scholarly journals Fault Detection of Planetary Gears Based on Signal Space Constellations

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 366
Author(s):  
Maite Martincorena-Arraiza ◽  
Carlos A. De La Cruz Blas ◽  
Antonio Lopez-Martin ◽  
Cristián Molina Vicuña ◽  
Ignacio R. Matías
Keyword(s):  
Laboratory Test ◽  
Orthonormal Basis ◽  
Test Bench ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Planetary Gears ◽  
Planetary Gearbox ◽  
Opposite Case ◽  
Schmidt's Method ◽  
Low Pass

A new method to process the vibration signal acquired by an accelerometer placed in a planetary gearbox housing is proposed, which is useful to detect potential faults. The method is based on the phenomenological model and consists of the projection of the healthy vibration signals onto an orthonormal basis. Low pass components representation and Gram–Schmidt’s method are conveniently used to obtain such a basis. Thus, the measured signals can be represented by a set of scalars that provide information on the gear state. If these scalars are within a predefined range, then the gear can be diagnosed as correct; in the opposite case, it will require further evaluation. The method is validated using measured vibration signals obtained from a laboratory test bench.

scholarly journals Fault diagnosis of a planetary gearbox based on a local bi-spectrum and a convolutional neural network

2021 ◽  
Author(s):  
Lingli Jiang ◽  
LI Shuhui ◽  
LI Xuejun ◽  
Jiale Lei ◽  
YANG Dalian
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Planetary Gearbox ◽  
Gaussian Colored Noise ◽  
Diagnosis Method ◽  
Non Gaussian ◽  
Fault Information ◽  
Fault Characteristic

Abstract The vibration signals of a planetary gearbox have the characteristics of strong background noise and instability and are non-Gaussian. Bi-spectrums can suppress Gaussian colored noise and are suitable for vibration signal processing of planetary gearboxes. In the traditional fault diagnosis methods based on bi-spectrums, the fault characteristic frequency amplitudes of bi-spectrum or bi-spectrum slices, or the further quantitative calculations of fault characteristic values, are generally used as the basis of fault diagnosis processes. It has been found that bi-spectrum images can directly characterize the faults of the planetary gearboxes. Convolutional neural networks (CNNs) have been used in mechanical fault diagnoses in recent years. One-dimensional original signals are converted into two-dimensional images as CNN input, which is an effective method for mechanical fault diagnoses. At the present time, there has not been any relevant research conducted using bi-spectral images as CNN input. In this study, a fault diagnosis method based on local bi-spectrum and CNN was proposed. A bi-spectral analysis of the vibration signals of the planetary gearbox was first carried out in order to reveal the fault information while retaining the non-Gaussian information. Then, according to the bi-spectrum symmetry, local images containing the entire domain information were taken as the input of the CNN, which reduced the redundancy of the fault information. Then, in order to improve the diagnostic accuracy of the CNN, the key parameters of CNN architecture were optimized. Finally, a CNN diagnosis model was built to realize the classification diagnoses of different fault positions and different fault degrees of planetary gearboxes. This study’s comparison of the diagnosis results of the full bi-spectrum+CNN, local bi-spectrum+SVM, original vibration signal+CNN, and local bi-spectrum+BP neural networks showed that the method proposed in this study had achieved both accuracy and rapidity in the fault diagnoses of planetary gearboxes.

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 Compressive sensing-based vibration signal reconstruction using sparsity adaptive subspace pursuit

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879087 ◽  
Author(s):  
Lin Zhou ◽  
Qianxiang Yu ◽  
Daozhi Liu ◽  
Ming Li ◽  
Shukai Chi ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Data Storage ◽  
Wireless Sensors ◽  
Signal Reconstruction ◽  
Estimation Method ◽  
Vibration Signal ◽  
Estimation Accuracy ◽  
Offshore Structure ◽  
Vibration Signals ◽  
Reconstructed Signal

