A Fitted empirical demodulation for low frequency electrical signal evaluation

Empirical Demodulation (ED) is a technique used to build a discrete signal, called an empirical envelope, from a modulated time sequence. For example, in three-phase induction motors, this envelope can carry fault frequency data that allows the machine health status to be evaluated during a spectral analysis. However, due to mathematical reasons, the method is very sensitive to the amplitude oscillations within the signal. When these oscillations are unwanted, as in the presence of measurement noise, the results can be strongly affected. This work proposes an iterative and adjustable version of the ED that considerably reduces its sensitivity to the presence of high frequency noise, thus eliminating the need for signal pre-filtering. To prove the effectiveness of the Fitted Empirical Demodulation, the authors applied the new tool in motor current signals for analysis of the rotor bars conditions

Improvement Procedure for Image Segmentation of Fruits and Vegetables Based on the Otsu Method

Currently, there are significant challenges in the classification, recognition, and detection of fruits and vegetables. An important step in solving this problem is to obtain an accurate segmentation of the object of interest. However, the background and object separation in a grayscale image shows high errors for some thresholding techniques due to uneven or poorly conditioned lighting. An accepted strategy to reduce segmentation errors is to select the channel of an RGB image with high contrast. This paper presents the results of an experimental procedure based on enhancing binary segmentation by using the Otsu method. The procedure was carried out with images of real agricultural products, both with and without additional noise, to corroborate the robustness of the proposed strategy. The experimental tests were performed using our database of RGB images of agricultural products under uncontrolled illumination. The results show that the best segmentation is achieved by selecting the Blue channel of the RGB test images due to its higher contrast. Here, the quantitative results are measured by applying the Jaccard and Dice metrics based on the ground-truth images as optimal reference. Most of the results using both metrics show an improvement greater than 45.5% in the two experimental tests

Advances in the Field of Electrical Machines and Drives

Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

Sviluppo di una Metodologia per la Selezione e il Controllo Qualità di Ventilatori per Cappe Aspiranti in Linea di Produzione Mediante Analisi Vibrazionale

Fin dai primi anni del secolo scorso i ricercatori hanno condotto ricerche e sviluppato soluzioni per diagnosticare l’insorgere di difettosità nei motori ad induzione per aumentarne l’affidabilità e la qualità. La letteratura è ricca di esempi nei quali vengono utilizzate le più conosciute tecniche di elaborazione del segnale e negli ultimi anni l’utilizzo di algoritmi di intelligenza artificiale ha portato ad ulteriori miglioramenti nella prevenzione dei guasti e delle loro conseguenze. In questo lavoro viene presentato un approccio differente per diagnosticare la presenza di difettosità nei motori ad induzione, una metodologia originale per il tipo di applicazione basata sul calcolo delle divergenze statistiche tra distribuzioni di probabilità e sul calcolo delle entropie e della cross-entropia. Vengono proposti e confrontati cinque diversi metodi per ottenere le distribuzioni di probabilità dai segnali misurati, due differenti formulazioni per il calcolo delle divergenze e quattro per il calcolo dell’entropia. L’efficacia e la maggiore robustezza degli indicatori calcolati con il metodo proposto rispetto ai tradizionali indicatori statistici sono dimostrate tramite le analisi condotte sulle misure accelerometriche acquisite durante lo sviluppo della procedura per il controllo qualità dei ventilatori per cappe aspiranti uscenti dalla linea di produzione di SIT S.p.A. Ne viene presentata inoltre una versione modificata utilizzando la trasmissibilità del banco di collaudo come filtro inverso, soluzione che la rende efficace anche quando applicata alle misure acquisite dal sensore accelerometrico di linea. La procedura proposta ha dimostrato capacità di classificazione con un accuratezza superiore al 95%. Infine, sfruttando le potenzialità del machine learning, viene proposta una soluzione che, utilizzando un Autoencoder, è in grado di migliorare i risultati ottenuti in precedenza, raggiungendo valori analoghi come accuratezza ma migliori in termini di falsi negativi.Since the early years of the last century, researchers have conducted research and developed solutions to diagnose the onset of defects in induction motors to increase their reliability and quality. The literature is full of examples in which the well-known signal processing techniques are used and in recent years the use of artificial intelligence algorithms has led to further improvements in the prevention of faults and their consequences. In this work a different approach is presented to diagnose the presence of defects in induction motors, an original methodology for the type of application based on the calculation of statistical divergences between probability distributions and on the calculation of entropies and cross-entropy. Five different methods for obtaining probability distributions from measured signals are proposed and compared, two different formulations for calculating divergences and four for calculating entropy. The effectiveness and greater robustness of the indicators calculated with the proposed method compared to traditional statistical indicators are demonstrated through the analyses conducted on the accelerometric measurements acquired during the development of the procedure for the quality control of the fans for extractor hoods leaving the production line of SIT S.p.A. A modified version is also presented using the transmissibility of the production bench as an inverse filter, a solution that makes it effective even when applied to the measurements acquired by the accelerometric sensor positioned on the production station. The proposed procedure has demonstrated classification capabilities with an accuracy greater than 95%. Finally, exploiting the potential of machine learning, a solution is proposed which, using an Autoencoder, is able to improve the results previously obtained, reaching similar values in terms of accuracy but better in terms of false negatives

