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One of the crucial challenges for Data Center (DC) operation is inefficient thermal management which leads to excessive energy waste. The information technology (IT) equipment and cooling systems of a DC are major contributors to power consumption. Additionally, failure of a DC cooling system leads to higher operating temperatures, causing critical electronic devices, such as servers, to fail which leads to significant economic loss. Improvements can be made in two ways, through (1) better design of a DC architecture and (2) optimization of the system for better heat transfer from hot servers. Row-based cooling is a suitable DC configuration that reduces energy costs by improving airflow distribution. Here, the IT equipment is contained within an enclosure that includes a cooling unit which separates cold and back chambers to eliminate hot air recirculation and cold air bypass, both of which produce undesirable airflow distributions. Besides, due to scalability, ease of implementation, and operational cost, row-based systems have gained in popularity for DC computing applications. However, a general thermal model is required to predict spatiotemporal temperature changes inside the DC and properly apply appropriate strategies. As yet, only primitive tools have been developed that are time-consuming and provide unacceptable errors during extrapolative predictions. We address these deficiencies by developing a rapid, adaptive, and accurate hybrid model by combining a DDM and the thermofluid transport relations to predict temperatures in a DC. Our hybrid model has low interpolative prediction errors below 0.7 oC and extrapolative errors less than one half of black-box models. Additionally, by changing the studied DC configuration such as cooling unit fans and severs locations, there are a few zones with prediction error more than 2 oC. Existing methods for cooling unit fault detection and diagnosis (FDD) are designed to successfully overcome individually occurring faults but have difficulty handling simultaneous faults. We apply a gray-box model involves a case study to detect and diagnose cooling unit fan and pump failure in a row-based DC cooling system. Fast detection of anomalous behavior saves energy and reduces operational costs by initiating remedial actions. Cooling unit fans and pumps are relatively low-reliability components, where the failure of one or more components can cause the entire system to overheat. Therefore, appropriate energy-saving strategies depend largely on the accuracy and timeliness of temperature prediction models. We used our gray-box model to produce thermal maps of the DC airspace for single as well as simultaneous failure conditions, which are fed as inputs for two different data-driven classifiers, CNN and RNN, to rapidly predict multiple simultaneous failures. Our FDD strategy can detect and diagnose multiple faults with accuracy as high as 100% while requiring relatively few simultaneous fault training data samples. / Thesis / Candidate in Philosophy

Kalman-type filtering methods are mostly designed based on exact knowledge of the system’s model with known parameters. In real applications, there may be considerable amount of uncertainties about the model structure, physical parameters, level of noise, and initial conditions. In order to overcome such difficulties, robust state estimation techniques are recommended. This PhD thesis presents a novel robust state estimation method that is referred to as the 2nd-order smooth variable structure filter (2nd-order SVSF) and satisfies the first and second order sliding conditions. It is an extension to the 1st-order SVSF introduced in 2007. In the 1st-order SVSF chattering is reduced by using a smoothing boundary layer; however, the 2nd-order SVSF alleviates chattering by preserving the second order sliding condition. It reduces the estimation error and its first difference until the existence boundary layer is reached. Then after, it guarantees that the estimation error and its difference remain bounded given bounded noise and modeling uncertainties. As such, the 2nd-order SVSF produces more accurate and smoother state estimates under highly uncertain conditions than the 1st-order version. The main issue with the 2nd-order SVSF is that it is not optimal in the mean square error sense. In order to overcome this issue, the dynamic 2nd-order SVSF is initially presented based on a dynamic sliding mode manifold. This manifold introduces a variable cut-off frequency coefficient that adjusts the filter bandwidth. An optimal derivation of the 2nd-order SVSF is then obtained by minimizing the state error covariance matrix with respect to the cut-off frequency matrix. An experimental setup of an electro-hydrostatic actuator is used to compare the performance of the 2nd-order SVSF and its optimal version with other estimation methods such as the Kalman filter and the 1st-order SVSF. Experiments confirm the superior performance of the 2nd-order SVSF given modeling uncertainties. / Thesis / Doctor of Philosophy (PhD)