Wireless sensors produce large amounts of data in long-term online monitoring following the Shannon–Nyquist theorem, leading to a heavy burden on wireless communications and data storage. To address this problem, compressive sensing which allows wireless sensors to sample at a much lower rate than the Nyquist frequency has been considered. However, the lower rate sacrifices the integrity of the signal. Therefore, reconstruction from low-dimension measurement samples is necessary. Generally, the reconstruction needs the information of signal sparsity in advance, whereas it is usually unknown in practical applications. To address this issue, a sparsity adaptive subspace pursuit compressive sensing algorithm is deployed in this article. In order to balance the computational speed and estimation accuracy, a half-fold sparsity estimation method is proposed. To verify the effectiveness of this algorithm, several simulation tests were performed. First, the feasibility of subspace pursuit algorithm is verified using random sparse signals with five different sparsities. Second, the synthesized vibration signals for four different compression rates are reconstructed. The corresponding reconstruction correlation coefficient and root mean square error are demonstrated. The high correlation and low error result mean that the proposed algorithm can be applied in the vibration signal process. Third, implementation of the proposed approach for a practical vibration signal from an offshore structure is carried out. To reduce the effect of signal noise, the wavelet de-noising technique is used. Considering the randomness of the sampling, many reconstruction tests were carried out. Finally, to validate the reliability of the reconstructed signal, the structure modal parameters are calculated by the Eigensystem realization algorithm, and the result is only slightly different between original and reconstructed signal, which means that the proposed method can successfully save the modal information of vibration signals.

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.

Mine Ventilator Fault Diagnosis Based on Harmonic Wavelet Analysis

2011 ◽  
Vol 143-144 ◽  
pp. 613-617
Author(s):  
Shuang Xi Jing ◽  
Yong Chang ◽  
Jun Fa Leng
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Frequency Domain ◽  
Vibration Signal ◽  
Wavelet Function ◽  
Frequency Region ◽  
Analysis Method ◽  
Vibration Signals ◽  
Harmonic Wavelet ◽  
Frequency Components

Harmonic wavelet function, with the strict box-shaped characteristic of spectrum, has strong ability of identifying signal in frequency domain, and can extract weak components form vibration signals in frequency domain. Using harmonic wavelet analysis method, the selected frequency region and other frequency components of vibration signal of mine ventilator were decomposed into independent frequency bands without any over-lapping or leaking. Simulation and diagnosis example show that this method has good fault diagnosis effect, and the ventilator fault is diagnosed successfully.

A fractal-dimension-based envelope demodulation for rolling element bearing fault feature extraction from vibration signals

2016 ◽  
Vol 230 (18) ◽  
pp. 3194-3211 ◽  
Author(s):  
Juanjuan Shi ◽  
Ming Liang
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
Search Algorithm ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Condition Monitoring ◽  
Interference Frequency ◽  
Fault Feature Extraction

Vibration analysis has been extensively used as an effective tool for bearing condition monitoring. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault feature (i.e. fault-induced impulses), periodic interferences from other mechanical/electrical components, and background noise. The incipient impulses which excite as well as modulate the resonance frequency of the system are easily masked by compounded effects of periodic interferences and noise, making it challenging to do a reliable fault diagnosis. As such, this paper proposes an envelope demodulation method termed short time fractal dimension (STFD) transform for fault feature extraction from such vibration signal mixture. STFD transform calculation related issues are first addressed. Then, by STFD, the original signal can be quickly transformed into a STFD representation, where the envelope of fault-induced impulses becomes more pronounced whereas interferences are partly weakened due to their morphological appearance differences. It has been found that the lower the interference frequency, the less effect the interference has on STFD representations. When interference frequency keeps increasing, more effects on STFD representations will be resulted. Such effects can be reduced by the proposed kurtosis-based peak search algorithm (KPSA). Therefore, bearing fault signature is kept and interferences are further weakened in the STFD-KPSA representation. The proposed method has been favourably compared with two widely used enveloping methods, i.e. multi-morphological analysis and energy operator, in terms of extracting impulse envelopes from vibration signals obscured by multiple interferences. Its performance has also been examined using both simulated and experimental data.

Diagnosis of Machinery Fault Status using Transient Vibration Signal Parameters

2002 ◽  
Vol 8 (3) ◽  
pp. 321-335 ◽  
Author(s):  
Zhidong Chen ◽  
Chris K. Mechefske
Keyword(s):  
Vibration Signal ◽  
Rolling Element Bearings ◽  
Machine Condition ◽  
Transient Vibration ◽  
Vibration Signals ◽  
Model Based ◽  
Machine Condition Monitoring ◽  
Signal Parameters ◽  
Rolling Element ◽  
Efficient Machine

This paper reports the results of an investigation in which a Prony model based method is developed. The method shows potential for analysing transient vibration signals. An example is included that shows how the procedure was employed to analyse the transient vibration signals created from faulty low speed rolling element bearings. Spectral plots generated by applying the procedure to very short data samples, as well as trending parameters based on these spectral estimations and Prony parameters, are presented. An equation was also derived to quantitatively determine the fault status. It is shown that application of the Prony model based method has the potential to be an effective as well as efficient machine condition monitoring and diagnostic tool where short duration transient vibration signals are being generated.

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