Riconoscimento automatico di difetti per la diagnostica predittiva su sistemi di isolamento

Una macchina elettrica, durante il ciclo di vita, presenta fenomeni di invecchiamento nel sistema di isolamento. Questi si caratterizzano come impulsi di corrente, conosciuti come "scariche parziali", e sono sia il sintomo sia la causa del deterioramento dell'isolamento stesso. Per questo motivo vengono programmate azioni di manutenzione periodiche per limitare i danni che possono provocare. Risulta per\uf2 utile monitorare continuamente lo stato di una macchina, sia per non fermarla per una manutenzione non necessaria, sia per valutare online la condizione dell'isolamento, in modo tale da intervenire immediatamente nel caso in cui un difetto grave si manifesti improvvisamente. Le scariche parziali possono essere pi\uf9 o meno pericolose in funzione di alcuni fattori quali l'intensit\ue0, la frequenza con cui si manifestano e la posizione all'interno di un motore elettrico. Risulta dunque necessario distinguere la/e sorgente/i che generano tali fenomeni. Perci\uf2, in questa tesi, vengono presentati diversi approcci e tecniche per il riconoscimento automatico di difetti, sia con algoritmi di apprendimento supervisionato che non. Nel primo caso si identificano soluzioni di apprendimento rapido che possono essere realizzate su dispositivi hardware, con un ottimo compromesso tra capacit\ue0 di generalizzazione e occupazione di area. Nel secondo, vengono confrontati diversi algoritmi presenti in letteratura e proposta una scelta alternativa dei parametri in ingresso ad essi, che porta a risultati soddisfacenti

An approximate randomization-based neural network with dedicated digital architecture for energy-constrained devices

Variable energy constraints affect the implementations of neural networks on battery-operated embedded systems. This paper describes a learning algorithm for randomization-based neural networks with hard-limit activation functions. The approach adopts a novel cost function that balances accuracy and network complexity during training. From an energyspecific perspective, the new learning strategy allows to adjust, dynamically and in real time, the number of operations during the network’s forward phase. The proposed learning scheme leads to efficient predictors supported by digital architectures. The resulting digital architecture can switch to approximate computing at run time, in compliance with the available energy budget. Experiments on 10 real-world prediction testbeds confirmed the effectiveness of the learning scheme. Additional tests on limited-resource devices supported the implementation efficiency of the overall design approac

Hardware-Aware Affordance Detection for Application in Portable Embedded Systems

Affordance detection in computer vision allows segmenting an object into parts according to functions that those parts afford. Most solutions for affordance detection are developed in robotics using deep learning architectures that require substantial computing power. Therefore, these approaches are not convenient for application in embedded systems with limited resources. For instance, computer vision is used in smart prosthetic limbs, and in this context, affordance detection could be employed to determine the graspable segments of an object, which is a critical information for selecting a grasping strategy. This work proposes an affordance detection strategy based on hardware-aware deep learning solutions. Experimental results confirmed that the proposed solution achieves comparable accuracy with respect to the state-of-the-art approaches. In addition, the model was implemented on real-time embedded devices obtaining a high FPS rate, with limited power consumption. Finally, the experimental assessment in realistic conditions demonstrated that the developed method is robust and reliable. As a major outcome, the paper proposes and characterizes the first complete embedded solution for affordance detection in embedded devices. Such a solution could be used to substantially improve computer vision based prosthesis control but it is also highly relevant for other applications (e.g., resource-constrained robotic systems)

A Convolutional Neural Network-Based Method for Discriminating Shadowed Targets in Frequency-Modulated Continuous-Wave Radar Systems

The radar shadow effect prevents reliable target discrimination when a target lies in the shadow region of another target. In this paper, we address this issue in the case of Frequency-Modulated Continuous-Wave (FMCW) radars, which are low-cost and small-sized devices with an increasing number of applications. We propose a novel method based on Convolutional Neural Networks that take as input the spectrograms obtained after a Short-Time Fourier Transform (STFT) analysis of the radar-received signal. The method discerns whether a target is or is not in the shadow region of another target. The proposed method achieves test accuracy of 92% with a standard deviation of 2.86%

