scholarly journals Research on fault diagnosis of planetary gearbox based on Variable Multi-Scale Morphological Filtering and improved Symbol Dynamic Entropy

2021 ◽  
Author(s):  
Tongtong Liu ◽  
Lingli Cui ◽  
Jianyu Zhang ◽  
Chao Zhang
Keyword(s):  
Working Conditions ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
High Signal ◽  
Structural Element ◽  
Planetary Gearbox ◽  
Noise Interference ◽  
Multi Scale ◽  
Time Domain Signal ◽  
Morphological Filtering

Abstract Under complex working conditions with noise interference, the fault feature of planetary gearbox is difficult to be extracted and the fault mode is difficult to be identified. To tackle this problem, the technologies of Variable Multi-scale Morphological Filtering (VMSMF) and Average Multi-scale Double Symbolic Dynamic Entropy (AMDSDE) are proposed in this paper. VMSMF selects Chebyshev Window as the structural element and automatically selects the optimal scale parameters according to the signal characteristics of the planetary gearbox, which improves the filtering accuracy and calculation efficiency. AMDSDE fully considers the correlation between various state modes. Once combined with relevant knowledge of Mathematical statistics, the algorithm can effectively reduce misjudgment. Firstly, the Turn Domain Resampling (TDR) is used to transform the time domain signal of variable speed into the angle domain signal that is not affected by speed change. Secondly, the proposed VMSMF is used to de-noise the vibration signal, and the fault signal with a high signal-to-noise ratio is obtained. Finally, AMDSDE is used to extract the entropy value of the fault signal and judge the fault type. The proposed technology is verified by four kinds of signals collected from the sun gear of the planetary gearbox under non-stationary working conditions.

scholarly journals Study on Fault Diagnosis Method of Planetary Gearbox Based on Turn Domain Resampling and Variable Multi-Scale Morphological Filtering

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 52
Author(s):  
Tongtong Liu ◽  
Lingli Cui ◽  
Chao Zhang
Keyword(s):  
Frequency Conversion ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Operating Efficiency ◽  
Planetary Gearbox ◽  
Noise Interference ◽  
Multi Scale ◽  
Morphological Filtering ◽  
Diagnosis Method ◽  
Simulation Signal

The turn domain resampling (TDR) method is proposed in the paper on the basis of the existing angle domain resampling for solving the problem of non-fixed fault frequency under variable working conditions. TDR can select the appropriate sampling order according to the influence of frequency conversion, which avoided the error caused by the spline interpolation method. It can provide accurate parameters for the subsequent calculation of the equivalent frequency order. Variable multi-scale morphological filtering (VMSMF) method is proposed for the purpose of further reducing the interference of noise in resampling signal to feature extraction. VMSMF adaptively selects structural elements according to the parameter change of impact signal to make its scale more targeted. It only needs to calculate once using the optimal structural unit for a particular impact, and the filtering accuracy and operating efficiency have been greatly improved. The main steps of this article are as follows. First, the TDR is used to resample the original signal as to get the resampling signal which is still submerged by the strong noise. In the second step, VMSMF is used to filter the resampling signal to obtain the signal with less noise interference. Finally, the fault characteristics of the filtering signal was extracted and compared with the possible fault frequency calculated by the sampling parameters provided by resampling, so as to determine the fault type of the planetary gearbox. By analyzing the simulation signal and the experimental signal respectively, this method can find out the corresponding fault characteristics effectively.

scholarly journals An Adaptive Filtering Method for Bridge Vibration Signals Based on Improved CEEMDAN and Multi-Scale Permutation Entropy

2021 ◽  
Vol 8 (4) ◽  
pp. 163-168
Author(s):  
Dawei He ◽  
Boxin Wang ◽  
Xin Gao ◽  
Xia Wang
Keyword(s):  
Adaptive Filtering ◽  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Vibration Signal ◽  
Optimization Method ◽  
Permutation Entropy ◽  
Vibration Signals ◽  
Multi Scale ◽  
Long Span Bridge ◽  
Vibration Signal Analysis

Aiming at the serious noise of bridge vibration signals in complex environment, this paper proposed an adaptive filtering and denoising optimization method for bridge structural health monitoring. The method took CEEMDAN algorithm as the core, during the step-by-step decomposition of original signals, white noise with opposite signs was added in each stage, meanwhile multi-scale permutation entropy (MPE) was introduced to analyze the mean entropy of each intrinsic mode function (IMF) at different scales, and components with serious noise were eliminated to complete the first filtering; then, in order to optimize the remaining IMFs for signal reconstruction and ensuring the smoothness and similarity of filtering, an optimized reconstruction model was established to complete the second filtering. Compared with the CEEMDAN method, the proposed method could solve the problems of mode mixing and endpoint effect with good completeness, orthogonality, and signal-to-noise ratio. At last, the advantages and application value of the proposed method were verified again by the vibration signal analysis of a real long-span bridge structure.

scholarly journals Multi-Scale Demodulation for Fault Diagnosis Based on a Weighted-EMD De-Noising Technique and Time–Frequency Envelope Analysis

