A predictive maintenance procedure using pressure and acceleration signals from a centrifugal fan

Velarde-Suáréz, Sandra; Ballesteros-Tajadura, Rafael; Hurtado-Cruz, Juan Pablo

doi:10.1016/j.apacoust.2005.05.006

Cited by 15 publications

(7 citation statements)

References 5 publications

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“…(Liu, 2008). Malgré les coûts de développement élevés, les techniques de la PdM ont été implémentées avec succès dans certains domaines d'application, tels que les machines tournantes (Velarde-Suarez et al, 2006), l'aéronautique (Byington et al, 2002) ou l'industrie de l'électronique et des composants électriques (Bansal et al, 2005). Néanmoins, un état des lieux sur le développement des programmes de maintenance en général et de la pratique de la PdM en particulier, dans l'industrie du semi-conducteur, a révélé un besoin incontestable de porter l'effort vers une intégration des stratégies prédictives (Djurdjanovic et Liu, 2006).…”

Section: I42 Premier Positionnement Et Travaux Connexesunclassified

Towards Bayesian network methodology for predicting the equipment health factor of complex semiconductor systems

Bouaziz¹,

Zamaï²,

Duvivier³

2013

International Journal of Production Research

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Section: I42 Premier Positionnement Et Travaux Connexesunclassified

Towards Bayesian network methodology for predicting the equipment health factor of complex semiconductor systems

Bouaziz¹,

Zamaï²,

Duvivier³

2013

International Journal of Production Research

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“…Many researchers used motor current signal analysis in order to detect anomalies on bearings of electrical motors [4]. At the initiation level, damaged rotor bars, slightly damaged bearing elements and possible misalignments may consequence in induce of the electrical consumption [5]. Electrical Signature Analysis (ESA) is the main word in order to monitor electrical machine condition among the inspection of electrical signals for instance current and voltage.…”

Section: Introductionmentioning

confidence: 99%

A tribological study in the perspective of vibro-electric analysis

Erol

Meran

2016

Archives of Materials Science and Engineering

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Purpose: In this case study, oil starvation failure that is a common problem for mechanical constructions has been studied within perspective of vibro-electric analysis. The purpose of this research is testing material properties of a ball bearing under the oil starving failure and condition of resonance for inspecting predictive maintenance approaches. Design/methodology/approach: A test setup construction designed, built and placed in a laboratory location. An electrical motor has been selected for collecting electrical and vibrational data over a bearing by making tests. Test has been implemented under the electricity frequency of 63.8 Hz that was induced the rotation of electrical motor in order to set rotational speed. Findings: Respect to results of the analysis, inspecting of oil starving failure and resonance problem has been detected more clearly and informative by vibration analysis. Research limitations/implications: This research study is limited with some experience and data from case companies, data from relevant scientific literature and theories through scientific databases. Practical implications: Implementation of condition monitoring technologies on mechanical systems brings similarities with the health systems of the human beings. Such as developments in technologies of human health management, consultation of the mechanical systems should be practised in order to prevent breakdowns which cause unplanned stoppages. Vibrational and electrical monitoring techniques can be applied at enterprises in order to ease and reach higher accuracy for detection of symptoms cause from mechanical fault initiations. Originality/value: The originality of this research receive resources from comparison of traditional condition monitoring tool vibration analysis and more recently developed electrical consumption analysis used for predictive maintenance.

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“…As compared with an traditional maintenance implementation, 30% cost saving can be gained by vibration based maintenance [4]. It is suggested to make some artificial failure tests on systems before the installation in order to have idea about the sensitivity of the signals [5]. Manufacturing industries goes into more productive maintenance techniques respect to the competitiveness and deacreasing market place.…”

Section: Introductionmentioning

confidence: 99%

A Research Study on Vibrating Elements and Consuming Electricity in Predictive Maintenance

Erol¹

2016

Eur. j. interdiscip. stud.

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Mechanical looseness is one of the common failures detected on machines and mechanical systems. In this research, resonance effect that is a problem for mechanical systems has been studied within perspective of mechanical looseness failure through a bearing. A test setup has been designed, built and located in laboratory environment. A bearing in the electrical motor, that is one of the elements in the test setup construction, has been chosen for acquiring data throught acoustic, vibration and electrical consumption during the test. The purpose of this research is testing condition monitoring of mechanical looseness failure and resonance for studying comparison of different predictive maintenance perspectives. The test was implemented at the electricity frequency of 40.5 Hz that forced the electrical motor through identified rotation speed. According to the analysis results, inspecting of mechanical looseness failure and resonance problem has been detected as the most effectively by vibration analysis.

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A predictive maintenance procedure using pressure and acceleration signals from a centrifugal fan

Cited by 15 publications

References 5 publications

Towards Bayesian network methodology for predicting the equipment health factor of complex semiconductor systems

Towards Bayesian network methodology for predicting the equipment health factor of complex semiconductor systems

A tribological study in the perspective of vibro-electric analysis

A Research Study on Vibrating Elements and Consuming Electricity in Predictive Maintenance

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