Showing papers by "Sauro Longhi published in 2015"

Electric Motor Fault Detection and Diagnosis by Kernel Density Estimation and Kullback–Leibler Divergence Based on Stator Current Measurements

Abstract: This paper deals with the problem of fault detection and diagnosis of induction motor based on motor current signature analysis. Principal component analysis is used to reduce the three-phase current space to a 2-D space. Kernel density estimation (KDE) is adopted to evaluate the probability density functions of each healthy and faulty motor, which can be used as features in order to identify each fault. Kullback–Leibler divergence is used as an index to identify the dissimilarity between two probability distributions, and it allows automatic fault identification. The aim is also to improve computational performance in order to apply online a monitoring system. KDE is improved by fast Gaussian transform and a points reduction procedure. Since these techniques achieve a remarkable computational cost reduction with respect to the standard KDE, the algorithm can be used online. Experiments are carried out using two alternate current motors: an asynchronous induction machine and a single-phase motor. The faults considered to test the developed algorithm are cracked rotor, out-of-tolerance geometry rotor, and backlash. Tests are carried out at different load and voltage levels to show the proposed method performance.

High-resolution mapping of river and estuary areas by using unmanned aerial and surface platforms

Abstract: The accurate and precise mapping of Land Use / Land Cover (LU/LC) plays a key role in the management of a region In particular the knowledge of areas close to rivers and / or estuaries is fundamental to prevent flooding preserving the integrity of artificial structures and natural flora and fauna Actually the mapping is performed by processing remote sensed data with a typical GSD of 05m The survey is expensive and the presence of clouds could invalidate an entire survey This kind of survey is perfect for a monitoring over a long period while is not appropriate for a quasi real time imagery The adoption of unmanned aerial vehicles (UAVs) is a proper solution owing to the low cost and the reduced time for the setup, but it lacks of a complete view of the area The reason is the presence of canopy that occludes the view We integrate the images acquired by an UAV characterized by a GSD of 003m with an Unmanned Surface Vehicle (USV) that collects images of the river from different point of view enhanced by an RGB-D sensor The change detection algorithm applied to the collected data is based on a innovative hybrid classifier that fuses the benefit of pixel and object based approaches mapping the changes over a well defined temporal horizon

Signal Based Fault Detection and Diagnosis for Rotating Electrical Machines: Issues and Solutions

Abstract: Complex systems are found in almost all field of contemporary science and are associated with a wide variety of financial, physical, biological, information and social systems. Complex systems modelling could be addressed by signal based procedures, which are able to learn the complex system dynamics from data provided by sensors, which are installed on the system in order to monitor its physical variables. In this chapter the aim of diagnosis is to detect if the electrical machine is healthy or a change is occurring due to abnormal events and, in addition, the probable causes of the abnormal events. Diagnosis is addressed by developing machine learning procedures in order to classify the probable causes of deviations from system normal events. This chapter presents two Fault Detection and Diagnosis solutions for rotating electrical machines by signal based approaches. The first one uses a current signature analysis technique based on Kernel Density Estimation and Kullback–Liebler divergence. The second one presents a vibration signature analysis technique based on Multi-Scale Principal Component Analysis. Several simulations and experimentations on real electric motors are carried out in order to verify the effectiveness of the proposed solutions. The results show that the proposed signal based diagnosis procedures are able to detect and diagnose different electric motor faults and defects, improving the reliability of electrical machines. Fault Detection and Diagnosis algorithms could be used not only with the fault diagnosis purpose but also in a Quality Control scenario. In fact, they can be integrated in test benches at the end or in the middle of the production line in order to test the machines quality. When the electric motors reach the test benches, the sensors acquire measurements and the Fault Detection and Diagnosis procedures detect if the motor is healthy or faulty, in this last case further inspections can diagnose the fault.

Residential energy monitoring and management based on fuzzy logic

Abstract: In the new European scenario the design of a photovoltaic plant ensuring savings on electricity bills is strongly related to energy management policies. This paper introduces a low-cost device to monitor domestic electricity consumptions, photovoltaic production and define load shifting policies based on fuzzy logic forecasts.

