scholarly journals Learning to See the Vibration: A Neural Network for Vibration Frequency Prediction

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2530 ◽  
Author(s):  
Jiantao Liu ◽  
Xiaoxiang Yang
Keyword(s):  
Neural Network ◽  
Vibration Frequency ◽  
Frequency Estimation ◽  
Vibration Signal ◽  
Frequency Measurement ◽  
Optical Methods ◽  
Vibration Measurement ◽  
Vibration Signals ◽  
Engineering Practices ◽  
To Receive

Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrate that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.

scholarly journals Learning to See the Vibration: a Neural Network for Vibration Frequency Prediction

2018 ◽  
Author(s):  
Jiantao Liu ◽  
Xiaoxiang Yang
Keyword(s):  
Neural Network ◽  
Vibration Frequency ◽  
Frequency Estimation ◽  
Vibration Signal ◽  
Frequency Measurement ◽  
Optical Methods ◽  
Vibration Measurement ◽  
Vibration Signals ◽  
Engineering Practices ◽  
To Receive

Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrated that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result obtained was compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.

scholarly journals Artificial Neural Network for Vibration Frequency Measurement Using Kinect V2

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Jiantao Liu ◽  
Xiaoxiang Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vibration Frequency ◽  
Optical Measurement ◽  
Frequency Measurement ◽  
Microsoft Kinect ◽  
Depth Information ◽  
Research Attention ◽  
Full Field ◽  
Artificial Neural

Optical measurement can substantially reduce the required amount of labor and simplify the measurement process. Furthermore, the optical measurement method can provide full-field measurement results of the target object without affecting the physical properties of the measurement target, such as stiffness, mass, or damping. The advent of consumer grade depth cameras, such as the Microsoft Kinect, Intel RealSence, and ASUS Xtion, has attracted significant research attention owing to their availability and robustness in sampling depth information. This paper presents an effective method employing the Kinect sensor V2 and an artificial neural network for vibration frequency measurement. Experiments were conducted to verify the performance of the proposed method. The proposed method can provide good frequency prediction within acceptable accuracy compared to an industrial vibrometer, with the advantages of contactless process and easy pipeline implementation.

The Development of Neural Network Cepstrum Method for Bearing Fault Detection

2009 ◽  
Author(s):  
Yean-Ren Hwang ◽  
Kuo-Kuang Jen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vibration Signal ◽  
Motor Systems ◽  
Vibration Signals ◽  
Ann Models ◽  
Bearing Vibration ◽  
The Difference ◽  
Artificial Neural Network Ann ◽  
Coefficient Method

A bearing diagnosis system that combines cepstrum coefficient method for feature extraction from bearing vibration signals and artificial neural network (ANN) models for the classification is proposed in this paper. We first segment the vibration signal and obtain the corresponding cepstrum coefficients, then classify the motor systems through ANN models. Utilizing the proposed method, one can identify the characteristics hiding inside the vibration signal and then diagnose the abnormalities. To evaluate this method, several experiments for the normal and abnormal conditions have been performed in the laboratory and the results are used to verify the method. It is shown that the proposed method had effectively distinguished the difference between the normal and abnormal cases and classified correctly the corresponding feature conditions.

scholarly journals Applying Energy-Equal Entropy of Wavelet Packet to Diagnose Circuit Breaker Faults

2014 ◽  
Vol 8 (1) ◽  
pp. 445-452
Author(s):  
Liu Mingliang ◽  
Wang Keqi ◽  
Sun Laijun ◽  
Zhang Jianfeng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Circuit Breaker ◽  
Back Propagation ◽  
Vibration Signal ◽  
Characteristic Parameter ◽  
Vibration Signals

Aiming to better reflect features of machinery vibration signals of high-voltage (HV) circuit breaker (CB), a new method is proposed on the basis of energy-equal entropy of wavelet packet(WP). First of all, three-layer wavelet packet decomposes vibration signal, reconstructing 8 nodes of signals in the 3rd layer. Then, the vector is extracted with energy-equal entropy of reconstructed signals. At last, the simple back-propagation (BP) neural network for fault diagnosis contributes to classification of the characteristic parameter. This technology is the basis of a number of patents and patents pending, which is experimentally demonstrated by the significant improvement of diagnose faults.

