scholarly journals Hybrid Electro-Optic Capacitive Sensors for the Fault Diagnostic System of Hydrogenerator

2020 ◽  
Author(s):  
Ievgen O. Zaitsev ◽  
Anatolii Levytskyi
Keyword(s):  
Diagnostic System ◽  
Capacitive Sensors ◽  
Electro Optic ◽  
Fault Diagnostic

scholarly journals CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 617
Author(s):  
Umer Saeed ◽  
Young-Doo Lee ◽  
Sana Ullah Jan ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Diagnostic System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Context Aware ◽  
Sensor Faults ◽  
Training Time ◽  
Low Intensity ◽  
Fault Diagnostic

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

Research and Development of Vehicle Fault Diagnostic System Based on MDI

2014 ◽  
Vol 556-562 ◽  
pp. 3060-3064
Author(s):  
Jie Hu ◽  
Hao Li ◽  
Xian Jun Hou ◽  
Xiong Zhen Qin
Keyword(s):  
Diagnostic System ◽  
Hierarchical Architecture ◽  
Electronic Control ◽  
Electronic Control System ◽  
Fault Diagnostic ◽  
And Function ◽  
Read Rate ◽  
Vehicle Testing ◽  
Design Ideas ◽  
Standardized Diagnostic

For the deficiencies of traditional PC-style vehicle fault diagnostic system, this paper proposes a kind of standardized PC-style vehicle fault diagnostic system which based on the standardized diagnostic interface device, selects MDI which conformed to SAE J2534 standard, studies the communication mechanism of MDI and SAE J2534 standard, optimizes the data read rate, develops the diagnostic system software using the design ideas of hierarchical architecture and function modularization. Through vehicle testing and analysis, this system allows the fault diagnosis, maintenance help, real-time monitoring and online refresh for vehicle electronic control system, has a good versatility and scalability, has a reference for developing standardized diagnostic device and provides the favorable tools for vehicle diagnosis and maintenance.

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