A Study on the Development of Robust Fault Diagnostic System Based on Neuro-Fuzzy Scheme

1998 ◽  
Vol 31 (10) ◽  
pp. 173-178 ◽  
Author(s):  
Sung-Ho Kim ◽  
Kee-Sang Lee
Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 617
Author(s):  
Umer Saeed ◽  
Young-Doo Lee ◽  
Sana Ullah Jan ◽  
Insoo Koo

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.


2014 ◽  
Vol 556-562 ◽  
pp. 3060-3064
Author(s):  
Jie Hu ◽  
Hao Li ◽  
Xian Jun Hou ◽  
Xiong Zhen Qin

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.


Author(s):  
Mashhour Bani Amer ◽  
Mohammad Amawi ◽  
Hasan El-Khatib

In this paper, a neural fuzzy system for the diagnosis of potassium disturbances is presented. This paper develops an adaptive neuro-fuzzy expert system that can provide accurate diagnosis of potassium disturbances. The proposed diagnostic approach has many attractive features. First, it provides an efficient tool for diagnosis of K+ disturbances and aids clinicians, especially the non-expert ones, in providing fast and accurate diagnosis of K+ disturbances in critical time. Second, it significantly reduces the time needed to accomplish precise diagnosis of K+ disturbances and thus enhances the healthcare standards. Third, it is capable of diagnosing the different types of potassium disturbances using a hybrid neural fuzzy approach. Finally, it has good accuracy (higher than 87%), specificity (100%), and average sensitivity (83%). The performance of the proposed diagnostic system was experimentally evaluated and the achieved results confirmed that the proposed system is efficient and accurate in diagnosing K+ disturbances.


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