scholarly journals Joint Identification and Channel Estimation for Fault Detection in Industrial IoT with Correlated Sensors

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Lelio Chetot ◽  
Malcolm Egan ◽  
Jean-Marie Gorce
Keyword(s):  
Fault Detection ◽  
Channel Estimation ◽  
Industrial Iot ◽  
Joint Identification

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.

scholarly journals Design and Implementation of Fast Fault Detection in Cloud Infrastructure for Containerized IoT Services

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4592
Author(s):  
Hyunsik Yang ◽  
Younghan Kim
Keyword(s):  
Fault Detection ◽  
Cloud Services ◽  
Performance Measurements ◽  
Cloud Infrastructure ◽  
V2v Communication ◽  
Computing Services ◽  
Vehicle To Vehicle ◽  
Industrial Iot ◽  
Mission Critical ◽  
The Internet Of Things

The container-based cloud is used in various service infrastructures as it is lighter and more portable than a virtual machine (VM)-based infrastructure and is configurable in both bare-metal and VM environments. The Internet-of-Things (IoT) cloud-computing infrastructure is also evolving from a VM-based to a container-based infrastructure. In IoT clouds, the service availability of the cloud infrastructure is more important for mission-critical IoT services, such as real-time health monitoring, vehicle-to-vehicle (V2V) communication, and industrial IoT, than for general computing services. However, in the container environment that runs on a VM, the current fault detection method only considers the container’s infra, thus limiting the level of availability necessary for the performance of mission-critical IoT cloud services. Therefore, in a container environment running on a VM, fault detection and recovery methods that consider both the VM and container levels are necessary. In this study, we analyze the fault-detection architecture in a container environment and designed and implemented a Fast Fault Detection Manager (FFDM) architecture using OpenStack and Kubernetes for realizing fast fault detection. Through performance measurements, we verified that the FFDM can improve the fault detection time by more than three times over the existing method.

Sparse Bayesian learning based channel estimation in FBMC/OQAM industrial IoT networks

2021 ◽  
Author(s):  
Han Wang ◽  
Xingwang Li ◽  
Rutvij H. Jhaveri ◽  
Thippa Reddy Gadekallu ◽  
Mingfu Zhu ◽  
...  
Keyword(s):  
Channel Estimation ◽  
Bayesian Learning ◽  
Sparse Bayesian Learning ◽  
Industrial Iot

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.

scholarly journals Intelligent Incipient Fault Detection in Wind Turbines based on Industrial IoT Environment

2019 ◽  
Vol 1 (1) ◽  
pp. 1-19 ◽  
Author(s):  
P. H. F. Sousa ◽  
N. M. M. Nascimento ◽  
J. S. Almeida ◽  
P. P. Rebouças Filho ◽  
V. H. C. Albuquerque
Keyword(s):  
Fault Detection ◽  
Wind Turbines ◽  
Industrial Iot ◽  
Incipient Fault ◽  
Incipient Fault Detection

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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