1201 Concept of Fault Detection at Running Gear using On-track Monitoring System and Consideration of Feature by Fault Experiment/Simulation

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.

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Fault detection and isolation in motion monitoring system

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/embc.2012.6347174 ◽

2012 ◽

Author(s):

Duk-Jin Kim ◽

Myoung Hoon Suk ◽

B. Prabhakaran

Keyword(s):

Fault Detection ◽

Monitoring System ◽

Fault Detection And Isolation

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Real-time fault detection and condition monitoring system for chiller

2004 Semiconductor Manufacturing Technology Workshop Proceedings (IEEE Cat No 04EX846) SMTW-04 ◽

10.1109/smtw.2004.1393755 ◽

2005 ◽

Cited By ~ 2

Author(s):

Wen-Yao Chang ◽

Tzu-Chi Wang ◽

Yuan-Pin Shin

Keyword(s):

Fault Detection ◽

Real Time ◽

Monitoring System ◽

Condition Monitoring ◽

Condition Monitoring System

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

10.36227/techrxiv.14540061.v1 ◽

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.

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