GLRT Based Fault Detection in Sensor Drift Monitoring System

2009 ◽  
pp. 600-609 ◽  
Author(s):  
In-Yong Seo ◽  
Ho-Cheol Shin ◽  
Moon-Ghu Park ◽  
Seong-Jun Kim
Keyword(s):  
Fault Detection ◽  
Monitoring System ◽  
Sensor Drift

Smart Underground Wireless Cable Fault Detection and Monitoring System

2020 ◽  
Author(s):  
Rao. M Asif ◽  
Syed Rizwan Hassan ◽  
Ateeq Ur Rehman ◽  
Asad Ur Rehman ◽  
Bilal Masood ◽  
...  
Keyword(s):  
Fault Detection ◽  
Monitoring System

scholarly journals A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4688 ◽  
Author(s):  
André Eugênio Lazzaretti ◽  
Clayton Hilgemberg da Costa ◽  
Marcelo Paludetto Rodrigues ◽  
Guilherme Dan Yamada ◽  
Gilberto Lexinoski ◽  
...  
Keyword(s):  
Fault Detection ◽  
Monitoring System ◽  
Environmental Variables ◽  
Energy Use ◽  
Short Circuit ◽  
Environmental Parameters ◽  
Open Circuit ◽  
Ann Model ◽  
Pv System ◽  
Fault Detection And Classification

Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

scholarly journals Fault detection and classification in Industrial IoT in case of missing sensor data

2021 ◽  
Author(s):  
Merim Dzaferagic ◽  
Nicola Marchetti ◽  
Irene Macaluso
Keyword(s):  
Fault Detection ◽  
Monitoring System ◽  
Sensor Data ◽  
Generative Adversarial Networks ◽  
Missing Measurements ◽  
Tennessee Eastman Process ◽  
Fault Detection And Classification ◽  
Industrial Iot ◽  
Critical Components ◽  
The Impact

This paper addresses the issue of reliability in Industrial Internet of Things (IIoT) in case of missing sensors measurements due to network or hardware problems. We propose to support the fault detection and classification modules, which are the two critical components of a monitoring system for IIoT, with a generative model. The latter is responsible of imputing missing sensor measurements so that the monitoring system performance is robust to missing data. In particular, we adopt Generative Adversarial Networks (GANs) to generate missing sensor measurements and we propose to fine-tune the training of the GAN based on the impact that the generated data have on the fault detection and classification modules. We conduct a thorough evaluation of the proposed approach using the extended Tennessee Eastman Process dataset. Results show that the GAN-imputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct functioning of the monitoring system.

Performance analysis of long band passive optical network using amplifier spontaneous noise and fiber Bragg gratings

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Ranbir Singh Mohal ◽  
Rajbir Kaur ◽  
Charanjit Singh
Keyword(s):  
Fault Detection ◽  
Optical Networks ◽  
Monitoring System ◽  
Dispersion Compensation ◽  
Optical Network ◽  
Fiber Bragg Gratings ◽  
Bragg Gratings ◽  
Passive Optical Network ◽  
Spontaneous Noise ◽  
L Band

Abstract Long band (L-Band) passive optical networks (PONs) are attracting a lot of attention these days, thanks to rising capacity demands. Because of PONs requesting more and more channels, fault detection/monitoring is critical. Fault detection in the conventional band (C-Band) employing reflecting Fiber Bragg Gratings (FBGs) and a probe signal integrating an additional amplified spontaneous noise (ASEN) source has been frequently demonstrated. However, interference occurs when ASEN and transmitter signals are in the same wavelength band, and adding additional ASEN sources to the network raises the overall cost. So, in L-Band PONs, a cost-effective, low-complexity fault detection/monitoring system is required. Therefore, in this work, a fault detection/monitoring system for L-Band PON using C-Band ASEN from inline erbium doped fiber amplifier (EDFA) and dual purpose FBG, i.e. (1) ASEN reflection for fault monitoring and (2) dispersion compensation is proposed. A 4 × 10 Gbps L-Band PON is investigated over 40 km feeder fiber (FF) and 1 km drop fibers (DFs) that serve 32 optical network units (ONUs)/different input powers, dispersion values, and laser linewidths in terms of reflective power of FBGs, eye opening factor, and bit error rate (BER), respectively.

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