Use of Neural Networks in Road Recognition by Vibration Data

2010 ◽  
Author(s):  
Giacomo Reggiani ◽  
Marco Cocconcelli ◽  
Riccardo Rubini ◽  
Francesco Lolli
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sensitivity Analysis ◽  
Vibration Signal ◽  
Accelerometer Sensor ◽  
Vibration Signals ◽  
Vibration Data ◽  
The Neural Network ◽  
Different Types ◽  
Control Set

This paper deals with road recognition by the use of neural networks on vibration signals. In particular, an accelerometer sensor is used and it acquires the vibrations transmitted in the contact between the tyres and the ground. Twelve different types of road have been tested at different speed of the car. Based on these data a neural network has been proposed to correlate a set of suitable characteristics of the vibration signal with the type of road. The effectiveness of the resulting neural network has been proved on a control set of data. Moreover the paper reports a sensitivity analysis of the neural network in order to minimize the number of inputs needed and to make it rugged.

Study on Method for Real-Time Vibration Signal of Shearer Acquisition

2013 ◽  
Vol 373-375 ◽  
pp. 865-869
Author(s):  
Xiao Dong Ji ◽  
Zi Xian Yang ◽  
Xu Li ◽  
Guang Hui Xue ◽  
Miao Wu
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Underground Mining ◽  
Measurement Data ◽  
Vibration Signal ◽  
Vibration Measurement ◽  
Analysis Method ◽  
Vibration Signals ◽  
Signal Sample ◽  
Signal Analysis Method

In order to acquire and analyze the real-time vibration signals of shearer in the process of coal mining. This experiment used the portable vibration measurement data recorder, which developed by ourselves. We design a comprehensive set of solutions for data acquisition according to underground mining machines working in real time. Through the data collection package, obtained a shearer vibration signal sample on real-time working. Meanwhile, analyze the vibration signal using signal analysis method. Through the experiment we acquire vibration signals of shearer's ranging-arms on real-time working, and the analysis results indicate that data collection solution is reasonably practicable.

Application of Multi-Fractal Spectrum to Analysis the Vibration Signal of Power Transformer for the Detection of Winding Deformation

2011 ◽  
Vol 267 ◽  
pp. 584-589 ◽  
Author(s):  
Feng Hua Wang ◽  
Jun Zhang ◽  
Zhi Jian Jin ◽  
Qing Li
Keyword(s):  
Power Transformer ◽  
Short Circuit ◽  
Vibration Signal ◽  
Vibration Measurement ◽  
Response Curves ◽  
Vibration Signals ◽  
Short Circuit Test ◽  
Fractal Spectrum ◽  
Winding Deformation ◽  
The Relationship

The method of monitoring the tank vibration is becoming an effective method to detect the winding deformation of power transformer for the reliable and secure operation of power system. In order to further identify the relationship between the winding deformation fault of power transformer and the measured tank vibration signals, the short-circuit test is done in a 220kV transformer with the developed vibration measurement system and the vibration frequency response curves for some pre-setting winding deformation faults are obtained. Multi-fractal spectrum is applied to analyze the of the obtained vibration signals when winding conditions of power transformer are changed. It is shown that multi-fractal spectrum can effectively indicate the geometric structure features of vibration signals. The parameter variations of multi-fractal spectrum are agreed well with the preset winding faults, which provides another method for the feature extraction of vibration signal to detect the winding deformation of power transformer.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 262
Author(s):  
Chih-Yung Huang ◽  
Zaky Dzulfikri
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Template Matching ◽  
Health Condition ◽  
Vibration Measurement ◽  
One Dimensional ◽  
Stamping Process ◽  
Metalworking Industry ◽  
Increasing Demand ◽  
Wear Condition

Stamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of the process; therefore, effective monitoring of the tool health condition is essential for detecting stamping defects. In this study, vibration measurement was used to monitor the stamping process and tool health. A system was developed for capturing signals in the stamping process, and each stamping cycle was selected through template matching. A one-dimensional (1D) convolutional neural network (CNN) was developed to classify the tool wear condition. The results revealed that the 1D CNN architecture a yielded a high accuracy (>99%) and fast adaptability among different models.

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