Robust Control of Robot Arms via Quasi Sliding Modes and Neural Networks

Abstract: This chapter presents a control approach for robotic manipulators based on a discrete-time sliding mode control which has received much less coverage in the literature with respect to continuous time sliding-mode strategies. This is due to its major drawback, consisting in the presence of a sector, of width depending on the available bound on system uncertainties, where robustness is lost because the sliding mode condition cannot be exactly imposed. For this reason, only ultimate boundedness of trajectories can be guaranteed, and the larger the uncertainties affecting the system are, the wider is the bound on trajectories which can be guaranteed. As a possible solution to this problem, in this chapter a discontinuous control law has been proposed, employing a controller inside the sector based on an estimation, as accurate as possible, of the overall effect of uncertainties affecting the system. Different solutions for obtaining this estimate have been considered and the achievable performances have be compared using experimental data. The first approach consists in estimating the uncertain terms by a well established method which is an adaptive on-line procedure for autoregressive modeling of non-stationary multivariable time series by means of a Kalman filtering. In the second solution, radial basis neural networks are used to perform the estimation of the uncertainties affecting the system. The proposed control system is evaluated on the ERICC robot arm. Experimental evidence shows satisfactory trajectory tracking performances and noticeable robustness in the presence of model inaccuracies and payload perturbations.

AAL Technologies for Independent Life of Elderly People

Abstract: Assistive technologies have the objective to improve the people quality of life of in daily living, with a special aim to those who suffer of physical disabilities or cognitive impairment, which may be caused by an accident, disease or the natural process of ageing. The present paper describes the main results of a study realized for the INTERREG IVC INNOVAGE project, where the domain target addressed are: home and building automation and assistive robotics. The project provides a quick overview of the typical needs of elderly people, describes the state-of-the-art technologies which can be adopted to satisfy these needs and presents a critical analysis of the functionalities, which present and future assistive technologies should possess. The result of this study is a detailed assessments of requirements and limits of nowadays domotics and robotics technologies aimed to improve people quality of life.

A tool for home-based rehabilitation allowing for clinical evaluation in a visual markerless scenario

Abstract: This work deals with the design of an interactive monitoring tool for home-based physical rehabilitation. The software platform includes a video processing stage and the exercise performance evaluation. Image features are extracted by a Kinect v2 sensor and elaborated to return the exercises score. Furthermore the tool provides to physiotherapists a quantitative exercise evaluation of subject's performances. The proposed tool for home rehabilitation has been tested on 5 subjects and 5 different exercises and results are presented. In particular both exercises and relative evaluation indexes were selected by specialists in neurorehabilitation.

Nonlinear Decentralized Model Predictive Control for Unmanned Vehicles Moving in Formation

Abstract: Unmanned vehicles operating in formation may perform more complex tasks than vehicles working indi- vidually. In order to control a formation of unmanned vehicles, however, the following main issues must be faced: vehicle motion is usually described by nonlinear models, feasible control actions for each vehicle are constrained, collision between the members of the formation must be avoided while, at the same time, the computational efforts must be kept low due to limitations on the onboard hardware. To solve these problems, a nonlinear decentralized model predictive control algorithm is presented in this paper. The adopted model is based on the nonlinear kinematic equations describing the motion of a body with six degrees of freedom, where each vehicle shares information with its leader only by means of a wireless local area network. Saturation and collision-free constraints are included within the formulation of the optimization problem, while de- centralization allows to distribute the computational efforts amongst all the vehicles of the formation. In order to show the effectiveness of the proposed approach, it has been applied to a formation of quadrotor vehicles. Simulation results prove that the approach presented in this paper is a valid way to solve the problem of controlling a formation of unmanned vehicles, granting at the same time the possibility to deal with constraints and nonlinearity while limiting the computational efforts through decentralization. DOI: http://dx.doi.org/10.5755/j01.itc.44.1.7219

