Propagation of Sensor Noise in Navigation Equations and High Accuracy Dynamic Calibration of Sensors

Accurate navigation in GPS denied locations is extremely hard to achieve. Inertial Navigation Systems (INS) are currently the only reasonable onboard alternative to GPS but INS has error terms that grow very quickly. Even current high-end INSs have an error exceeding one km after only ten minutes of use. A high-accuracy INS would be very useful in underwater applications, spacecraft and extraterrestrial rovers, and military applications. This thesis seeks to enable such Inertial Navigation Systems

A Survey of Error Analysis and Calibration Methods for MEMS Triaxial Accelerometers

MEMS accelerometers are widely used in various fields due to their small size and low cost, and have good application prospects. However, the low accuracy limits its range of applications. To ensure data accuracy and safety we need to calibrate MEMS accelerometers. Many authors have improved accelerometer accuracy by calculating calibration parameters, and a large number of published calibration methods have been confusing. In this context, this paper introduces these techniques and methods, analyzes and summarizes the main error models and calibration procedures, and provides useful suggestions. Finally, the content of the accelerometer calibration method needs to be overcome

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)

Laboratory validation of an Arduino based accelerometer designed for SHM applications

Nowadays, low-cost sensors based on low-cost microcontrollers and microprocessors are gaining a lot of attention from researchers. This increasing interest is due to the fact that the implementation of low-cost solutions may make Structural Health Monitoring (SHM) applicable and affordable to structures with a low budget for their SHM. However, many of the present solutions do not have comparable accuracy and resolution with those of the traditional commercial accelerometers. Also, the noise density of these newly developed prototypes has not been checked through laboratory experiments. In fact, the characteristics of the designed and created accelerometer are simply copied from the datasheet of the chipset used to develop the solution. Moreover, the sampling frequency of the majority of the available low-cost solutions usually is lower than 100 Hz. This paper presents a consistent work with the development of a low-cost wireless accelerometer with a sampling frequency of 333 Hz and noise density of 51µg/vHz. This accelerometer's accuracy, noise density, and reliability are evaluated through a series of laboratory experiments. It is essential to mention that this accelerometer does not need any additional data acquisition equipment and is self-sufficient.Postprint (published version

A Movement-Tremors Recorder for Patients of Neurodegenerative Diseases

Neurodegenerative diseases such as Alzheimer, Parkinson, motor neuron, and Chorea affect millions of people today. Their effect on the central nervous system causes the loss of brain functions as well as motor disturbances and sometimes cognitive deficits. In such a scenario, the monitoring and evaluation of early symptoms are mandatory for the improvement of the patient's quality of life. Here, the authors describe the development, the laboratory calibration, and the "in-field validation" under the medical supervision of a movement tremors recorder for subjects affected by neurodegenerative diseases. The developed device is based on an array of four accelerometers connected to an embedded development board. This system is able to monitor tremor/movement, accidental falls, and, moreover, it can track the Alzheimer subjects' geographical position. A remote supervisor can collect data from the system through Bluetooth, Wi-Fi, or GSM connections. A data compression algorithm was developed directly on board in order to increase the efficiency of data transmission and reduce power consumptions

MEMS Accelerometers

Micro-electro-mechanical system (MEMS) devices are widely used for inertia, pressure, and ultrasound sensing applications. Research on integrated MEMS technology has undergone extensive development driven by the requirements of a compact footprint, low cost, and increased functionality. Accelerometers are among the most widely used sensors implemented in MEMS technology. MEMS accelerometers are showing a growing presence in almost all industries ranging from automotive to medical. A traditional MEMS accelerometer employs a proof mass suspended to springs, which displaces in response to an external acceleration. A single proof mass can be used for one- or multi-axis sensing. A variety of transduction mechanisms have been used to detect the displacement. They include capacitive, piezoelectric, thermal, tunneling, and optical mechanisms. Capacitive accelerometers are widely used due to their DC measurement interface, thermal stability, reliability, and low cost. However, they are sensitive to electromagnetic field interferences and have poor performance for high-end applications (e.g., precise attitude control for the satellite). Over the past three decades, steady progress has been made in the area of optical accelerometers for high-performance and high-sensitivity applications but several challenges are still to be tackled by researchers and engineers to fully realize opto-mechanical accelerometers, such as chip-scale integration, scaling, low bandwidth, etc

Low-cost wireless structural health monitoring of bridges

Nowadays, low-cost accelerometers are getting more attention from civil engineers to make Structural Health Monitoring (SHM) applications affordable and applicable to a broader range of structures. The present accelerometers based on Arduino or Raspberry Pi technologies in the literature share some of the following drawbacks: (1) high Noise Density (ND), (2) low sampling frequency, (3) not having the Internet’s timestamp with microsecond resolution, (4) not being used in experimental eigenfrequency analysis of a flexible and a less-flexible bridge, and (5) synchronization issues. To solve these problems, a new low-cost triaxial accelerometer based on Arduino technology is presented in this work (Low-cost Adaptable Reliable Accelerometer—LARA). Laboratory test results show that LARA has a ND of 51 µg/vHz, and a frequency sampling speed of 333 Hz. In addition, LARA has been applied to the eigenfrequency analysis of a short-span footbridge and its results are compared with those of a high-precision commercial sensor.The authors are indebted to the Spanish Ministry of Economy and Competitiveness for the funding provided through the research project BIA2017-86811-C2-1-R directed by José Turmo and BIA2017-86811-C2-2-R. All these projects are funded with FEDER funds. The authors are also indebted to the Secretaria d’ Universitats i Recerca de la Generalitat de Catalunya, Catalunya, Spain, for the funding provided through Agaur (2017 SGR 1482). It is also to be noted that funding for this research has been provided for the Seyedmilad Komarizadehasl by the Spanish Agencia Estatal de Investigación del Ministerio de Ciencia Innovación y Universidades grant and the Fondo Social Europeo grant (PRE2018-083238).Peer ReviewedPostprint (published version