This work explores two areas of research. The first is the development of a video utility metric for use in aerial surveillance and reconnaissance tasks. To our knowledge, metrics that compute how useful aerial video is to a human in the context of performing tasks like detection, recognition, or identification (DRI) do not exist. However, the Targeting Task Performance (TTP) metric was previously developed to estimate the usefulness of still images for DRI tasks. We modify and extend the TTP metric to create a similar metric for video, called Video Targeting Task Performance (VTTP). The VTTP metric accounts for various things like the amount of lighting, motion blur, human vision, and the size of an object in the image. VTTP can also be predictively calculated to estimate the utility that a proposed flight path will yield. This allows it to be used to help automate path planning so that operators are able to devote more of their attention to DRI. We have used the metric to plan and fly actual paths. We also carried out a small user study that verified that VTTP correlates with subjective human assessment of video. The second area of research explores a new method of detecting GPS spoofing on an unmanned aerial system (UAS) equipped with a camera and a terrain elevation map. Spoofing allows an attacker to remotely tamper with the position, time, and velocity readings output by a GPS receiver. This tampering can throw off the UAS's state estimates, but the optical flow through the camera still depends on the actual movement of the UAS. We develop a method of detecting spoofing by calculating the expected optical flow based on the state estimates and comparing it against the actual optical flow. If the UAS is successfully spoofed to a different location, then the detector can also be triggered by differences in the terrain between where the UAS actually is and where it thinks it is. We tested the spoofing detector in simulation, and found that it works well in some scenarios.

[pt] Este trabalho descreve a concepção e realização de um microcomputador para aplicações onde há necessidade de um elevado grau de segurança. A transmissão incorreta de dados pode ser evitada: - com a utilização de circuitos auto-testáveis - pelo bloqueio do seu funcionamento no caso de deteção de falha. O microcomputador é baseado no microprocessador 8080 da Intel e apresenta as seguintes características: -toda a parte em funcionamento do sistema é testada - o diagnóstico é feito no nível de placas, sendo a placa defeituosa indicada no painel após o bloqueio do computador - deteção nas unidades memória através de bits de paridade 8 portas de entrada/saída uma avaliação do número de circuitos utilizados é apresentada em função do tamanho de memória. / [en] The design and realization of a microcomputer for high security level applications is described. Erroneous data transmission may be prevented by: -self - checking circuits -blocking the computer upon detection of a failure. The microcomputer is built around the Intel 8080 microprocessor and has the followinf characteristics: - All the working circuits are tested. - a card level dragnosis is provided with indication by panel of the faulty unit, atter computer stop. - memory detection by parity bits - 8 I/0 ports. An evaluation of the number of the envisaged memory is presented.

The proposed research is devoted to devising system identification and fault detection approaches and algorithms for a system characterized by nonlinear dynamics. Mathematical models of dynamical systems and fault models are built based on observed data from systems. In particular, we will focus on statistical subspace instrumental variable methods which allow the consideration of an appealing mathematical model in many control applications consisting of a nonlinear feedback system with nonlinearities at both inputs and outputs. Different solutions within the proposed framework are presented to solve the system identification and fault detection problems. Specifically, Augmented Subspace Instrumental Variable Identification (ASIVID) approaches are proposed to identify the closed-loop nonlinear Hammerstein systems. Then fast approaches are presented to determine the system order. Hard-over failures are detected by order determination approaches when failures manifest themselves as rank deficiencies of the dynamical systems. Geometric interpretations of subspace tracking theorems are presented in this dissertation in order to propose a fault tolerance strategy. Possible fields of application considered in this research include manufacturing systems, autonomous vehicle systems, space systems and burgeoning bio-mechanical systems.

<p> Roller bearing is one of the machine industry’s common components. The roller bearing operation status is usually related to production efficiency. Failure of bearings during operation will cause downtime and severe economic losses. To prevent this situation, the proposal of effective bearing fault diagnosis methods has become a popular research topic. This thesis research first validates several popular bearing diagnosis methods based on signal processing and machine learning. Second, a novel signal feature extraction method called sparse wavelet packet transform (WPT) decomposition and a corresponding feature learning model called multi-scale and multi-rate convolutional neural network (MSMR-CNN) are proposed. Finally, the proposed method is verified using both Case Western Reserve University (CWRU) dataset and the self-collected dataset. The results demonstrate that our proposed MSMR-CNN method achieves higher performance of bearing fault classification accuracy in comparison with the methods which are recently proposed by the other researchers using machine learning and neural networks .</p>

