Development of Incipient Fault Diagnostic System for High Pressure Regulator

2006 ◽  
Author(s):  
Mitsuhiro Toyoda ◽  
Kajiro Watanabe
Keyword(s):  
High Pressure ◽  
Diagnostic System ◽  
Pressure Regulator ◽  
Incipient Fault ◽  
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.

Hybrid Model-Based and Data-Driven Diagnostic Algorithm for Gas Turbine Engines

2020 ◽  
Author(s):  
Amare Fentaye ◽  
Valentina Zaccaria ◽  
Moksadur Rahman ◽  
Mikael Stenfelt ◽  
Konstantinos Kyprianidis
Keyword(s):  
High Pressure ◽  
Measurement Uncertainty ◽  
Gas Turbine ◽  
Diagnostic Algorithm ◽  
Data Driven ◽  
Intermediate Pressure ◽  
Path Component ◽  
Fault Diagnostic ◽  
Component Faults ◽  
Pressure Compressor

Abstract Data-driven algorithms require large and comprehensive training samples in order to provide reliable diagnostic solutions. However, in many gas turbine applications, it is hard to find fault data due to proprietary and liability issues. Operational data samples obtained from end-users through collaboration projects do not represent fault conditions sufficiently and are not labeled either. Conversely, model-based methods have some accuracy deficiencies due to measurement uncertainty and model smearing effects when the number of gas path components to be assessed is large. The present paper integrates physics-based and data-driven approaches aiming to overcome this limitation. In the proposed method, an adaptive gas path analysis (AGPA) is used to correct measurement data against the ambient condition variations and normalize. Fault signatures drawn from the AGPA are used to assess the health status of the case engine through a Bayesian network (BN) based fault diagnostic algorithm. The performance of the proposed technique is evaluated based on five different gas path component faults of a three-shaft turbofan engine, namely intermediate-pressure compressor fouling (IPCF), high-pressure compressor fouling (HPCF), high-pressure turbine erosion (HPTE), intermediate-pressure turbine erosion (IPTE), and low-pressure turbine erosion (LPTE). Robustness of the method under measurement uncertainty has also been tested using noise-contaminated data. Moreover, the fault diagnostic effectiveness of the BN algorithm on different number and type of measurements is also examined based on three different sensor groups. The test results verify the effectiveness of the proposed method to diagnose single gas path component faults correctly even under a significant noise level and different instrumentation suites. This enables to accommodate measurement suite inconsistencies between engines of the same type. The proposed method can further be used to support the gas turbine maintenance decision-making process when coupled with overall Engine Health Management (EHM) systems.

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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