Fault detection in rotating machines by vibration signal processing techniques

Machines with moving parts give rise to vibrations and consequently noise. The setting up and the status of each machine yield to a peculiar vibration signature. Therefore, a change in the vibration signature, due to a change in the machine state, can be used to detect incipient defects before they become critical. This is the goal of condition monitoring, in which the informations obtained from a machine signature are used in order to detect faults at an early stage. There are a large number of signal processing techniques that can be used in order to extract interesting information from a measured vibration signal. This study seeks to detect rotating machine defects using a range of techniques including synchronous time averaging, Hilbert transform-based demodulation, continuous wavelet transform, Wigner-Ville distribution and spectral correlation density function. The detection and the diagnostic capability of these techniques are discussed and compared on the basis of experimental results concerning gear tooth faults, i.e. fatigue crack at the tooth root and tooth spalls of different sizes, as well as assembly faults in diesel engine. Moreover, the sensitivity to fault severity is assessed by the application of these signal processing techniques to gear tooth faults of different sizes.

Influence of processing conditions on quality aspects of potato-based fresh pasta

Gnocchi is a typical Italian potato-based fresh pasta that can be either homemade or industrially manufactured. The homemade traditional product is consumed fresh on the day it is produced, whereas the industrially manufactured one is vacuum-packed in polyethylene and usually stored at refrigerated conditions. At industrial level, most kinds of gnocchi are usually produced by using some potato derivatives (i.e. flakes, dehydrated products or flour) to which soft wheat flour, salt, some emulsifiers and aromas are added. Recently, a novel type of gnocchi emerged on the Italian pasta market, since it would be as much similar as possible to the traditional homemade one. It is industrially produced from fresh potatoes as main ingredient and soft wheat flour, pasteurized liquid eggs and salt, moreover this product undergoes steam cooking and mashing industrial treatments. Neither preservatives nor emulsifiers are included in the recipe. The main aim of this work was to get inside the industrial manufacture of gnocchi, in order to improve the quality characteristics of the final product, by the study of the main steps of the production, starting from the raw and steam cooked tubers, through the semi-finished materials, such as the potato puree and the formulated dough. For this purpose the investigation of the enzymatic activity of the raw and steam cooked potatoes, the main characteristics of the puree (colour, texture and starch), the interaction among ingredients of differently formulated doughs and the basic quality aspects of the final product have been performed. Results obtained in this work indicated that steam cooking influenced the analysed enzymes (Pectin methylesterase and α- and β-amylases) in different tissues of the tuber. PME resulted still active in the cortex, it therefore may affect the texture of cooked potatoes to be used as main ingredient in the production of gnocchi. Starch degrading enzymes (α- and β-amylases) were inactivated both in the cortex and in the pith of the tuber. The study performed on the potato puree showed that, between the two analysed samples, the product which employed dual lower pressure treatments seemed to be the most suitable to the production of gnocchi, in terms of its better physicochemical and textural properties. It did not evidence aggregation phenomena responsible of hard lumps, which may occur in this kind of semi-finished product. The textural properties of gnocchi doughs were not influenced by the different formulation as expected. Among the ingredients involved in the preparation of the different samples, soft wheat flour seemed to be the most crucial in affecting the quality features of gnocchi doughs. As a consequence of the interactive effect of the ingredients on the physicochemical and textural characteristics of the different doughs, a uniform and well-defined split-up among samples was not obtained. In the comparison of different kinds of gnocchi, the optimal physicochemical and textural properties were detected in the sample made with fresh tubers. This was probably caused not only by the use of fresh steam cooked potatoes, but also by the pasteurized liquid eggs and by the absence of any kind of emulsifier, additive or preserving substance.