Background : The heat load has an emergent role in the energy consumption of the heating system in buildings. The industry experts also have been constantly focusing on the heat load optimization techniques and in the recent years, numerous Machine Learning (ML) techniques have come into picture to resolve various tasks. Objectives : This study is mainly focused on to analyze the time-series hourly data and choose suitable Supervised Machine Learning approach among Multivariate Linear Regression (MLR), Support Vector Regression, and Multi-layer Perceptron (MLP) Regressor so as to predict heat demand for identifying the deviating behaviors and potentially faults. Methods : An experiment is performed and the method consists of imputing the missing values, extreme values and selection of six different feature sets. Cross validation on Multivariate Linear Regression, Support Vector Regression, and Multi-layer Perceptron Regressor was performed to find the best suitable algorithm. Finally the residuals of the best algorithm and the best feature set was used to find the fault using the calculation of studentized residuals. Because of the time-series based data in data set, regression based algorithms was the best suitable choice to work with such type of data that is continuous. The faults in the system were identified based on the studentized residuals that exceeds the threshold value of 3 are classified as fault. Results : Among the regression based algorithms, Multi-layer Perceptron Regressor resulted in Mean Absolute Error (MAE) of 1.77 and Mean Absolute Percentage Error (MAPE) 0.29% on the feature set 1. Multivariate Linear Regression shown Mean Absolute Error 1.83 and Mean Absolute Percentage Error 0.31% on feature set 1 that has relatively higher error for the metrics of Mean Absolute Error and Mean Absolute Percentage Error as comparing to Multi-layer Perceptron Regressor. Support Vector Regression (SVR) shown Mean Absolute Error 2.54 that is higher than that of both Multivariate Linear Regression and Multi-layer Perceptron Regressor, while theMean Absolute Percentage Error 0.24% that is similar to Multivariate Linear Regression and Multi-layer Perceptron Regressor on the feature set 1. So the best performing algorithm is Multi-layer Perceptron Regressor. The feature sets 4,5 and 6 which are super-sets of 1, 2 and 3 feature sets along with addition of outdoor temperature. These feature sets 4, 5 and 6 did not show much impact even after considering the outdoor temperature. From, the Table 5.1 the feature sets 1, 2 and 3 are comparitively better than feature sets 4, 5 and 6 for the metrics Mean Absolute Error and Mean Absolute Percentage Error.Finally on comparing the first three feature sets, the feature set 1 resulted in less error for all three algorithms as comparing to feature set 2 and feature set 3 that can be seen in Table 5.1. So the feature set 1 is the best feature set. Conclusions : Multi-layer Perceptron Regressor perfomed well on six different feature sets comparing with Multivariate Linear Regression and Support Vector Regression. The feature set 1 had shown Mean Absolute Error and Mean Absolute Percentage values relatively low than other feature sets. Therefore the feature set 1 was the best performing and the best suited algorithm was Multi-layer Perceptron Regressor. The Figure A.3 represents the flow of work done in the thesis.

Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous studies in this area but few which consider the problem in a practical sense with the aim of developing a single system that can be used to monitor motor condition under a range of different conditions, with different motor specifications and loads. This thesis aims to address some of these problems by developing a general FDD system for induction motor. The solution of this problem involved the development and testing of a new approach; the adaptive mixed-residual approach (AMRA). The main aim of the AMRA system is to avoid the vast majority of unplanned failures of the machine and therefore as opposed to tackling a single induction motor fault, the system is developed to detect all four of the most statistically prevalent induction motor fault types; rotor fault, stator fault, air-gap fault and bearing fault. The mixed-residual fault detection algorithm is used to detect these fault types which includes a combination of spectral and model-based techniques coupled with particle swarm optimisation (PSO) for automatic identification of motor parameters. The AMRA residuals are analysed by a fuzzy-logic classifier and the system requires only current and voltage inputs to operate. Validation results indicate that the system performs well under a range of load torques and different coupling methods proving it to have significant potential for use in industrial applications. / The full-text was made available at the end of the embargo period on 29th Sept 2017.

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