Search results for "Fault Detection"
showing 10 items of 77 documents
Hyperspectral image classification using CNN: Application to industrial food packaging
2021
Abstract During food tray packaging, some contamination may exist due to the presence of undesired objects. It is essential to detect anomalies during the packaging process in order to discard the faulty tray and avoid human consumption. This study demonstrates the on-line classification feasibility when using hyperspectral imaging systems for real-time food packaging control by using Convolutional Neural Networks (CNN) as a classifier in heat-sealed food trays. A hyperspectral camera is used to capture individual food tray information and fed to a CNN classifier to detect faulty food trays with object contamination. The proposed system is able to detect up to eleven different contamination…
A Smart Sensing Method for Real- Time Monitoring of Low Voltage Series-Arc-Fault
2020
This paper proposes a smart sensing method for real-time monitoring of low voltage series arc fault. It is based on the wavelet coefficient mean-difference algorithm and the four spikes appearing within two fundamental periods criterion with adaptive threshold. The method also uses the hard thresholding wavelet denoising with the universal threshold. An arc fault factor and a load adaptation factor are introduced and combined with a correction factor, so allowing the selection of the adaptive threshold in real-time and the series arc fault detection.
Parameter Identification of a Winding Function Based Model for Fault Detection of Induction Machines
2018
Prediction of machines' faulty parts is important in industrial applications in order to reduce productivity losses. As far as electrical machines are considered, a model-based fault diagnosis approach is usually used for this purpose. The model is derived from the modified winding function theory and hence, it requires a considerable amount of parameters at various operating conditions in order to be successfully used. However, the complete set of parameters is difficult to be obtained, as manufacturers of electric machines normally provide only the parameters that describe simple motor models (e.g. T-equivalent circuit at rated conditions). Therefore, the current work presents a method th…
FAULT DETECTION AND DATA PREDICTION FOR WIRELESS SENSOR NETWORKS
2014
In the last few years, Wireless Sensor Networks (WSNs) have been extensively used as a pervasive sensing module of Ambient Intelligence (AmI) systems in several application fields, thanks to their versatility and ability to monitor diverse environmental quantities. Although wireless sensor nodes are able to perform onboard computations and to share the sensed data, they are limited by the scarcity of energy resources which heavily influences the network lifetime; moreover, the design phase of a WSN requires testing the application scalability prior to actual deployment. In this regard, this dissertation focuses on data prediction to address such crucial tasks as prolonging the network lifet…
Modeling and Verification of Symbolic Distributed Applications Through an Intelligent Monitoring Agent
2022
Wireless Sensor Networks (WSNs) represent a key component in emerging distributed computing paradigms such as IoT, Ambient Intelligence, and Smart Cities. In these contexts, the difficulty of testing, verifying, and monitoring applications in their intended scenarios ranges from challenging to impractical. Current simulators can only be used to investigate correctness at source code level and with limited accuracy. This paper proposes a system and a methodology to model and verify symbolic distributed applications running on WSNs. The approach allows to complement the distributed application code at a high level of abstraction in order to test and reprogram it, directly, on deployed network…
A distributed Bayesian approach to fault detection in sensor networks
2012
Sensor networks are widely used in industrial and academic applications as the pervasive sensing module of an intelligent system. Sensor nodes may occasionally produce incorrect measurements due to battery depletion, dust on the sensor, manumissions and other causes. The aim of this paper is to develop a distributed Bayesian fault detection algorithm that classifies measurements coming from the network as corrupted or not. The computational complexity is polynomial so the algorithm scales well with the size of the network. We tested the approach on a synthetic dataset and obtained significant results in terms of correctly labeled measurements.
QoS-Aware Fault Detection in Wireless Sensor Networks
2013
Wireless sensor networks (WSNs) are a fundamental building block of many pervasive applications. Nevertheless the use of such technology raises new challenges regarding the development of reliable and fault-tolerant systems. One of the most critical issues is the detection of corrupted readings amidst the huge amount of gathered sensory data. Indeed, such readings could significantly affect the quality of service (QoS) of the WSN, and thus it is highly desirable to automatically discard them. This issue is usually addressed through “fault detection” algorithms that classify readings by exploiting temporal and spatial correlations. Generally, these algorithms do not take into account QoS re…
Rotor bar pre-fault detection in the squirrel cage induction motors
2015
The paper deals with a diagnosis technique to detect and monitor incipient faults in the rotor bars of squirrel gage induction motors. The failure mode analysis is performed monitoring the motor axial vibrations. To accomplish the task, the authors present a mathematical model that allows relating the occurrence and the severity of the faults to the presence and the magnitude of some frequency components of the axial vibration spectrum. To validate the proposed approach, the results obtained by applying the mathematical model are compared with the ones obtained by experimental tests done on both healthy and faulty motors.
Diagnosis of inverter-fed induction motors in short time windows using physics-assisted deep learning framework
2019
This article presents a framework for accurate fault diagnostics in inverter-fed induction machinery operating under variable speed and load conditions within very short time windows. Condition indicators based on fault characteristic frequencies observed over the extended Park's vector modulus are fused with deep features extracted using stacked autoencoders to generate a multidimensional feature space for fault classification using support vector machine. The proposed approach is demonstrated in a laboratory setup to detect the most commonly occurring faults, namely, the stator turns fault, broken rotor bars fault and bearing fault with an accuracy > 98% within a short time window of 2–3 …
Robust fault detection design for unknown inputs Takagi-Sugeno models with parametric uncertainties and time-varying delays
2014
This paper deals with the design of robust fault detection system for Takagi-Sugeno (T-S) modes with parametric uncertainties and time-varying delay. An Unknown Input Observer (UIO) is designed such that the unknown inputs are thoroughly decoupled from residual signals while they show the maximum possible sensitivity to the faults and the minimum possible sensitivity to the external disturbances. Since the system under consideration is subjected to parametric uncertainties, the H ∞ model matching approach is used to design an optimal observer. Design procedure is given in terms of Linear Matrix Inequalities (LMIs). Finally, a numerical example is presented to show the effectiveness of the p…