Physiological and structural aspects of fruit and vegetable mild processing

Over the past years fruit and vegetable industry has become interested in the application of both osmotic dehydration and vacuum impregnation as mild technologies because of their low temperature and energy requirements. Osmotic dehydration is a partial dewatering process by immersion of cellular tissue in hypertonic solution. The diffusion of water from the vegetable tissue to the solution is usually accompanied by the simultaneous solutes counter-diffusion into the tissue. Vacuum impregnation is a unit operation in which porous products are immersed in a solution and subjected to a two-steps pressure change. The first step (vacuum increase) consists of the reduction of the pressure in a solid-liquid system and the gas in the product pores is expanded, partially flowing out. When the atmospheric pressure is restored (second step), the residual gas in the pores compresses and the external liquid flows into the pores. This unit operation allows introducing specific solutes in the tissue, e.g. antioxidants, pH regulators, preservatives, cryoprotectancts. Fruit and vegetable interact dynamically with the environment and the present study attempts to enhance our understanding on the structural, physico-chemical and metabolic changes of plant tissues upon the application of technological processes (osmotic dehydration and vacuum impregnation), by following a multianalytical approach. Macro (low-frequency nuclear magnetic resonance), micro (light microscopy) and ultrastructural (transmission electron microscopy) measurements combined with textural and differential scanning calorimetry analysis allowed evaluating the effects of individual osmotic dehydration or vacuum impregnation processes on (i) the interaction between air and liquid in real plant tissues, (ii) the plant tissue water state and (iii) the cell compartments. Isothermal calorimetry, respiration and photosynthesis determinations led to investigate the metabolic changes upon the application of osmotic dehydration or vacuum impregnation. The proposed multianalytical approach should enable both better designs of processing technologies and estimations of their effects on tissue.

Adaptive multiscale biological signal processing

Biological processes are very complex mechanisms, most of them being accompanied by or manifested as signals that reflect their essential characteristics and qualities. The development of diagnostic techniques based on signal and image acquisition from the human body is commonly retained as one of the propelling factors in the advancements in medicine and biosciences recorded in the recent past. It is a fact that the instruments used for biological signal and image recording, like any other acquisition system, are affected by non-idealities which, by different degrees, negatively impact on the accuracy of the recording. This work discusses how it is possible to attenuate, and ideally to remove, these effects, with a particular attention toward ultrasound imaging and extracellular recordings. Original algorithms developed during the Ph.D. research activity will be examined and compared to ones in literature tackling the same problems; results will be drawn on the base of comparative tests on both synthetic and in-vivo acquisitions, evaluating standard metrics in the respective field of application. All the developed algorithms share an adaptive approach to signal analysis, meaning that their behavior is not dependent only on designer choices, but driven by input signal characteristics too. Performance comparisons following the state of the art concerning image quality assessment, contrast gain estimation and resolution gain quantification as well as visual inspection highlighted very good results featured by the proposed ultrasound image deconvolution and restoring algorithms: axial resolution up to 5 times better than algorithms in literature are possible. Concerning extracellular recordings, the results of the proposed denoising technique compared to other signal processing algorithms pointed out an improvement of the state of the art of almost 4 dB.

Vegetables biofortified with iodine: chemical, agronomic and qualitative aspects of raw and fried potatoes

