Francesco Ferracuti

Fuzzy logic based economical analysis of photovoltaic energy management

Since 2002 the European Union has seen a rapid growth in the photovoltaic (PV) sector. During the last two years incentives for PV installations were cut almost worldwide slowing the growth of the market. In this scenario the design of a new plant ensuring economic convenience is strongly related to household electricity consumption patterns and energy management actions. This paper presents a high-resolution model of domestic electricity use based on Fuzzy Logic Inference System. Taking into account consumers sensibility concerning the rational use of energy, the model gives as output a 1-min resolution overall electricity usage pattern of the household. The focus of this work is the use of a novel fuzzy model combined with a cost benefits analysis to evaluate the real economic benefits of load shifting actions. A case study is presented to quantify its effectiveness in the new net metering Italian scenario.

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

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.

Multi-scale PCA based fault diagnosis on a paper mill plant

In paper mill plants, the competition for increasing efficiency and reducing costs is a primary purpose. Fault detection and diagnosis can help by minimize the loss of production. In particular for the stock preparation sub-process a signal based fault detection and isolation procedure is developed. Multi-Scale Principal Component Analysis (MSPCA) is used to monitor some critical variables of the stock preparation of a paper mill plant in order to diagnose faults and malfunctions. MSPCA simultaneously extracts both, cross correlation across the sensors (PCA approach) and auto-correlation within a sensor (Wavelet approach). The advantage of MSPCA is validated on considered paper mill plant where several sensors are installed to control and monitor the automation system.

Induction motor fault detection and diagnosis using KDE and Kullback-Leibler divergence

The present paper proposes a novel data-driven Fault Detection and Diagnosis algorithm for induction motors based on Motor Current Signature Analysis. Principal Component Analysis is used to reduce the three-phase currents space in two dimensions. Then, Kernel Density Estimation is adopted to estimate the Probability Density Function of healthy and of each faulty motors, which will give typical patterns that can be used to identify each fault. Kullback-Leibler divergence is used as an index to identify the dissimilarity between two determined probability distributions, that allows the automatic identification of distinct fault types. Several simulations and experimental results are carried out using two benchmarks in order to verify the effectiveness of the proposed methodology: the first is used to prove appropriateness of the method for air gap eccentricity fault diagnosis and the second is used to prove suitability of the method for rotor broken bars and connectors fault diagnosis. Simulations and classification results prove that the proposed Fault Detection and Diagnosis procedure is able to detect and diagnose different induction motor fault types.

Multi-apartment residential microgrid monitoring system based on kernel canonical variate analysis

In the residential energy sector there is a growing interest in smart energy management systems able to monitor, manage and minimize energy consumption. A key factor to curb household energy consumption is the amendment of occupant erroneous behaviors and systems malfunctioning. In this scenario energy efficiency benefits can be either amplified or neutralized by, respectively, good or bad practices carried out by end users. Authors propose a diagnostic system for a residential microgrid application able to detect faults and occupant bad behaviors. In particular a nonlinear monitoring method, based on kernel canonical variate analysis, is developed. To overcome the normality assumption regarding the signals probability distribution, Upper Control Limits are derived from the estimated Probability Density Function through Kernel Density Estimation. The proposed method, applied to a smart residential microgrid, is tested on experimental data acquired from July 2012 to October 2013.

MSPCA with KDE Thresholding to Support QC in Electrical Motors Production Line

Abstract In this paper a Fault Detection and Isolation (FDI) procedure is applied for the defects detection and analysis of electrical motors at the end of production line in hood factories. The objective consists of developing a fast and robust methodology to detect defective motors and to identify defects for quality analysis on production line. Using a signal based FDI procedure, an end of a line bench system is designed, which is able to analyze the defects of produced motors. Multi-Scale Principal Component Analysis (MSPCA) is used for defect detection and a Kernel Density Estimation (KDE) algorithm is used for fault isolation on the PCA residual contributions. Also a method to choose the WT levels is adopted. MSPCA with KDE thresholding advantage is demonstrated by experimentations on test bench, using vibration measurements. Experiments show that the stochastic method used to compute thresholds on PCA residuals is robust and at the same time accurate.

