TMS320 DSP based neural networks on fault diagnostic system of turbo-generator

2004 ◽  
Author(s):  
Ruixin Li ◽  
Jun Zhang ◽  
Taiyong Wang ◽  
Pu Han ◽  
Lijing Zhang
Keyword(s):  
Neural Networks ◽  
Diagnostic System ◽  
Turbo Generator ◽  
Fault Diagnostic

Study on Practical Application of Turboprop Engine Condition Monitoring and Fault Diagnostic System Using Fuzzy-Neuro Algorithms

2012 ◽  
Author(s):  
Changduk Kong ◽  
Semyeong Lim ◽  
Keonwoo Kim
Keyword(s):  
Neural Networks ◽  
Diagnostic System ◽  
Engine Performance ◽  
Performance Model ◽  
Performance Data ◽  
Turboprop Engine ◽  
The Neural Networks ◽  
Fault Diagnostic ◽  
Intelligent Methods ◽  
Learning Data

Recently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks, Fuzzy Logic and Genetic Algorithms have been studied to improve the model based engine diagnostic methods. Among them the Neural Networks is mostly used to engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if only use of the Neural Networks. In addition, it has a very complex structure due to finding effectively faults of single type faults and multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measuring performance data, and proposes a fault diagnostic system using the base performance model and artificial intelligent methods such as Fuzzy and Neural Networks. Each real engine performance model, which is named as the base performance model that can simulate a new engine performance, is inversely made using its performance test data. Therefore the condition monitoring of each engine can be more precisely carried out through comparison with measuring performance data. The proposed diagnostic system identifies firstly the faulted components using Fuzzy Logic, and then quantifies faults of the identified components using Neural Networks leaned by fault learning data base obtained from the developed base performance model. In leaning the measuring performance data of the faulted components, the FFBP(Feed Forward Back Propagation) is used. In order to user’s friendly purpose, the proposed diagnostic program is coded by the GUI type using MATLAB. The proposed program is verified by application of several case studies having the arbitrary implanted engine component faults as well as real engine performance data.

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.

Detection of Roller Bearing Conditions Using Mechatronics Approach

1999 ◽  
Author(s):  
T. I. Liu ◽  
F. Ordukhani
Keyword(s):  
Neural Networks ◽  
Roller Bearing ◽  
Diagnostic System ◽  
Feature Selection Technique ◽  
Vibration Signals ◽  
Selection Technique ◽  
On Line ◽  
Line Classification ◽  
Nonlinear Pattern

Abstract An on-line monitoring and diagnostic system is needed to detect faulty bearings. In this work, by applying the feature selection technique to the data obtained from vibration signals, six indices were selected. Artificial neural networks were used for nonlinear pattern recognition. An attempt was made to distinguish between normal and defective bearings. Counterpropagation neural networks with various network sizes were trained for these tasks. The counterpropagation neural networks were able to recognize a normal from a defective bearing with the success rate between 88.3% to 100%. The best results were obtained when all the six indices were used for the on-line classification of roller bearings.

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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