A Method of Fault Detection and Diagnosis for Electromagnetic Direction Valve Based on Artificial Neural Network

2012 ◽  
Vol 476-478 ◽  
pp. 2384-2388
Author(s):  
Min Qiang Dai ◽  
Wei Cai ◽  
Sheng Dun Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Line Detection ◽  
On Line ◽  
Artificial Neural ◽  
Detection And Diagnosis

The magnetic field and vibration signal of electromagnetic direction valve can be detected real-timely by a non-intrusive on line detection device, which can use to monitor working state of the valve. A method of fault detection and diagnosis for electromagnetic direction valve from the signal detected by the non-intrusive on line detection device is presented in this paper. The wave frequency bands energy analysis method is adopted to distinguish the electromagnetic direction valve’s state, and the vibration signal are decomposed by three-layer wavelet packet which wavelet basis is db10. The fault identification method is based on BP artificial neural network (ANN), which is the most well-known three-layers BP ANN whose input and output layers have 8 and 3 neurons respectively.

HMM-Based Fault Detection and Diagnosis Scheme for Rolling Element Bearings

2004 ◽  
Vol 127 (4) ◽  
pp. 299-306 ◽  
Author(s):  
Hasan Ocak ◽  
Kenneth A. Loparo
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Markov Modeling ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Hidden Markov Modeling ◽  
Rolling Element ◽  
Detection And Diagnosis

In this paper, we introduce a new bearing fault detection and diagnosis scheme based on hidden Markov modeling (HMM) of vibration signals. Features extracted from amplitude demodulated vibration signals from both normal and faulty bearings were used to train HMMs to represent various bearing conditions. The features were based on the reflection coefficients of the polynomial transfer function of an autoregressive model of the vibration signals. Faults can be detected online by monitoring the probabilities of the pretrained HMM for the normal case given the features extracted from the vibration signals. The new technique also allows for diagnosis of the type of bearing fault by selecting the HMM with the highest probability. The new scheme was also adapted to diagnose multiple bearing faults. In this adapted scheme, features were based on the selected node energies of a wavelet packet decomposition of the vibration signal. For each fault, a different set of nodes, which correlates with the fault, is chosen. Both schemes were tested with experimental data collected from an accelerometer measuring the vibration from the drive-end ball bearing of an induction motor (Reliance Electric 2 HP IQPreAlert) driven mechanical system and have proven to be very accurate.

scholarly journals On-line Fault Detection of Transmission Line Using Artificial Neural Network.(Dept.E)

2020 ◽  
Vol 30 (4) ◽  
pp. 16-21
Author(s):  
Samah El Safty ◽  
Hamdy Ashour ◽  
Hesien El Dessouki ◽  
Mohamed El Sawaf
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Transmission Line ◽  
On Line ◽  
Artificial Neural
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