Sabrina Iarlori

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.

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.

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.

Kernel canonical variate analysis based management system for monitoring and diagnosing smart homes

In the contest of household energy management, a growing interest is addressed to smart system development, able to monitor and manage resources in order to minimize wasting. One of the key factors in curbing energy consumption in the household sector is the amendment of occupant erroneous behaviours and systems malfunctioning, due to the lack of awareness of the final user. Indeed the benefits achievable with energy efficiency could be either amplified or neutralized by, respectively, good or bad practices carried out by the final users. Authors propose a diagnostic system for home energy management application able to detect faults and occupant behaviours. In particular a nonlinear monitoring method, based on Kernel Canonical Variate Analysis, is developed. To remove the assumption of normality, Upper Control Limits are derived from the estimated Probability Density Function through Kernel Density Estimation. The proposed method is applied to smart home temperature sensors to detect anomalies respect to efficient user behaviours and sensors and actuators faults. The method is tested on experimental data acquired in a real apartment.

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

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.

AAL Technologies for Independent Life of Elderly People

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.

RGB-D Video Monitoring System to Assess the Dementia Disease State Based on Recurrent Neural Networks with Parametric Bias Action Recognition and DAFS Index Evaluation

Within 2050, demographic changes, due to the significant increase of elderly, will represent one of the most important aspect for social assistance and healthcare institutions, particularly in European Union. Great attention is given to dementia diseases with over 35 million people worldwide who live in this condition, affected by cognitive impairment, frailty and social exclusion with considerable negative consequences for their independence. Preference will be given to intervention with high impact on the quality of life of the individual associated with a socio-economic burden, also for people who care for them. The main challenge comes from the social objective of assisting and keeping elderly people in their familiar home surrounding or to enable them to “aging in place”.

Accuracy evaluation of the Kinect v2 sensor during dynamic movements in a rehabilitation scenario

In this paper, the accuracy evaluation of the Kinect v2 sensor is investigated in a rehabilitation scenario. The accuracy analysis is provided in terms of joint positions and angles during dynamic postures used in low-back pain rehabilitation. Although other studies have focused on the validation of the accuracy in terms of joint angles and positions, they present results only considering static postures whereas the rehabilitation exercise monitoring involves to consider dynamic movements with a wide range of motion and issues related to the joints tracking. In this work, joint positions and angles represent clinical features, chosen by medical staff, used to evaluate the subjects movements. The spatial and temporal accuracy is investigated with respect to the gold standard, represented by a stereophotogrammetric system, characterized by 6 infrared cameras. The results provide salient information for evaluating the reliability of Kinect v2 sensor for dynamic postures.

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

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 subjects 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.

A novel computer vision based e-rehabilitation system: From gaming to therapy support

We propose a novel e-rehabilitation system based on a commercial RGB-D device. Differently from exergaming approaches, clinical objectives scores of each specific body part involved in the exercise are computed. Subjects performances are sent to the physiotherapists in order to support and improve decisions and therapies.