Modelling and experimental investigation of magnetic flux leakage distribution for hairline crack detection and characterization

The Magnetic Flux Leakage (MFL) method is a well-established branch of electromagnetic Non-Destructive Evaluation (NDE) extensively used to assess the physical condition of ferromagnetic structures. The main research objective of this research work presented in this thesis is the detection and characterization of the MFL distribution caused by rectangular surface and far-surface hairline cracks. It looks at the use of the direct current and pulsed current techniques to investigate the presence of hairline cracks in ferromagnetic steel pipelines, by comparing the Finite Element Modelling (FEM) technique with practical experiments. First, the expected response of an MFL probe scanned across the area of a hairline crack was predicted using the 3D FEM numerical simulation technique. The axial magnetization technique is employed and the characteristics of the surface and far-surface leakage field profile

Semi-automatic liquid filling system using NodeMCU as an integrated Iot Learning tool

Computer programming and IoT are the key skills required in Industrial Revolution 4.0 (IR4.0). The industry demand is very high and therefore related students in this field should grasp adequate knowledge and skill in college or university prior to employment. However, learning technology related subject without applying it to an actual hardware can pose difficulty to relate the theoretical knowledge to problems in real application. It is proven that learning through hands-on activities is more effective and promotes deeper understanding of the subject matter (He et al. in Integrating Internet of Things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course. Erie, PA, USA, pp 1–9 (2016)). Thus, to fulfill the learning requirement, an integrated learning tool that combines learning of computer programming and IoT control for an industrial liquid filling system model is developed and tested. The integrated learning tool uses NodeMCU, Blynk app and smartphone to enable the IoT application. The system set-up is pre-designed for semi-automation liquid filling process to enhance hands-on learning experience but can be easily programmed for full automation. Overall, it is a user and cost friendly learning tool that can be developed by academic staff to aid learning of IoT and computer programming in related education levels and field

Modelling and experimental investigation of magnetic flux leakage distribution for hairline crack detection and characterization

The Magnetic Flux Leakage (MFL) method is a well-established branch of electromagnetic Non-Destructive Evaluation (NDE) extensively used to assess the physical condition of ferromagnetic structures. The main research objective of this research work presented in this thesis is the detection and characterization of the MFL distribution caused by rectangular surface and far-surface hairline cracks. It looks at the use of the direct current and pulsed current techniques to investigate the presence of hairline cracks in ferromagnetic steel pipelines, by comparing the Finite Element Modelling (FEM) technique with practical experiments. First, the expected response of an MFL probe scanned across the area of a hairline crack was predicted using the 3D FEM numerical simulation technique. The axial magnetization technique is employed and the characteristics of the surface and far-surface leakage field profile

Design, organization and implementation of a methods pool and an application systematics for condition based maintenance

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

Novel Approaches for Structural Health Monitoring

The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability and to serve their intended purpose. However, old unsolved problematics as well as new challenges exist. Furthermore, unprecedented conditions, such as stricter safety requirements and ageing civil infrastructure, pose new challenges for confrontation. Therefore, this Special Issue gathers the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring in dealing with old and new aspects of this ever-growing research field