Using Time-Resolved Electron Microscopy And Data Analytics To Quantify The Evolution Of Supported Metal Nanoparticles

Supported precious metal nanoparticles are important heterogeneous catalysts for both industrial processes and commercial products. Their high catalytic activity stems from their high surface free energy and under-coordinated surfaces, however these same properties destabilize the particles and cause them to grow and deactivate. While research studying the degradation of supported catalysts has been undertaken for decades, the exact mechanisms at play, and how the vary with reaction conditions, are not well understood. Advances in experimental instrumentation have positioned Transmission Electron Microscopy (TEM) as an ideal tool for characterizing the dynamic evolution of these nanoscale systems with both high spatial and temporal resolution. However, the difficulty of manually analyzing large in situ datasets to quantify nanostructural evolution remains a challenge. This dissertation focuses on combining in situ experimental observations with machine learning and data analytics to quantify image data and understand nanoparticle coarsening. The first thrust of this research is developing a machine-learning pipeline for automated image segmentation. By optimizing state-of-the-art deep learning segmentation models, we were able to rapidly segment and measure particles from thousands of TEM images in a reliable and reproducible fashion. Utilizing this automated image processing pipeline, we observed the evolution of a model catalyst at high temperature and assessed the competition between coarsening by evaporation and surface diffusion as a function of particle size and temperature. After developing a physical model to describe each mechanism, we were able to characterize particle interactions along the support and to identify a critical particle size which avoids degradation. Finally, we used a combination of temperature-dependent in situ experiments and Kinetic Monte Carlo simulations to understand how the rate of nanoparticle evaporation depends on nanoparticle morphology. Our mechanistic model allows us to understand how random structural fluctuations and surface roughening contribute to the evaporation process. In all, this research aims at developing techniques and data-rich quantitative methods for understanding how supported nanocatalysts can be engineered for optimal activity and lifetime

Structural Health Monitoring using Unmanned Aerial Systems

The use of Structural Health Monitoring (SHM) techniques is paramount to the safety and longevity of the structures. Many fields use this approach to monitor the performance of a system through time to determine the proper time and funds associated with repair and replacement. The monitoring of these systems includes nondestructive testing techniques (NDT), sensors permanently installed on the structure, and can also rely heavily on visual inspection. Visual inspection is widely used due to the level of trust owners have in the inspection personnel, however it is time consuming, expensive, and relies heavily on the experience of the inspectors. It is for these reasons that rapid data acquisition platforms must be developed using remote sensing systems to collect, process, and display data to decision makers quickly to make well informed decisions based on quantitative data or provide information for further inspection with a contact technique for targeted inspection. The proposed multirotor Unmanned Aerial System (UAS) platform carries a multispectral imaging payload to collect data and serve as another tool in the SHM cycle. Several demonstrations were performed in a laboratory setting using UAS acquired imagery for identification and measurement of structures. Outdoor validation was completed using a simulated bridge deck and ground based setups on in service structures. Finally, static laboratory measurements were obtained using multispectral patterns to obtain multiscale deformation measurements that will be required for use on a UAS. The novel multiscale, multispectral image analysis using UAS acquired imagery demonstrates the value of the remote sensing system as a nondestructive testing platform and tool for SHM.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201