rolling element bearing faults in rotating machinery involves the procedure of feature extraction using modern signal processing techniques and artificial intelligence technique‐based fault detection and identification. This paper presents a comparative study of both the basis and matching pursuits when applied to fault diagnosis of rolling element bearings using vibration analysis.

Findings

Fault features were extracted from vibration acceleration signals and subsequently fed to a feed forward neural network (FFNN) for classification. The classification rate and mean square error (MSE) were calculated to evaluate the performance of the intelligent diagnostic procedure. Results from the basis pursuit fault diagnosis procedure were compared with the classification result of a matching pursuit feature‐based diagnostic procedure. The comparison clearly illustrates that basis pursuit feature‐based fault diagnosis is significantly more accurate than matching pursuit feature‐based fault diagnosis in detecting these faults.

Practical implications

Intelligent diagnosis can reduce the reliance on experienced personnel to make expert judgements on the state of the integrity of machines. The proposed method has the potential to be extensively applied in various industrial scenarios, although this application concerned rolling element bearings only. The principles of the application are directly translatable to other parts of complex machinery.

Originality/value

This work presents a novel intelligent diagnosis strategy using pursuit features and feed forward neural networks. The value of the work is to ease the burden of making decisions on the integrity of plant through a manual program in condition monitoring and diagnostics particularly of complex pieces of plant.

Details

Journal of Quality in Maintenance Engineering, vol. 13 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

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Article
Publication date: 5 June 2007

Gao Zhan‐feng, Du Yan‐liang, Sun Bao‐chen and Jin Xiu‐mei

The purpose of this article is to suggest that Fraby‐Perot optic sensor is a practical measurement gage to monitor the strain of great structures such as railway bridges.

1609

Abstract

Purpose

The purpose of this article is to suggest that Fraby‐Perot optic sensor is a practical measurement gage to monitor the strain of great structures such as railway bridges.

Design/methodology/approach

A remote strain monitoring system based on F‐P optic fiber and virtual instrument is designed to monitor the strains of a railway bridge.

Findings

The application results show that the Fraby‐Perot optical fiber sensors can accurately measure strain and they are suitable for the long‐term and automatic monitoring. In addition, the system has several advantages over conventional structural instruments including fast response, ability of both static and dynamic monitoring, absolute measurement, immunity to interferences such as lightning strikes, electromagnetic noise and radio frequency, low attenuation of light signals in long fiber optic cables.

Practical implications

Health monitoring of structures is getting more and more recognition all over the world because it can minimize the cost of reparation and maintenance and ensure the safety of structures. A strain monitoring system based on F‐P optic fiber sensor was developed according to the health monitoring requirements of Wuhu Yangtze River Railway Bridge, which is the first cable‐stayed bridge with a maximum span of 312 m carrying both railway and highway traffic in China. It has run stably in the monitoring field more than two years and fulfilled the monitoring requirement very well. Now the system has been transplanted successfully to the Zhengzhou Yellow Railway Bridge for strain monitoring. So the work can be referenced by other similar health monitoring projects.

Originality/value

Long‐term, real‐time monitoring of strain using FP fiber optic sensors in railway bridge is an innovation. A remote strain data acquisition and real‐time processing are another character of the system. The work studied can be referenced by other structures monitoring, such as tunnel, concrete bridges, concrete and earth dams.

Details

Journal of Quality in Maintenance Engineering, vol. 13 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

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Article
Publication date: 5 June 2007

Olivier Basile, Pierre Dehombreux and Fouad Riane

Reliability models are generally estimated from small samples. This paper seeks to calculate the uncertainty affecting reliability parameters in function of the sample size.

635

Abstract

Purpose

Reliability models are generally estimated from small samples. This paper seeks to calculate the uncertainty affecting reliability parameters in function of the sample size.

Design/methodology/approach

The confidence intervals are calculated on the basis of Monte Carlo simulations and using the variance‐covariance matrix; the two methods are compared.

Findings

Numerical results for the estimation of uncertainty have been obtained for standard reliability models, non‐homogeneous Poisson process and generalized renewal process.

Originality/value

For the generalized renewal process, the article points out the influence of the age correction factor on the number of repairs authorized and on uncertainty. The surface plot of the likelihood function with respect to parameters is a convenient tool to interpret the uncertainty.

Details

Journal of Quality in Maintenance Engineering, vol. 13 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

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Article
Publication date: 29 April 2014

Zhouhang Wang, Maen Atli and H. Kondo Adjallah

The purpose of this paper is to introduce a method for modelling the multi-state repairable systems subject to stochastic degradation processes by using the coloured stochastic…

283

Abstract

Purpose

The purpose of this paper is to introduce a method for modelling the multi-state repairable systems subject to stochastic degradation processes by using the coloured stochastic Petri nets (CSPN). The method is a compact and flexible Petri nets model for multi-state repairable systems and offers an alternative to the combinatory of Markov graphs.