An integrated simulation environment for Wireless Sensor Networks

Abstract: Simulators for Wireless Sensor Networks (WSNs) are one of the most important tools for systems development. They enable to study and evaluate new theories and hypotheses for sensors data gathering, testing new applications and protocols. Nowadays, there are a large number of open source WSN simulators and they can be divided into different categories according to their features and main applications. Due to the ability to increase the real WSN prototyping, the Cross Levels Simulator, like Cooja, has become an important class of simulators. Although they are open source, flexible and extensible in all levels, the test interface, the external connection at a physical level and the direct interaction with the process control via the WSN is very poor. In this work we present the Cooja Advanced Sky Interface which is an extension of the Contiki's Cooja network simulator for the Sky mote. Due to the absence of the analog output control in the Contiki OS for the Sky mote, as additional contribution, the Contiki Sky DAC driver has been developed and tested in the Cooja Simulator with the Advanced Sky GUI and GISOO plugin to give the ability to implement control over the wireless sensor network.

Low Cost RGB-D Vision Based System to Support Motor Disabilities Rehabilitation at Home

Abstract: Physical rehabilitation is an important medical activity sector for the recovery of physical functions and clinical treatment of people affected by different pathologies, as neurodegenerative diseases (i.e. multiple sclerosis, Parkinson and Alzheimer diseases, amyotrophic lateral sclerosis), neuromuscular disorders (i.e. dystrophies, myopathies, amyotrophies and neuropathies), neurovascular disorders/trauma (i.e. stroke and traumatic brain injuries), and mobility for the elderly. During the rehabilitation, the patient has to perform different exercises specific for the own disease: while some exercises have to be performed with specific equipment and under the supervision of professional staff, others can be performed by patients without the supervision of physiotherapists. In this last case, it is possible to reduce the costs of health and care national system and to accomplish the treatment at home. In this work, a computer vision system for physical rehabilitation at home is proposed. The vision system exploits a low cost RGB-D camera and open source libraries for the image processing, in order to monitor the exercises performed by the patients, and returns a video feedback to improve the treatment effectiveness and to increase the user’s motivation, interest, and perseverance. Moreover, the vision system evaluates an exercise score in order to monitor the rehabilitation progress, an helpful information both for the clinician staff and patients, and allow physiotherapists to monitor the patients at home and correct their posture if the exercises are not well performed. This approach has been implemented and experimentally tested using the Microsoft Kinect camera, demonstrating good and reliable performances.

An integrated simulation module for wireless cyber-physical system

Abstract: Wireless Sensors Network (WSN) integration in a Cyber Physical System (CPS) is becoming one of the most important research topics for increasing the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability in the wireless automation systems Due the complexity of the CPS, simulators and emulators have to be used to replace the real experiments in order to provide necessary feedback and facilities for this regard Although the simulators are open source, flexible, extensible and full integrated in a mathematical modelling tools, the external connection at a physical level and the direct interaction with the process control via the WSN in the CPSs is very poor This paper proposes a new simulation module to control a wireless cyber-physical system, by integrating LabVIEW development environment for a visual programming language from National Instruments, and COOJA, a cross level wireless sensor network simulator The developed software module, called “GILOO” (Graphical Integration of Labview and cOOja) enables to develop and to debug control policies in a simulated or realistic scenario, using the virtual environment or the hardware module, such as the National Instruments Data Acquisition (SCADA), the FPGA platform, the CompactRio, etc The designed GILOO module has been experimentally tested and preliminary results are shown in this paper In detail, a smart home mock-up is proposed to verify its correct behavior and to realize the networked control of an indoor LED lighting system

Evacuation Aid for Elderly in Care Homes and Hospitals: An Interactive System for Reducing Pre-movement Time in Case of Fire

Abstract: Population aging phenomena are increasing the attention to safety aspects for Elderly in care homes and hospitals: individuals that can autonomously evacuate should be helped during the evacuation by providing specific devices to them. Our activities are aimed by the design of “guidance” system for these categories, and to inquiry their impact on people motion. One of the most important evacuation steps is represented by the pre-movement phase: after the fire alarm sound, individuals continue to spend time in activities not directly connected to the evacuation. This phase could be very long. This work proposes an interactive system for the pre-movement time based on an experimental analysis of pre-movement time behaviors. The system is composed of an individual wearable device: the Zig-Bee-based localization module identifies people’s positions after the alarm and understands whether they are motionless; the interactive module gives a personal stimulus to the latecomers. Technical requirements are evaluated. The effectiveness of the system is investigated through simulations: up to 30 % reduction in total evacuation time can be obtained.

