scholarly journals Fault detection of piston conditions in water hydraulic motor by vibration signal with wavelet analysis

2006 ◽  
pp. 115-134
Author(s):  
H.X. Chen ◽  
P.S.K. Chua ◽  
G.H. Lim
Keyword(s):  
Fault Detection ◽  
Wavelet Analysis ◽  
Vibration Signal ◽  
Hydraulic Motor ◽  
Water Hydraulic

Fault Detection in a Water Hydraulic Motor Using a Wavelet Transform

2006 ◽  
Vol 34 (4) ◽  
pp. 12440 ◽  
Author(s):  
D. M. R. Mitchell ◽  
HX Chen ◽  
GH Lim ◽  
PSK Chua
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Hydraulic Motor ◽  
Water Hydraulic

Development of a machine learning algorithm for fault detection in a cantilever beam

2021 ◽  
pp. 095745652110004
Author(s):  
Amit Kumar Gorai ◽  
Tarapada Roy ◽  
Sumeet Mishra
Keyword(s):  
Mechanical Properties ◽  
Fault Detection ◽  
Cantilever Beam ◽  
Learning Algorithm ◽  
Model Development ◽  
Vibration Signal ◽  
Beam Specimen ◽  
Discrete Wavelet ◽  
Feed Forward Network ◽  
Model Training

The mechanical properties of a component change with any type of damage such as crack development, generation of holes, bend, excessive wear, and tear. The change in mechanical properties causes the material to behave differently in terms of noise and vibration under different loading conditions. Thus, the present study aims to develop an artificial neural network model using vibration signal data for early fault detection in a cantilever beam. The discrete wavelet transform coefficients of de-noised vibration signals were used for model development. The vibration signal was recorded using the OROS OR35 module for different fault conditions (no fault, notch fault, and hole fault) of a cantilever beam. A feed-forward network was trained using backpropagation to map the input features to output. A total of 603 training datasets (201 datasets for three types of cantilever beam—no fault, notch fault, and hole fault) were used for training, and 201 datasets were used for testing of the model. The testing dataset was recorded for a hole fault cantilever beam specimen. The results indicated that the proposed model predicted the test samples with 78.6% accuracy. To increase the accuracy of prediction, more data need to be used in the model training.

Research of the Vertical Vibrations of Tandem Mill Based on Wavelet Analysis

2015 ◽  
Vol 724 ◽  
pp. 279-282
Author(s):  
Chun Hua Ren ◽  
Xu Ma ◽  
Ze Ming Li ◽  
Yan Hong Ding
Keyword(s):  
Wavelet Analysis ◽  
Wavelet Decomposition ◽  
Vibration Signal ◽  
Vertical Vibration ◽  
Rolling Speed ◽  
Vibration Data ◽  
Signal Features ◽  
Operating Side ◽  
Vertical Vibrations ◽  
Vibration Parameters

In this paper, the defect sheet was captured coincidentally. According to the defective product’s characteristics, we suspected to be caused by the vertical vibration of the roll. When the rolling speed reached a certain value, the vibration of the fourth stand can be feel. The experiment of the vibration data collection was taken to compare the vibration parameters of rolling operating side with those of drive side by wavelet analysis. The result states that the abnormal vibration signal features can be extracted in a special frequency segment of wavelet decomposition, and the vibration frequency to the roll is confirmed which appeared product defects.

scholarly journals Engine Fault Detection Approach Based on Angle Domain Signal Model

2021 ◽  
Vol 2068 (1) ◽  
pp. 012034
Author(s):  
Hai Zeng ◽  
Ning Zeng ◽  
Jin Han ◽  
Yan Ding
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Failure Detection ◽  
Vibration Signal ◽  
Bench Test ◽  
Signal Model ◽  
Vibration Signals ◽  
Engine Vibration ◽  
Detection Approach ◽  
Diagnosis Approach

Abstract Engine vibration signals include strong noise and non-stationary signals. By the time domain signal processing approach, it is hard to extract the failure features of engine vibration signals, so it is hard to identify engine failures. For improving the success rate of engine failure detection, an engine angle domain vibration signal model is established and an engine fault detection approach based on the signal model is proposed. The angle domain signal model reveals the modulation feature of the engine angular signal. The engine fault diagnosis approach based on the angle domain signal model involves equal angle sampling and envelope analysis of engine vibration signals. The engine bench test verifies the effectiveness of the engine fault diagnosis approach based on the angle domain signal model. In addition, this approach indicates a new path of engine fault diagnosis and detection.

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