Design/methodology/approach

The method is grounded on specific theorems used to design an algorithm for systematic construction of multi-state repairable systems models, whatever is their size.

Findings

Stop and constraint functions were derived from these theorems and allow to considering k-out-of-n structure systems and to identifying the minimal cut sets, useful to monitoring the states evolution of the system.

Research limitations/implications

The properties of this model will be studied, and new investigations will help to demonstrate the feasibility of the approach in real world, and more complex structure will be considered.

Practical implications

The simulation models based on CSPN can be used as a tool by maintenance decision makers, for prediction of the effectiveness of maintenance strategies.

Originality/value

The proposed approach and model provide an efficient tool for advanced investigations on the development and implementation of maintenance policies and strategies in real life.

Details

Journal of Manufacturing Technology Management, vol. 25 no. 4
Type: Research Article
ISSN: 1741-038X

Keywords

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Article
Publication date: 5 June 2007

Tian Han, Bo‐Suk Yang and Zhong‐Jun Yin

The purpose of this paper is to identify the efficiency of vibration signals for fault diagnosis system of induction motors.

1367

Abstract

Purpose

The purpose of this paper is to identify the efficiency of vibration signals for fault diagnosis system of induction motors.

Design/methodology/approach

A fault diagnosis system for induction motors using vibration signals is designed based on pattern recognition. Genetic algorithm is used for feature reduction and neural network tuning.

Findings

The usage of genetic algorithm improves the system performance through selecting significant features and optimizing network structure. The efficiency of vibration signals is demonstrated.

Practical implications

Condition monitoring and fault diagnosis for induction motors is one of the main industry maintenance parts. Motors faults usually result in whole production line breakdown. In this paper, one fault diagnosis system is proposed for induction motors based on feature recognition through combination of feature extraction, genetic algorithm and neural network techniques. From the paper, one can learn practically the whole procedure of feature‐based fault diagnosis system and the efficiency of GA and vibration signals for motor fault diagnosis. One real test has been done to validate the system performance. The results indicate that this system is promising for the real application in industry.

Originality/value

The use of genetic algorithm for feature selection and neural network tuning; the choice of vibration analysis for fault diagnosis of induction motor.

Details

Journal of Quality in Maintenance Engineering, vol. 13 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

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Article
Publication date: 5 June 2007

Alexandre Muller, Marie‐Christine Suhner and Benoît Iung

This paper proposes the extension of a prognosis process by means of the integration of maintenance alternative impacts in order to develop a maintenance decision‐making tool.

1213

Abstract

Purpose

This paper proposes the extension of a prognosis process by means of the integration of maintenance alternative impacts in order to develop a maintenance decision‐making tool.

Design/methodology/approach

The deployment of this extended prognosis process follows a methodology based both on probabilistic and on event approaches.

Findings

The importance of the maintenance function has increased due to its role in keeping and improving the system availability and safety but also the product quality. To support this new role, the maintenance concept has undergone several major developments to lead to proactive considerations mainly based on prognosis process allowing one to select the best maintenance plan to be carried out.

Practical implications

Studies over the last 20 years have indicated that around Europe the direct cost of maintenance is equivalent to between 4 and 8 per cent of total sales turnover. The indirect cost of maintenance is likely to be a similar amount. Thus, in the countries where modern maintenance practices have yet to be well adopted by industry, the potential savings from modern maintenance are massive. These modern and efficient maintenances imply identifying the root‐cause of component failures, reducing the failures of production systems, eliminating costly unscheduled shutdown maintenances, and improving productivity as well as quality. It means, for the companies, migrating from their traditional reactive approach, which is “fail and fix”, to “predict and prevent”. The advantage of the latter is that maintenance is performed only when a certain level of equipment deterioration occurs. This “proactive” maintenance is mainly based on prognosis process often considered as the Achilles heel, while its goal is fundamental for implementing anticipation capabilities. This paper looks into this issue by proposing the development of an innovative prognosis process integrating the modelling of maintenance actions and their impacts on system performances. It leads to offering a maintenance aided decision‐making tool cable of assisting the decision‐maker in selecting the best maintenance plan to be carried out.

Originality/value

The feasibility of this new prognosis is experimented on the manufacturing Tele‐Maintenance (TELMA) platform supporting the unwinding of metal bobbins.

Details

Journal of Quality in Maintenance Engineering, vol. 13 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

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