C-V characteristics of piezotronic metal-insulator-semiconductor transistor

Third generation semiconductors for piezotronics and piezo-phototronics, such as ZnO and GaN, have both piezoelectric and semiconducting properties. Piezotronic devices normally exhibit high strain sensitivity because strain-induced piezoelectric charges control or tune the carrier transport at junctions, contacts and interfaces. The distribution width of piezoelectric charges in a junction is one of important parameters. Capacitance-voltage (C-V) characteristics can be used to estimate the distribution width of strain-induced piezoelectric charges. Piezotronic metal-insulator-semiconductor (MIS) has been modelled by analytical solutions and numerical simulations in this paper, which can serve as guidance for C-V measurements and experimental designs of piezotronic devices

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

Prediction, classification and diagnosis of spur gear conditions using artificial neural network and acoustic emission

The gear system is a critical component in the machinery and predicting the performance of a gear system is an important function. Unpredictable failures of a gear system can cause serious threats to human life, and have large scale economic effects. It is necessary to inspect gear teeth periodically to identify crack propagation and, other damages at the earliest. This study has two main objectives. Firstly, the research predicted and classified specific film thickness (λ) of spur gear by Artificial Neural Network (ANN) and Regression models. Parameters such as acoustic emission (AE), temperature and specific film thickness (λ) data were extracted from works of other researchers. The acoustic emission signals and temperature were used as input to ANN and Regression models, while (λ) was the output of the models. Second objective is to use the third generation ANN (Spiking Neural Network) for fault diagnosis and classification of spur gear based on AE signal. For this purpose, a test rig was built with several gear faults. The AE signal was processed through preprocessing, features extraction and selection methods before the developed ANN diagnosis and classification model were built. These processes were meant to improve the accuracy of diagnosis system based on information or features fed into the model. This research investigated the possibility of improving accuracy of spur gear condition monitoring and fault diagnoses by using Feed-Forward Back- Propagation Neural Networks (FFBP), Elman Network (EN), Regression Model and Spiking Neural Network (SNN). The findings showed that use of specific film thickness has resulted in the FFBP network being able to provide 99.9% classification accuracy, while regression and multiple regression models attained 73.3 % and 81.2% classification accuracy respectively. For gear fault diagnosis, the SNN achieved nearly 97% accuracy in its diagnosis. Finally, the methods use in the study have proven to have high accuracy and can be used as tools for prediction, classification and fault diagnosis in spur gear

Advances in Design by Metallic Materials: Synthesis, Characterization, Simulation and Applications

Very recently, a great deal of attention has been paid by researchers and technologists to trying to eliminate metal materials in the design of products and processes in favor of plastics and composites. After a few years, it is possible to state that metal materials are even more present in our lives and this is especially thanks to their ability to evolve. This Special Issue is focused on the recent evolution of metals and alloys with the scope of presenting the state of the art of solutions where metallic materials have become established, without a doubt, as a successful design solution thanks to their unique properties

Corrosion and biofouling of offshore wind monopile foundations

The impact of corrosion and biofouling on offshore wind turbines is considered to be a key issue in terms of operation and maintenance (O&M) which must be better addressed. Early design assumptions for monopile foundations anticipated low, uniform corrosion rates in a sealed compartment that would be completely air- and water-tight. However, operational experience has shown that in practice it is very difficult to maintain a fully sealed compartment, with seawater and oxygen ingress frequently observed within many monopiles across the industry. A key concern is that this situation may accelerate corrosion of the internal surfaces. On the external surfaces, the accumulation of biofouling is known to impede the safe transfer of technicians from vessel to transition piece (TP) and requires frequent cleaning. It is also likely to influence the dynamic behaviour of the foundation due to the added weight and the hydrodynamic loading due to thickness and surface roughness changes. There is sufficient evidence to suggest that the current offshore wind guidelines on biofouling could be improved to optimise the design margins. This thesis investigated the influence of internal monopile corrosion and external biofouling growth on the turbines at Teesside Offshore Wind Farm (owned and operated by EDF Energy). At Teesside, the primary drivers of internal monopile corrosion are identified as temperature, oxygen, pH and tidal variation. The influence of each of these parameters on the corrosion rate of monopile steel were investigated in a series of laboratory experiments and in-situ monopile trials. The experimental study was conducted at EDF laboratories in France using 186 corrosion coupons that were exposed to various treatments simulating internal monopile conditions. At Teesside, 49 coupons were suspended at various internal monopile locations across 5 foundations. In both cases, the weight loss measurement of coupons over time was used to determine the corrosion rates. Results suggest that tidal (wet/dry cycles) low pH and oxygen ingress have the greatest influence on the corrosion degradation of unprotected monopile steel. Internal tidal variations create a particularly aggressive corrosion environment. A decision tree matrix has been developed to predict corrosion rate classification (high/medium/low) under a range of environmental conditions. In parallel, a biofouling assessment was conducted at Teesside Offshore Wind Farm to determine the type and extent of marine growth on the intertidal and submerged zones of turbines. This has enabled a better understanding of the species diversity and community morphology but has also facilitated the development and testing of two sampling methodologies for the intertidal and subsea regions of offshore wind turbines; scrape sampling and remotely operated vehicle (ROV) surveying, respectively. The results of the assessment suggest a zonation pattern of marine growth with depth that is consistent with findings from other offshore wind farms and platforms. A super abundance of the non-native midge species T. japonicas at the intertidal zone has also been observed at other offshore wind farms in Belgium and Denmark, however, this is first evidence of its existence at a UK offshore wind farm. Removal of biofouling from the intertidal zones and jet-washing has now been optimised to coincide with peak settlement periods of mussels and barnacles. Image analysis and 3D mapping was conducted on the subsea ROV video footage to estimate thickness, roughness and added weight of biofouling. This research provides an initial investigation into the effects of internal corrosion and external biofouling on monopile foundations at Teesside Offshore Wind Farm. The methodologies developed for this investigation and the results are critically discussed in the context of asset life assessment and improvements are suggested in further work