Design, organization and implementation of a methods pool and an application systematics for condition based maintenance
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Date
2011-03-22
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Abstract
Zunehmender Wettbewerb in der Industrie erfordert immer kürzere Amortisationszeiten von kapitalintensiven Produktionsanlangen. Wesentliche Voraussetzungen für die Realisierung kurzer Amortisationszeiträume sind eine hohe Verfügbarkeit der Anlagen und das Erreichen einer gleichmäßig hohen und konstanten Produktqualität. Eine effiziente Instandhaltungsstrategie unterstützt diese Anforderungen an die Verfügbarkeit und an die Produktqualität, vor allem durch eine geringe Bedarfswartung und zunehmend vorbeugende Instandhaltungsbemühungen. In der Industrie wird hierzu häufig die zustandsbasierte Instandhaltung (Condition Based Maintenance - CBM) angewendet. Die CBM Methode versucht aus Zustandseinschätzung der Maschinen, abgeleitet von verschiedenen Zustandsüberwachungs-Verfahren (Condition Monitoring Technique - CMT) und zerstörungsfreien Prüfungen (Nondestructive Test - NDT), erste Mängel zu identifizieren, bevor sie sich kritisch auf die Produktion auswirken.
Ein effektives CBM Programm verlangt eine frühe Fehlererkennung und eine genaue Identifikation der Fehlerattribute. Diese Anforderungen werden in der Industrie heute noch unzureichend erfüllt. Die Ursache liegt vor allem in den hohen Kosten, die sich aufgrund unzureichender Information über die potenziellen Fehler ergeben, sowie in der unzulänglichen Kenntnis oder ungeeigneten Anwendung von verschiedenem CMTs und NDTs begründet. Daher werden im Rahmen dieser Arbeit eine neuartige Toolbox und ein Anwendungskonzept entwickelt, um die Umsetzung eines effektiven CBM Programms in der Automobil-Zulieferindustrie zu unterstützen. Hierbei ist der Ansatz so allgemein gewählt, dass er nicht nur auf das Anwendungsgebiet der Automobilindustrie beschränkt ist, sondern auch auf die allgemeine Herstellungs- oder Produktionsindustrie angewendet werden kann.
Die CBM-Toolbox setzt sich aus drei Hauptwerkzeugen zusammen. Das erste Werkzeug fasst statistische Fehler-Analysen zusammen, die die in einem Informationssystem des Betriebes vorhandenen Fehlerdaten auswertet, um die relevanten Informationen tabellarisch bzw. grafisch darzustellen. Das zweite Werkzeug ist eine Wissensdatenbank in der das Expertenwissen über verschiedene CMTs und NDTs verwaltet wird. Dieses Expertenwissen ist so strukturiert, dass zusätzlich zu jeder Methode, ihre Anwendbarkeit, Nachweisbarkeit und Vorteile bzw. Nachteile dargestellt werden. Das dritte Werkzeug ist eine objektbasierte Problem-und-Ursache-Analyse, deren Ergebnis eine tabellarisch dargestellte Problem-Ursache Beziehung von besonderen Maschinenanlagen ist. Diese Hauptwerkzeuge werden durch zwei weitere Werkzeuge, ein Finanzanalyse-Werkzeug und eine Auswahlmatrix ergänzt, die die verschiedenen Entscheidungsmöglichkeiten hinsichtlich der Umsetzbarkeit bewertet.
The everyday increasing competition in industry and the compulsion of faster investment paybacks for complex and expensive machinery, in addition to operational safety, health and environmental requirements, take for granted high availability of the production machinery and high and stable quality of products. These targets are reached only if the machinery is kept in proper working condition by utilizing an appropriate maintenance tactic. In this frame of thought, monitoring of machinery systems has become progressively more important in meeting the rapidly changing maintenance requirements of today’s manufacturing systems. Besides, as the pressure to reduce manning in plants increases, so does the need for additional automation and reduced organizational level maintenance. Augmented automation in manufacturing plants has led to rapid growth in the number of machinery sensors installed. Along with reduced manning, increased operating tempos are requiring maintenance providers to make repairs faster and ensure that equipment operates reliably for longer periods. To deal with these challenges, condition based maintenance (CBM) has been widely employed within industry. CBM, as a preventive and predictive action, strives to identify incipient faults before they become critical through structural condition assessment derived from Different condition monitoring techniques (CMT) and nondestructive tests (NDT). An effective CBM program requires early recognition of failures and accurate identification of the associated attributes in a feasible manner. The achievement of this proficiency in industry is still intricate and relatively expensive due to deficient information about the potential failures as well as inadequate knowledge or improper application of different CMTs and NDTs. Accordingly, a new toolbox has been developed to facilitate and sustain effective CBM programs in the automotive supply industry. The CBM toolbox is consisted of three major tools. The first tool is a series of statistical failure analyses which uses the failure history data available in a plant’s information system to generate valuable information in tabulated and graphical postures. The second tool is a repository filled with expert knowledge about different CMTs and NDTs formatted in a way that in addition to the concept of each technique, its applicability, detectability, and its pros and cons are expressed. The third tool is an object based problem and cause analysis whose outcome is tabulated problem-cause relationships associated with particular machinery objects. These major tools are also accompanied by two supplementary tools, a financial analysis tool and a selection matrix, to ensure feasibility of all undertaken decisions while using the toolbox.
The everyday increasing competition in industry and the compulsion of faster investment paybacks for complex and expensive machinery, in addition to operational safety, health and environmental requirements, take for granted high availability of the production machinery and high and stable quality of products. These targets are reached only if the machinery is kept in proper working condition by utilizing an appropriate maintenance tactic. In this frame of thought, monitoring of machinery systems has become progressively more important in meeting the rapidly changing maintenance requirements of today’s manufacturing systems. Besides, as the pressure to reduce manning in plants increases, so does the need for additional automation and reduced organizational level maintenance. Augmented automation in manufacturing plants has led to rapid growth in the number of machinery sensors installed. Along with reduced manning, increased operating tempos are requiring maintenance providers to make repairs faster and ensure that equipment operates reliably for longer periods. To deal with these challenges, condition based maintenance (CBM) has been widely employed within industry. CBM, as a preventive and predictive action, strives to identify incipient faults before they become critical through structural condition assessment derived from Different condition monitoring techniques (CMT) and nondestructive tests (NDT). An effective CBM program requires early recognition of failures and accurate identification of the associated attributes in a feasible manner. The achievement of this proficiency in industry is still intricate and relatively expensive due to deficient information about the potential failures as well as inadequate knowledge or improper application of different CMTs and NDTs. Accordingly, a new toolbox has been developed to facilitate and sustain effective CBM programs in the automotive supply industry. The CBM toolbox is consisted of three major tools. The first tool is a series of statistical failure analyses which uses the failure history data available in a plant’s information system to generate valuable information in tabulated and graphical postures. The second tool is a repository filled with expert knowledge about different CMTs and NDTs formatted in a way that in addition to the concept of each technique, its applicability, detectability, and its pros and cons are expressed. The third tool is an object based problem and cause analysis whose outcome is tabulated problem-cause relationships associated with particular machinery objects. These major tools are also accompanied by two supplementary tools, a financial analysis tool and a selection matrix, to ensure feasibility of all undertaken decisions while using the toolbox.
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Condition based maintenance, Preventive maintenance, Condition monitoring, Technology management