Iodine is an essential microelement for human health because it is a constituent of the thyroid hormones that regulate growth and development of the organism. Iodine Deficiency Disorders (IDDs) are believed to be one of the commonest preventable human health problems in the world today, according to the World Health Organization: that diseases include endemic goiter, cretinism and fetal abnormalities, among others, and they are caused by lack of iodine in the diet, that is the main source of iodine. Since iodine intake from food is not enough respect to human needs, this can be remedied through dietary diversification, mineral supplementation, food fortification, or increasing the concentration and/or bioavailability of mineral elements in the edible portions of crops through agricultural intervention or genetic selection (biofortification). The introduction of iodized salt is a strategy widely used and accepted to eradicate iodine deficiency, because it is an inexpensive source of stable iodine. Since the intake of salt, though iodized, must still be limited according to the risk of cardiovascular disease, so the increase of iodine content in plants for the production of functional foods is representing a field of study of particular interest and a potential market. In Italy potatoes enriched with iodine are produced by a patented procedure of agronomic biofortification for the fresh market since several years, furthermore they are recently accepted and recommended by Italian Thyroid Association, as an alternative source of iodine. Researches performed during the PhD course intended to characterize this innovative vegetables products, focusing the attention on different aspects, such as chemistry, agriculture, and quality of fresh and fried potatoes. For this purpose, lipid fraction of raw material was firstly investigated, in order to assess whether the presence of iodine in plant metabolism can affect fatty acid or sterol biosynthesis, according to the hypothesis that iodine can be bounded to polyunsaturated fatty acids of cell membranes, protecting them from peroxydation; phytosterols of plant sterol are also studied because their importance in reducing serum cholesterol, especially in potato plant sterols are also involved in synthesis of glycoalkaloid, a family of steroidal toxic secondary metabolites present in plants of the Solanaceae family. To achieve this goal chromatographic analytical techniques were employed to identify and quantify fatty acids and sterols profile of common and iodine enriched row potatoes. Another aim of the project was to evaluate the effects of frying on the quality of iodine-enriched and common potatoes. Since iodine-enriched potatoes are nowadays produced only for the fresh market, preliminary trials of cultivation under controlled environment were carried out to verify if potato varieties suitable for processing were able to absorb and accumulate iodine in the tuber. In a successive phase, these varieties were grown in the field, to evaluate their potential productivity and quality at harvest and after storage. The best potato variety to be destined for processing purposes, was finally subjected to repeated frying cycles; the effects of lipid oxidation on the composition and quality of both potatoes and frying oil bath were evaluated by chromatographic and spectrophotometric analytical techniques. Special attention were paid on volatile compounds of fried potatoes.

Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included.

Design and implementation of a novel multi-constellation FPGA-based dual frequency GNSS receiver for space applications

The PhD activity described in the document is part of the Microsatellite and Microsystem Laboratory of the II Faculty of Engineering, University of Bologna. The main objective is the design and development of a GNSS receiver for the orbit determination of microsatellites in low earth orbit. The development starts from the electronic design and goes up to the implementation of the navigation algorithms, covering all the aspects that are involved in this type of applications. The use of GPS receivers for orbit determination is a consolidated application used in many space missions, but the development of the new GNSS system within few years, such as the European Galileo, the Chinese COMPASS and the Russian modernized GLONASS, proposes new challenges and offers new opportunities to increase the orbit determination performances. The evaluation of improvements coming from the new systems together with the implementation of a receiver that is compatible with at least one of the new systems, are the main activities of the PhD. The activities can be divided in three section: receiver requirements definition and prototype implementation, design and analysis of the GNSS signal tracking algorithms, and design and analysis of the navigation algorithms. The receiver prototype is based on a Virtex FPGA by Xilinx, and includes a PowerPC processor. The architecture follows the software defined radio paradigm, so most of signal processing is performed in software while only what is strictly necessary is done in hardware. The tracking algorithms are implemented as a combination of Phase Locked Loop and Frequency Locked Loop for the carrier, and Delay Locked Loop with variable bandwidth for the code. The navigation algorithm is based on the extended Kalman filter and includes an accurate LEO orbit model.

Ultra-low power WSNs: distributed signal processing and dynamic resource management