A digital implementation of extreme learning machines for resource-constrained devices

The availability of compact digital circuitry for the support of neural networks is a key requirement for resource-constrained embedded systems. This brief tackles the implementation of single hidden-layer feedforward neural networks, based on hard-limit activation functions, on reconfigurable devices. The resulting design strategy relies on a novel learning procedure that inherits the approach adopted in the Extreme Learning Machine paradigm. The eventual training process balances accuracy and network complexity effectively, thus supporting a digital architecture that prioritizes area utilization over computational performance. Experimental tests confirm that the design approach leads to efficient digital implementations of the predictor on low-performance devices

Learning with Similarity Functions: A Novel Design for the Extreme Learning Machine

This research analyzes the affinities between two well-known learning schemes that apply randomization in the training process, namely, Extreme Learning Machines (ELMs) and the learning framework using similarity functions. These paradigms share a common approach to inductive learning, which combines an explicit data remapping with a linear separator; however, they seem to exploit different strategies in the design of the mapping layer. This paper shows that the theory of learning with similarity functions can stimulate a novel reinterpretation of ELM, thus leading to a common framework. This in turn allows one to improve the strategy applied by ELM for the setup of the neurons\u2019 parameters. Experimental results confirm that the new approach may improve over the standard strategy in terms of the trade-off between classification accuracy and dimensionality of the remapped space

Comparison between PD Acquisition System Measurements Using Different Number of Bits for the Quantization

The PRPDA (Phase Resolved Partial Discharges Acquisition) systems usually employ 8 bits mathrm A/mathrm D converters. Avoiding the converter saturation, it has been necessary, during the measurements, to set the reference voltage on the expected maximum PD signals magnitude. There is an amplitude threshold below which PD signals are not sufficiently quantized to let the separation among PD signals relevant to different defects and noise using automatic recognition algorithms. Therefore, it is necessary to employ PD acquisition systems that exploit more bits for the quantization in order to better distinguish the PD signals from the environmental noise. In this study, PDs and noise measurements have been performed considering for the quantization 8 bit and 12 bit acquisition systems. The efficiency of the recognizing method on the two datasets has been compared and evaluated. The considered algorithm is based upon the Equivalent Time - Equivalent Bandwidth (TW) projection of the data in order to allow a simple clustering in a 2D space

Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links

In the climate change scenario the world is facing, extreme weather events can lead to increasingly serious disasters. To improve managing the consequent risks, there is a pressing need to have real-time systems that provide accurate monitoring and possibly forecasting which could help to warn people in the affected areas ahead of time and save them from hazards. The oblique earth-space links (OELs) have been used recently as a method for real-time rainfall detection. This technique poses two main issues related to its indirect nature. The first one is the classification of rainy and non-rainy periods. The second one is the determination of the attenuation baseline, which is an essential reference for estimating rainfall intensity along the link. This work focuses mainly on the first issue. Data referring to eighteen rain events were used and have been collected by analyzing a satellite-to-earth link quality and employing a tipping bucket rain gauge (TBRG) properly positioned, used as reference. It reports a comparison among the results obtained by applying four different machine learning (ML) classifiers, namely the support vector machine (SVM), neural network (NN), random forest (RF), and decision tree (DT). Various data arrangements were explored, using a preprocessed version of the TBRG data, and extracting two different sets of characteristics from the microwave link data, containing 6 or 12 different features, respectively. The achieved results demonstrate that the NN classifier has outperformed the other classifiers

Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenance program. Partial discharges (PDs) phenomena affect the insulation system of an electrical machine and—in the long term—can lead to a breakdown, with a consequent, significant economic loss; wind turbines provide an excellent example. Embedded solutions are therefore required to monitor the insulation status. The paper presents an online system that adopts unsupervised methodologies for assessing the condition of the monitored machine in real time. The monitoring process does not rely on any prior knowledge about the apparatus; nonetheless, the method can identify the relevant drifts in the machine status. In addition, the system is specifically designed to run on low-cost embedded devices