An Integrated Simulation Module for Cyber-Physical Automation Systems †

The integration of Wireless Sensors Networks (WSNs) into Cyber Physical Systems (CPSs) is an important research problem to solve in order to increase the performances, safety, reliability and usability of wireless automation systems. Due to the complexity of real CPSs, emulators and simulators are often used to replace the real control devices and physical connections during the development stage. The most widespread simulators are free, open source, expandable, flexible and fully integrated into mathematical modeling tools; however, the connection at a physical level and the direct interaction with the real process via the WSN are only marginally tackled; moreover, the simulated wireless sensor motes are not able to generate the analogue output typically required for control purposes. A new simulation module for the control of a wireless cyber-physical system is proposed in this paper. The module integrates the COntiki OS JAva Simulator (COOJA), a cross-level wireless sensor network simulator, and the LabVIEW system design software from National Instruments. The proposed software module has been called “GILOO” (Graphical Integration of Labview and cOOja). It allows one to develop and to debug control strategies over the WSN both using virtual or real hardware modules, such as the National Instruments Real-Time Module platform, the CompactRio, the Supervisory Control And Data Acquisition (SCADA), etc. To test the proposed solution, we decided to integrate it with one of the most popular simulators, i.e., the Contiki OS, and wireless motes, i.e., the Sky mote. As a further contribution, the Contiki Sky DAC driver and a new “Advanced Sky GUI” have been proposed and tested in the COOJA Simulator in order to provide the possibility to develop control over the WSN. To test the performances of the proposed GILOO software module, several experimental tests have been made, and interesting preliminary results are reported. The GILOO module has been applied to a smart home mock-up where a networked control has been developed for the LED lighting system.

RGBD camera monitoring system for Alzheimer's disease assessment using Recurrent Neural Networks with Parametric Bias action recognition

Abstract The present paper proposes a computer vision system to diagnose the stage of illness in patients affected by Alzheimers disease. In the context of Ambient Assisted Living (AAL), the system monitors people in home environment during daily personal care activities. The aim is to evaluate the dementia stage, observing actions listed in the Direct Assessment of Funcional Status (DAFS) index and detecting anomalies during the performance, in order to assign a score explaining if the action is correct or not. In this work brushing teeth and grooming hair by a hairbrush are analysed. The technology consists of the application of a Recurrent Neural Network with Parametric Bias (RNNPB) that is able to learn movements connected with a specific action and recognize human activities by parametric bias that work like mirror neurons. This study has been conducted using Microsoft Kinect to collect data about the actions observed and oversee the user tracking and gesture recognition. Experiments prove that the proposed computer vision system can learn and recognize complex human activities and evaluates DAFS score.

Indoor thermal comfort control through fuzzy logic PMV optimization

Control and monitoring of indoor thermal conditions represent crucial tasks for peoples satisfaction in working and living spaces. Among all standards released, predicted mean vote (PMV) is the international index adopted to define users thermal comfort conditions in thermal moderate environments. PMV is a nonlinear function of various quantities, which generally limits its applicability to the heating, ventilation, and air conditioning (HVAC) control problem. Furthermore this index does not consider explicitly outdoor weather conditions. In order to overcome both problems, we introduce a novel fuzzy controller for HVAC systems. The control, considering PMV index value as well as outdoor weather conditions, has been experimentally tested in a working space in the central east coast of Italy. Furthermore temperature regulation performances have been compared with those of a classical PID.

IoT based indoor personal comfort levels monitoring

We present a low-cost IoT based system able to monitor acoustic, olfactory, visual and thermal comfort levels. The system is provided with different ambient sensors, computing, control and connectivity features. The integration of the device with a smartwatch makes it possible the analysis of the personal comfort parameters.