This thesis presents several data processing and compression techniques capable of addressing the strict requirements of wireless sensor networks. After introducing a general overview of sensor networks, the energy problem is introduced, dividing the different energy reduction approaches according to the different subsystem they try to optimize. To manage the complexity brought by these techniques, a quick overview of the most common middlewares for WSNs is given, describing in detail SPINE2, a framework for data processing in the node environment. The focus is then shifted on the in-network aggregation techniques, used to reduce data sent by the network nodes trying to prolong the network lifetime as long as possible. Among the several techniques, the most promising approach is the Compressive Sensing (CS). To investigate this technique, a practical implementation of the algorithm is compared against a simpler aggregation scheme, deriving a mixed algorithm able to successfully reduce the power consumption. The analysis moves from compression implemented on single nodes to CS for signal ensembles, trying to exploit the correlations among sensors and nodes to improve compression and reconstruction quality. The two main techniques for signal ensembles, Distributed CS (DCS) and Kronecker CS (KCS), are introduced and compared against a common set of data gathered by real deployments. The best trade-off between reconstruction quality and power consumption is then investigated. The usage of CS is also addressed when the signal of interest is sampled at a Sub-Nyquist rate, evaluating the reconstruction performance. Finally the group sparsity CS (GS-CS) is compared to another well-known technique for reconstruction of signals from an highly sub-sampled version. These two frameworks are compared again against a real data-set and an insightful analysis of the trade-off between reconstruction quality and lifetime is given.

Efficient ultrasonic signal processing techniques for aided medical diagnostics

Ultrasound imaging is widely used in medical diagnostics as it is the fastest, least invasive, and least expensive imaging modality. However, ultrasound images are intrinsically difficult to be interpreted. In this scenario, Computer Aided Detection (CAD) systems can be used to support physicians during diagnosis providing them a second opinion. This thesis discusses efficient ultrasound processing techniques for computer aided medical diagnostics, focusing on two major topics: (i) Ultrasound Tissue Characterization (UTC), aimed at characterizing and differentiating between healthy and diseased tissue; (ii) Ultrasound Image Segmentation (UIS), aimed at detecting the boundaries of anatomical structures to automatically measure organ dimensions and compute clinically relevant functional indices. Research on UTC produced a CAD tool for Prostate Cancer detection to improve the biopsy protocol. In particular, this thesis contributes with: (i) the development of a robust classification system; (ii) the exploitation of parallel computing on GPU for real-time performance; (iii) the introduction of both an innovative Semi-Supervised Learning algorithm and a novel supervised/semi-supervised learning scheme for CAD system training that improve system performance reducing data collection effort and avoiding collected data wasting. The tool provides physicians a risk map highlighting suspect tissue areas, allowing them to perform a lesion-directed biopsy. Clinical validation demonstrated the system validity as a diagnostic support tool and its effectiveness at reducing the number of biopsy cores requested for an accurate diagnosis. For UIS the research developed a heart disease diagnostic tool based on Real-Time 3D Echocardiography. Thesis contributions to this application are: (i) the development of an automated GPU based level-set segmentation framework for 3D images; (ii) the application of this framework to the myocardium segmentation. Experimental results showed the high efficiency and flexibility of the proposed framework. Its effectiveness as a tool for quantitative analysis of 3D cardiac morphology and function was demonstrated through clinical validation.

Diagnosis and Fault detection in Electrical Machines and Drives based on Advanced Signal Processing Techniques

In the present thesis, a new methodology of diagnosis based on advanced use of time-frequency technique analysis is presented. More precisely, a new fault index that allows tracking individual fault components in a single frequency band is defined. More in detail, a frequency sliding is applied to the signals being analyzed (currents, voltages, vibration signals), so that each single fault frequency component is shifted into a prefixed single frequency band. Then, the discrete Wavelet Transform is applied to the resulting signal to extract the fault signature in the frequency band that has been chosen. Once the state of the machine has been qualitatively diagnosed, a quantitative evaluation of the fault degree is necessary. For this purpose, a fault index based on the energy calculation of approximation and/or detail signals resulting from wavelet decomposition has been introduced to quantify the fault extend. The main advantages of the developed new method over existing Diagnosis techniques are the following: - Capability of monitoring the fault evolution continuously over time under any transient operating condition; - Speed/slip measurement or estimation is not required; - Higher accuracy in filtering frequency components around the fundamental in case of rotor faults; - Reduction in the likelihood of false indications by avoiding confusion with other fault harmonics (the contribution of the most relevant fault frequency components under speed-varying conditions are clamped in a single frequency band); - Low memory requirement due to low sampling frequency; - Reduction in the latency of time processing (no requirement of repeated sampling operation).