A review on machinery diagnostics and prognostics implementing condition-based maintenance

Jardine, Andrew; Lin, Daming; Banjević, Dragan

doi:10.1016/j.ymssp.2005.09.012

Cited by 3,571 publications

(2,175 citation statements)

References 198 publications

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“…These two references [7,19] fully support the authors method, from data collection through data processing to optimal maintenance decision making.…”

Section: Introduction and Literature Surveymentioning

confidence: 61%

“…The paper [7] is a very large review on machinery diagnostics and prognostics implementing condition-based maintenance using 271 references and other reviews in the paper [19] using 104 references which point to future perspectives on maintenance optimization. These two references [7,19] fully support the authors method, from data collection through data processing to optimal maintenance decision making.…”

Section: Introduction and Literature Surveymentioning

confidence: 99%

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Preventive maintenance models – higher operational reliability

Legát

Mošna

Aleš

et al. 2016

EiN

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Article citation info: Introduction and literature surveyMany maintenance strategies, policies and methods have been developed, which are aimed at making maintenance cheaper and more effective. Such programs have the minimization of costs, downtime and losses due to failure of critical objects of the equipment as their main objective. Cost minimization improves the effectiveness and profitability of the organization [1,2,9,12,13,20].For creation of the maintenance policies, well described data mining input is very important. [4].In recent years, useful models of preventive and predictive maintenance optimization with different complexity and applicability have been further developed.In the paper, [5] the authors proposed a quasi-periodic imperfect preventive maintenance policy. Finally, a real case study of preventive maintenance on Chinese diesel locomotives is examined to illustrate the proposed maintenance policy.The paper [6] proposes an approach in which preventive and failure replacement costs as well as inspection cost are taken into account to determine the optimal replacement policy and an age-based inspection scheme, such that the total average costs of replacements and inspections is minimized.Determination of the preventive effect of optimal replacement policies in the paper [8] is based on aging intensity and the cost ratio of failure and preventive replacements. One of its conclusions is that not every preventive maintenance is fully effective and a policy of, "run to failure" can be more effective (note: in some cases).The proposed model in the paper [10] takes into consideration the stochastic nature of equipment failures. The output from the model is a cost distribution against the time from which the minimum cost may be found for a particular period and this period is defined as the optimum lifespan of the machine part.The paper [11] considers periodic preventive maintenance policies for a deteriorating repairable system. On each failure, the system is repaired and, at the planned times, it is periodically maintained to improve its performance reliability. Most periodic preventive maintenance (PM) models for repairable systems have been studied assuming that the failure process between two PMs follows the nonhomogeneous Poisson process (NHPP), implying the minimal repair on each failure.The paper [14] regarding warranty policy considering three maintenance options for products with multiple failure modes also showed the broad usability of the Weibull distribution. This fact supports the decision of the authors to also use the Weibull function.The paper [15] presents a new mathematical function to model an improvement based on the ratio of maintenance and repair costs, and demonstrate how it outperforms fixed improvement factor models by analyzing the effectiveness in terms of cost and reliability of a system. Legát V, MošnA F, ALeš Z, JurčA V. Preventive maintenance models -higher operational reliability. eksploatacja i niezawodnosc - Maintenance and reliability 2017; 19 (1): 134-141, http://dx...

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“…These two references [7,19] fully support the authors method, from data collection through data processing to optimal maintenance decision making.…”

Section: Introduction and Literature Surveymentioning

confidence: 61%

Section: Introduction and Literature Surveymentioning

confidence: 99%

Preventive maintenance models – higher operational reliability

Legát

Mošna

Aleš

et al. 2016

EiN

View full text Add to dashboard Cite

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“…The extraction of faulty features from these health monitoring signals and then performing intelligent detection and identification of bearing faults has been studied extensively in the past three decades. Mainly based on vibration signature analysis, many fault diagnosis methods combining advanced signal processing and pattern recognition approaches have been proposed for bearing diagnostics [2][3][4]. Although bearing diagnostics can be helpful for the implementation of condition-based maintenance (CBM) by effectively diagnosing bearing faults, they cannot reveal bearing degradation trends or be used for estimation of remaining useful life.…”

Section: Introductionmentioning

confidence: 99%

Performance Degradation Assessment of Rolling Element Bearings Based on an Index Combining SVD and Information Exergy

Zhang

et al. 2014

Entropy

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Performance degradation assessment of rolling element bearings is vital for the reliable and cost-efficient operation and maintenance of rotating machines, especially for the implementation of condition-based maintenance (CBM). For robust degradation assessment of rolling element bearings, uncertainties such as those induced from usage variations or sensor errors must be taken into account. This paper presents an information exergy index for bearing performance degradation assessment that combines singular value decomposition (SVD) and the information exergy method. Information exergy integrates condition monitoring information of multiple instants and multiple sensors, and thus performance degradation assessment uncertainties are reduced and robust degradation assessment results can be obtained using the proposed index. The effectiveness and robustness of the proposed information exergy index are validated through experimental case studies.

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“…It uses data collected through condition monitoring to advise whether maintenance is necessary, and thus reduces maintenance cost of the system [1]. Many of the maintenance techniques that are currently implemented are using a certain type of time-based preventive maintenance, where the components of the system are checked regularly after a certain period of time to ensure that no serious fault has occurred.…”

Section: Introductionmentioning

confidence: 99%

The use of Bayesian Networks in Detecting the States of Ventilation Mills in Power Plants

Vujnović¹,

Todorov²,

Đurović³

et al. 2014

ELS

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Abstract-The main objective of this paper is to present a new method of predictive maintenance which can detect the states of coal grinding mills in thermal power plants using Bayesian networks. Several possible structures of Bayesian networks are proposed for solving this problem and one of them is implemented and tested on an actual system. This method uses acoustic signals and statistical signal pre-processing tools to compute the inputs of the Bayesian network. After that the network is trained and tested using signals measured in the vicinity of the mill in the period of 2 months. The goal of this algorithm is to increase the efficiency of the coal grinding process and reduce the maintenance cost by eliminating the unnecessary maintenance checks of the system

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A review on machinery diagnostics and prognostics implementing condition-based maintenance

Cited by 3,571 publications

References 198 publications

Preventive maintenance models – higher operational reliability

Preventive maintenance models – higher operational reliability

Performance Degradation Assessment of Rolling Element Bearings Based on an Index Combining SVD and Information Exergy

The use of Bayesian Networks in Detecting the States of Ventilation Mills in Power Plants

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