Training and Retraining Motor Functions at Home with the Help of Current Technology for Video Games: Basis for the Project

Abstract: Chronic diseases are an international concern, for their increasing incidence and the strain on individuals and on healthcare systems. In order to enable the healthcare system to cope with increasing demands and to avoid strong decrements in subject’s functionality and well-being, a variety of changes for the management of chronic disease care have been advocated by the World Health Organization. The objective of the research is to verify the feasibility and acceptability of a technological solution that allows to transfer a tailored rehabilitation program for patients with disabilities in the home environment. The first step of the study is to identify the correct technological solution on the basis of the International Standard of Organization (ISO) definition of usability and acceptability. The second step is to verify the capacity of the system to perform correctly what is requested. The third step will be to verify the system efficacy in the patients’ training.

On the design of observers robust to load variations for synchronous converters

Abstract: Due to the ever growing concern on energy efficiency issues, synchronous rectification gained increasing attention in recent years. In the semiconductor industry fast controllers developed for DC-DC converters are typically based on inductor current measures. In this context system costs and performances strictly depend on the current sensor and its proper design. In this paper we present a sensorless synchronous approach based on a current observer. The same observer formulation is valid for the three standard DC-DC converters, simplifying its design for different configurations. Furthermore the designed observers are proven to be robust against load variations, the most uncertain parameter of the control law. Our sensorless approach has been tested in a cascade PI structure for buck, boost and buck-boost converters. Numerical results show its good performances in these different scenarios.

Embedded low level control of DC motors for actuating robotic legs

Abstract: In this paper an embedded system for accurate control of torque, speed and position of DC motors which actuate the joints of a robotic leg is presented. The proposed embedded system is not based on dedicated and expensive motor controllers but on a general pourpose embedded board equipped with a 32bit microcontroller. For each motor the embedded system can selectively operate in three different control modes: torque, speed and position, to control concurrently at least three DC motors with different control tasks. Each motor is simply equipped with a rotary encoder and a low-cost current sensor which provide measurements of velocity and current as feedback signals. The experimental results show that the embedded controller is able to regulate the motor variables as expected by simulation results. Although the embedded controller was designed for the proposed robotic application, it is applicable to other systems where the concurrent control of several DC-motors is required.

Household Electrical Consumptions Modeling and Management Through Neural Networks and Fuzzy Logic Approaches

Abstract: In recent years the European Union and, moreover, Italy has seen a rapid growth in the photovoltaic (PV) sector, following the introduction of the feed in tariff schemes. In this scenario, the design of a new PV plant ensuring savings on electricity bills is strongly related to household electricity consumption patterns. This chapter presents a high-resolution model of domestic electricity use, based on Fuzzy Logic Inference System. The model is built with a “bottom-up” approach and the basic block is the single appliance. Using as inputs patterns of active occupancy and typical domestic habits, the fuzzy model give as output the likelihood to start each appliance within the next minute. In order to validate the model, electricity demand was recorded over the period of one year within 12 dwellings in the central east coast of Italy. A thorough quantitative comparison is made between the synthetic and measured data sets, showing them to have similar statistical characteristics. The focus of the second part of this work is to develop a neural networks based energy management algorithm coupled with the fuzzy model to correctly size a residential photovoltaic plant evaluating the economic benefits of energy management actions in a case study. A cost benefits analysis is presented to quantify its effectiveness in the new Italian scenario and the evaluation of energy management actions.