Parallel processing computer implementation of a real time DC motor drive fault detection algorithm

1990 ◽  
Vol 137 (5) ◽  
pp. 309 ◽  
Author(s):  
G.S. Stavrakakis ◽  
C. Lefas ◽  
A. Pouliezos
Keyword(s):  
Parallel Processing ◽  
Fault Detection ◽  
Real Time ◽  
Dc Motor ◽  
Detection Algorithm ◽  
Motor Drive ◽  
Computer Implementation

Real-time fault detection algorithm for the Space Shuttle Main Engine

1990 ◽  
Author(s):  
H. PANOSSIAN ◽  
V. KEMP ◽  
R. NELSON ◽  
M. TANIGUCHI
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Space Shuttle ◽  
Detection Algorithm ◽  
Main Engine

A Study on Fault Detection Algorithm for Motor Control Unit of Electric Vehicle

2013 ◽  
Vol 431 ◽  
pp. 226-230
Author(s):  
Dong Hyun Seo ◽  
Wae Gyeong Shin ◽  
Jong Sang No
Keyword(s):  
Motor Control ◽  
Fault Detection ◽  
Real Time ◽  
Electric Motor ◽  
Control Unit ◽  
Detection Algorithm ◽  
Control Units ◽  
Safety And Reliability ◽  
Different Characteristics ◽  
And Control

Algorithms for motor control unit in electric vehicles are being actively developed with consideration given to safety and reliability these days. Faults during driving are a critical problem that is directly linked to the safety of drivers, and studies on fault detection of control units in various situations are needed. This study investigated the faults of control units in a signal level interface with a dynamic model of drive motor and the real-time interconnection of motor control unit and HILS (hardware-in-the-loop simulation). It was found through real-time simulation that simulating the fault conditions with the sensors of motor control unit could reveal different characteristics of motor control unit. Furthermore, vehicle driving simulations with electric motor control were performed. The results of this study are expected to help the development of electric motor simulations and the evaluation of MCU and control algorithms.

A Sensor of Aero-Engine Real-Time Fault Detection System Based on ARM9

2012 ◽  
Vol 591-593 ◽  
pp. 1470-1474
Author(s):  
Yi Gang Sun ◽  
Lei Wang ◽  
Wei Xing Chen
Keyword(s):  
Fault Detection ◽  
Data Processing ◽  
Real Time ◽  
Detection System ◽  
Aircraft Engine ◽  
Detection Algorithm ◽  
Signal Acquisition ◽  
Processing Unit ◽  
Processing Efficiency ◽  
Data Processing Unit

A system is designed to monitor fault of sensors for aircraft engine real-time. SCM C8051F120 is used to control sensor signal acquisition process, and after processing and storage, the data will be transferred to the data processing unit via Ethernet for analysis and detection. ARM9 embedded computer based on WinCE is used as a data processing core for the data processing unit, three layers BP neural network is used as a sensor fault detection algorithm and troubleshooting software with C++ is developed. It can handle large amounts of data and improve processing efficiency. It has a good interface as well. Compared with current systems, it has been greatly improved in real-time and accuracy. After verification, the system is accurate and strong real-time, and can monitor aircraft engine sensor faults correctly.

Adaptive Algorithm Based on Wavelet and SVM for Turbopump Fault Detection

2014 ◽  
Vol 644-650 ◽  
pp. 994-1002
Author(s):  
Tao Hong ◽  
Shuo Yang
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Rocket Engine ◽  
Step Length ◽  
Training Sample ◽  
Detection Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Acceleration Signal ◽  
Liquid Rocket

An adaptive fault detection algorithm based on wavelet and SVM (Support Vector Machine) is proposed for LRE(Liquid Rocket Engine) turbopump real-time fault detection. The algorithm firstly divides the historical signals into some segments by reasonable step length. Then for each segment it gets M-layer detail signals through Daubechies wavelet transform. Thirdly it divides every layer into K average segments and calculates there RMS values, gets M RMS sequences of detail signals. After that it constructs M-dimensional RMS vector as fault feature by extracting RMS values at the same position in every RMS sequence, and extracts all the fault feature vectors of historical signal to construct SVM training sample set and then obtains SVM classifier. At last the classifier will be real-time updated by a reasonable method in the testing process to improve the classification accuracy. To validate the algorithm, a track of the vibration acceleration signal of a certain type of turbopump was chosen as the test object. The test results showed that the algorithm met its demands of accuracy and real-time performance.

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