scholarly journals Fault Detection and Diagnosis in Multi-Robot Systems: A Survey

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4019 ◽  
Author(s):  
Eliahu Khalastchi ◽  
Meir Kalech
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Research Opportunities ◽  
The World ◽  
Different Types ◽  
Robot Systems ◽  
Detection And Diagnosis ◽  
Types Of Faults ◽  
Multi Robot

The use of robots has increased significantly in the recent years; rapidly expending to numerous applications. These sophisticated machines are susceptible to different types of faults that might endanger the robot or its surroundings. These faults must be detected and diagnosed in time to allow continual operation. The field of Fault Detection and Diagnosis (FDD) has been studied for many years. This research has given birth to many approaches that are applicable to different types of physical machines. However, the domain of robotics poses unique requirements that challenge traditional FDD approaches. The study of FDD for robotics is relatively new; only few surveys were presented. These surveys have focused on the single robot scenario. To the best of our knowledge, there is no survey that focuses on FDD for Multi-Robot Systems (MRS). In this paper we set out to fill this gap. This paper provides detailed insights to the world of FDD for MRS. We first describe how different attributes of MRS pose different challenges for FDD. With respect to these challenges, we survey different FDD approaches applicable for MRS. We conclude with a description of research opportunities in this field. With these contributions it is the authors’ intention to provide detailed insights to the world of FDD for MRS.

scholarly journals Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4612 ◽  
Author(s):  
Pangun Park ◽  
Piergiuseppe Di Marco ◽  
Hyejeon Shin ◽  
Junseong Bang
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Short Term Memory ◽  
Fault Detection And Diagnosis ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Detection And Diagnosis ◽  
Lstm Network ◽  
Types Of Faults

Fault detection and diagnosis is one of the most critical components of preventing accidents and ensuring the system safety of industrial processes. In this paper, we propose an integrated learning approach for jointly achieving fault detection and fault diagnosis of rare events in multivariate time series data. The proposed approach combines an autoencoder to detect a rare fault event and a long short-term memory (LSTM) network to classify different types of faults. The autoencoder is trained with offline normal data, which is then used as the anomaly detection. The predicted faulty data, captured by autoencoder, are put into the LSTM network to identify the types of faults. It basically combines the strong low-dimensional nonlinear representations of the autoencoder for the rare event detection and the strong time series learning ability of LSTM for the fault diagnosis. The proposed approach is compared with a deep convolutional neural network approach for fault detection and identification on the Tennessee Eastman process. Experimental results show that the combined approach accurately detects deviations from normal behaviour and identifies the types of faults within the useful time.

scholarly journals Survey on Fault Detection and Diagnosis Using Neural Network in WBAN

2018 ◽  
Vol 7 (2.20) ◽  
pp. 346
Author(s):  
R N.S.Kalpana ◽  
Dr P.Nallathai
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Health Information ◽  
Sensor Network ◽  
Fault Detection And Diagnosis ◽  
The Body ◽  
Body Area ◽  
Different Types ◽  
Detection And Diagnosis ◽  
Network Approaches

Wireless Body Area Networks (WBAN) is the sensor network used for monitoring health information in e-health systems. WBAN is a combination of sensors used to obtain vital information from the body. It is a special type of WSN. WBAN technology should handle the data in a smart way by reacting to the monitored data and to evaluate the data.  It  requires  fault detection and diagnosis methods for sensors used in WBAN.  This paper gives a survey of different types of neural-network approaches for faults detection and diagnosis in WBAN using neural network.  

scholarly journals Novel Features Extraction for Fault Detection Using Thermography Characteristics and IV Measurements of CIGS Thin-Film Module

2020 ◽  
Vol 19 (5) ◽  
pp. 311-325
Author(s):  
Reham A. Eltuhamy ◽  
Mohamed Rady ◽  
Khaled H. Ibrahim ◽  
Haitham A. Mahmoud
Keyword(s):  
Thin Film ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Pv Module ◽  
Cigs Thin Film ◽  
Copper Indium ◽  
Pv Modules ◽  
Detection And Diagnosis ◽  
Types Of Faults ◽  
Copper Indium Gallium

Regarding the fault diagnosis of Copper Indium Gallium Selenide (CIGS) PV modules, previously published articles focused on employing statistical analysis of thermography images. This approach failed in many cases to distinguish among fault types. This article presents a novel methodology to diagnose and predict faults of thin-film CIGS PV modules using infrared thermography analysis combined with measurements of I-V characteristics. The proposed methodology encompasses a comprehensive site work to capture images that cover many fault types of the PV module under study. The novelty of the technique depends on utilizing processing and analysis of the captured images using new proposed mathematical parameters to extract different faults’ features. Using I-V measurements combined with thermography analysis, the differences between different types of faults are detected. Then, a general classification matrix of CIGS fault detection and diagnosis, using features based on mathematical parameters and IV measurements has been established. Results show that the analysis of the temperature distribution is proved to be insufficient to identify specific modes of different faults. In addition, the proposed procedure for fault detection and classification, which depends on the pattern of faults, can be used for any type of PV module. This results in more reliance on the proposed technique to increase the confidence level of fault detection.

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