Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings : a review

A rolling bearing is an essential component of a rotating mechanical transmission system. Its performance and quality directly affects the life and reliability of machinery. Bearings’ performance and reliability need high requirements because of a more complex and poor working conditions of bearings. A bearing with high reliability reduces equipment operation accidents and equipment maintenance costs and achieves condition-based maintenance. First in this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings are introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced, and finally, the advantages, disadvantages, and trends developed in the future of the detection main individual physics technology and multi-sensors information fusion technology are summarized. This paper is expected to provide the necessary basis for the follow-up study of the fault diagnosis of rolling bearings and a foundational knowledge for researchers about rolling bearings.The Natural Science Foundation of China (NSFC) (grant numbers: 51675403, 51275381 and 51505475), National Research Foundation, South Africa (grant numbers: IFR160118156967 and RDYR160404161474), and UOW Vice-Chancellor’s Postdoctoral Research Fellowship.International Journal of Advanced Manufacturing Technology2019-04-01hj2018Electrical, Electronic and Computer Engineerin

Development of Advanced Techniques For Gear Wear Monitoring and Prediction

Gears are widely used in industrial machinery, and gear failure is a main cause of machine failure. Since gear wear is often the initial stage of gear failure, its monitoring and prediction are key to minimising machine downtime, maintenance costs, and safety risks. However, existing gear wear monitoring and prediction techniques face some ongoing technical challenges, including providing direct wear information and wear assessment and prediction in a cost-effective and efficient manner. To tackle the challenges, this research aims to develop a set of advanced techniques for gear wear monitoring and prediction. The four objectives of the research and their corresponding methodologies and outcomes are summarised as follow. (a) To develop a method to obtain direct and comprehensive wear information without disassembling the gearbox. This objective was realised by combining surface replication with image analysis, allowing easy acquisition of high-resolution mould images showing wear evolution on a tooth flank. (b) To investigate the relationship between the features of worn gear surfaces and those of wear debris. To further understand the role of wear debris analysis in wear assessment, a study on various features of macropits and wear particles in the same fatigue process was conducted and provided new insights into gear pitting and its monitoring. (c) To develop an automated system for gear wear assessment. Deep learning models were developed to identify wear mechanisms and severities using gear mould images and wear debris images. High classification accuracies were achieved, and comparisons between the two image sources were made. (d) To develop a gear wear prediction model using direct wear information. A deep generative model was developed and trained on time series of gear mould images. Tests showed that the model using the state-of-the-art AI technology can generate realistic and accurate predictions. Overall, this research addressed the main limitations of existing methods and provided a direct and evidence-based tool for monitoring and predicting gear wear. Its specific contributions include a new moulding-imaging method for monitoring gear wear evolution, a detailed comparison between worn gear surfaces and wear debris in a wear process, and AI and image-based gear wear assessment and prediction models for the first time. The techniques could be performed during regular inspections of machines and used with online methods for increased robustness

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

A Machine Learning approach for damage detection and localisation in Wind Turbine Gearbox Bearings

Increasing demand for renewable sources requires more cost-effective solutions to mitigate the cost of maintenance and produce more energy. Preventive maintenance is the most normally adopted scheme in industry for maintenance but despite being well accepted has severe limitations. Its inability to intelligently schedule maintenance at the right time and prevent unexpected breakdowns are the main downsides of this approach and consequently leads to several problems such as unnecessary maintenances. This strategy does not justify the additional costs and thereby represents a negative aspect for renewable energy resource companies that try to generate cost-competitive energy. These challenges are progressively leading towards the predictive maintenance approach to overcome these aforementioned issues. Wind Turbine Gearbox Bearings have received a lot of attention due to the high incidence failure rates provoked by the harsh operational and environmental conditions. Current techniques only reach a level one of diagnostics commonly known as the Novelty Detection stage and normally requires the expertise of a skilled operator to interpret data and infer damage from it. A data-driven approach by using Machine Learning methods has been used to tackle the damage detection and location stage in bearing components. The damage location was performed by using non-destructive methods such as the Acoustic Emission technique — these measurements were used as features to locate damage around the bearing component once the damage was detected. The implementation of this stages also led to the exploration of damage generation due to overload defects and proposed a methodology to simulate these defects in bearings — the study of this concept was implemented in a scaled-down experiment where damage detection and localisation was performed. Due to the importance of the implementation of a damage location stage, damage in AE sensors was also explored in this work. Features extracted from impedance curves allowed to train Machine Learning methods to trigger a novelty when a bonding scenario occurred. This ultimately allowed the identification of unhealthy sensors in the network that could potentially generate spurious results in the damage predictions stage

Tribology of Machine Elements

Tribology is a branch of science that deals with machine elements and their friction, wear, and lubrication. Tribology of Machine Elements - Fundamentals and Applications presents the fundamentals of tribology, with chapters on its applications in engines, metal forming, seals, blasting, sintering, laser texture, biomaterials, and grinding

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Flame and acoustic waves interactions and flame control

In this PhD project, the investigation of the stability of a laminar diffusion flame and the interaction of the flame with acoustic waves inside an acoustically excited cylindrical tube is presented. Interesting phenomena have been observed by studying both the infrasound and sound effect on the flame structure and dynamics.When a cylindrical tube burner is acoustically excited at one end, a standing wave will be produced along the tube burner. By applying a programming controlled signal from a signal generator, the loudspeaker generates acoustic waves with different frequencies and intensities to excite the flame, which can make the flame relatively stable or unstable, even blow out. Different methods in both frequency domain and time domain have been applied to analyze the flame stability affected by acoustic waves. Both infrasound and sound are tested in this research. Infrasound is the acoustic wave with a frequency too low to be heard by human ear covering sounds beneath the lowest limits of human hearing (20Hz) down to 0.001Hz. It is found that infrasound is able to take over buoyancy-driven flame flickering and make the flame flicker at the same frequency as the forcing infrasound. For some infrasound, half excited frequency has been detected clearly in the power spectrum of CH* chemiluminescence signals acquired by a photomultiplier. On the other hand, some higher frequency acoustic wave can have observable effect on flame flickering but the buoyancy-driven flickering is still the dominant oscillating mode; some other higher frequency acoustic wave can make the flame very stable, such as the acoustic wave at 140Hz. Image processing technique has shown that the influence of acoustic waves on the laminar diffusion flame varies spatially. It is also observed that a diffusion flame may oscillate at different frequency spatially. Taking the flame without acoustic excitation as an example, the inner most area of the flame oscillates at the typical flickering frequency, but the most outer areas of the flame oscillate at the second-harmonic of the typical flickering frequency. Finally, some control strategies are developed for the laboratory tube burner based on the gained physical insights in this research.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Aeronautical Engineering: A continuing bibliography with indexes, supplement 153, October 1982

This bibliography lists 535 reports, articles and other documents introduced into the NASA Scientific and Technical Information System in September 1982