Orientation Estimation Through Magneto-Inertial Sensor Fusion: A Heuristic Approach for Suboptimal Parameters Tuning

Magneto-Inertial Measurement Units (MIMUs) are a valid alternative tool to optical stereophotogrammetry in human motion analysis. The orientation of a MIMU may be estimated by using sensor fusion algorithms. Such algorithms require input parameters that are usually set using a trial-and-error (or grid-search ) approach to find the optimal values. However, using trial-and-error requires a known reference orientation, a circumstance rarely occurring in real-life applications. In this article, we present a way to suboptimally set input parameters, by exploiting the assumption that two MIMUs rigidly connected are expected to show no orientation difference during motion. This approach was validated by applying it to the popular complementary filter by Madgwick et al. and tested on 18 experimental conditions including three commercial products, three angular rates, and two dimensionality motion conditions. Two main findings were observed: i) the selection of the optimal parameter value strongly depends on the specific experimental conditions considered, ii) in 15 out of 18 conditions the errors obtained using the proposed approach and the trial-and-error were coincident, while in the other cases the maximum discrepancy amounted to 2.5 deg and less than 1.5 deg on average

Aplicação de meta-heurísticas para afinação de analisadores de espectro de vibração baseados em sistemas microeletromecânicos

Orientadores: Mateus Giesbrecht, Fabiano FruettDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O espectro de vibração mecânica é uma característica do domínio da frequência utilizada para o monitoramento de sistemas diversos e é, tradicionalmente, calculado pela Transformada Rápida de Fourier (FFT) -- do termo em inglês Fast Fourier Transform -- de uma série temporal. Uma alternativa viável, com diversas vantagens operacionais, é o uso de microacelerômetros gêmeos para a obtenção do espectro diretamente no domínio da frequência. Essa estratégia possui sua maior limitação nas diferenças encontradas nos parâmetros físicos de acelerômetros -- devidas a seu processo de fabricação --, de tal forma que o nível de distorção do espectro pode ser consideravelmente superior àquele encontrado no espectro levantado pela FFT. Para contornar essas diferenças, neste trabalho a afinação do microdispositivo analisador de espectro é proposta através do ajuste das amplitudes das tensões de atuação dos acelerômetros. Para realizar a afinação, a Evolução Diferencial (DE, do termo em inglês Differential Evolution) é usada e o problema da afinação é abordado sob duas diferentes perspectivas de otimização: uma mono-objetivo e uma multi-objetivo. Para ambos os problemas de otimização, as funções objetivo e restrições são baseadas nas componentes da série de Fourier do ganho de malha fechada do sistema analisador de espectro -- composição essa que depende das tensões de excitação. Para a solução do problema de otimização multi-objetivo, o algoritmo DE é devidamente adaptado. As vantagens e desvantagens de ambas as estratégias de afinação são discutidas em detalhe, bem como os resultados obtidos para a aproximação do conjunto de Pareto. Esses resultados -- especialmente o compromisso distorção-sensibilidade -- são demonstrados e discutidos. A validade da estratégia de afinação proposta é evidenciada, uma vez que é capaz de determinar as amplitudes das tensões a serem aplicadas ao micro analisador de espectro para atender os requisitos de nível de distorção e sensibilidadeAbstract: The mechanical vibration spectrum is a frequency-domain characteristic used for monitoring various systems and is traditionally calculated by the Fast Fourier Transform (FFT) of a time series. Another possible alternative, with several operational advantages, is the use of twin-microaccelerometers to obtain the spectrum directly in the frequency domain. This strategy has its greatest limitation in the differences found in the accelerometers physical parameters -- due to their manufacturing process --, such that the spectrum distortion level may be considerably higher than that found in the spectrum raised by the FFT. To overcome these differences, in this work the tuning of the spectrum analyzer microdevice is proposed by adjusting the accelerometers actuation voltages amplitudes. To perform the tuning, the Differential Evolution (DE) is used and the tuning problem is approached in two different optimization perspectives: a mono-objective and a multi-objective. For both optimization problems, the objective functions and constraints are based on the Fourier series components of the spectrum analyzer system closed-loop gain -- a composition that depends on the excitation voltages. To solve the multi-objective optimization problem, the DE algorithm is properly adapted. The advantages and disadvantages of both tuning strategies are discussed in detail, as well as the results obtained for the Pareto-set approximation. The results -- specially the distortion-sensitivity compromise -- are demonstrated and discussed. The validity of the proposed tuning strategy is evidenced, since it is able to determine the voltages amplitudes to be applied to the micro spectrum analyzer to attend the distortion level and sensitivity requirementsMestradoAutomaçãoMestra em Engenharia Elétrica161153/2018-6CNP