2020 ◽  
Vol 10 (21) ◽  
pp. 7796 ◽  
Author(s):  
Wei-tao Du ◽  
Qiang Zeng ◽  
Yi-min Shao ◽  
Li-ming Wang ◽  
Xiao-xi Ding
Keyword(s):  
Fault Diagnosis ◽  
Signal To Noise Ratio ◽  
Fast Fourier Transformation ◽  
Intrinsic Mode Functions ◽  
Envelope Analysis ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Envelope Signal

Demodulation is one of the most useful techniques for the fault diagnosis of rotating machinery. The commonly used demodulation methods try to select one sensitive sub-band signal that contains the most fault-related components for further analysis. However, a large number of the fault-related components that exist in other sub-bands are ignored in the commonly used envelope demodulation methods. Based on a weighted-empirical mode decomposition (EMD) de-noising technique and time–frequency (TF) impulse envelope analysis, a multi-scale demodulation method is proposed for fault diagnosis. In the proposed method, EMD is first employed to divide the signal into some IMFs (intrinsic mode functions). Then, a new weighted-EMD de-noising technique is presented, and different weights are assigned to IMFs for construction according to their fault-related degrees; thus, the fault-unrelated components are suppressed to improve the signal-to-noise ratio (SNR). After that, continuous wavelet transformation (CWT) is adopted to obtain the time–frequency representation (TFR) of the de-noised signal. Subsequently, the fault-related components in the entire frequency range scale are calculated together, referring to the TF impulse envelope signal. Finally, a fault diagnosis result can be obtained after the fast Fourier transformation of the TF impulse envelope signal. The proposed method and three commonly used methods are applied to the fault diagnosis of a planetary gearbox with a sun gear spalling fault and a fixed shaft gearbox with a crack fault. The results show that the proposed method can effectively detect gear faults and yields better performance than other methods.

scholarly journals Learning Attention Representation with a Multi-Scale CNN for Gear Fault Diagnosis under Different Working Conditions

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1233 ◽  
Author(s):  
Yong Yao ◽  
Sen Zhang ◽  
Suixian Yang ◽  
Gui Gui
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Vibration Signal ◽  
Multiple Scale ◽  
Attention Mechanism ◽  
Signal Acquisition ◽  
Multi Scale ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Vibration Signal Analysis

The gear fault signal under different working conditions is non-linear and non-stationary, which makes it difficult to distinguish faulty signals from normal signals. Currently, gear fault diagnosis under different working conditions is mainly based on vibration signals. However, vibration signal acquisition is limited by its requirement for contact measurement, while vibration signal analysis methods relies heavily on diagnostic expertise and prior knowledge of signal processing technology. To solve this problem, a novel acoustic-based diagnosis (ABD) method for gear fault diagnosis under different working conditions based on a multi-scale convolutional learning structure and attention mechanism is proposed in this paper. The multi-scale convolutional learning structure was designed to automatically mine multiple scale features using different filter banks from raw acoustic signals. Subsequently, the novel attention mechanism, which was based on a multi-scale convolutional learning structure, was established to adaptively allow the multi-scale network to focus on relevant fault pattern information under different working conditions. Finally, a stacked convolutional neural network (CNN) model was proposed to detect the fault mode of gears. The experimental results show that our method achieved much better performance in acoustic based gear fault diagnosis under different working conditions compared with a standard CNN model (without an attention mechanism), an end-to-end CNN model based on time and frequency domain signals, and other traditional fault diagnosis methods involving feature engineering.

scholarly journals Faulty Gear Diagnosis Using Weighted PCA with Swish Activated BLSTM Classifier

2021 ◽  
Author(s):  
Rohit Ghulanavar ◽  
A Jagadeesh ◽  
Kiran Kumar Dama
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Early Stage ◽  
Principal Component ◽  
Vibration Signal ◽  
Activation Function ◽  
Multi Scale ◽  
Time Domain Signal ◽  
Gear Fault ◽  
Diagnosis Method

Abstract The early faulty gear diagnosis is most necessary in the industry. In the current decade, with the tremendous growth of ANN (Artificial Neural Network), the researcher planned to use DL (Deep Learning) methods to sketch out faults in gear in an early stage. Traditional gear fault diagnosis method mostly utilizes deep NN (Neural Network) related to tine sequence of gathered signals. In this instance, feature extraction in the direction of inverse time domain signal is commonly ignored. To overcome this issue, here in this paper, proposed Weighted Principal Component Analysis (WPCA) and BLSTM (Bi-Directional Long Short Term Memory) along with Swish Activation function for faulty gear diagnosis from the vibration signals. WPCA is utilized to extract multi-scale features related to faulty gear from the vibration signal. Likewise, BLSTM is used to classify the extracted features to diagnose the fault in an earlier stage. Several experiments were conducted to evaluate the proposed work of categorizing the defects in gear from the vibrating signal. Experiments were conducted on three kinds of the dataset to classify the type of faulty gear accurately. The proposed work proves its superiority in organizing the gear faults in a most efficient way than existing methods.

scholarly journals Research on a Noise Reduction Method Based on DTCWT and the Cyclic Singular Energy Difference Spectrum

2020 ◽  
Vol 66 (10) ◽  
pp. 613-626
Author(s):  
Xihui Chen ◽  
Gang Cheng ◽  
Ning Liu ◽  
Xinhui Shi ◽  
Wei Lou
Keyword(s):  
Noise Reduction ◽  
Working Conditions ◽  
Reduction Method ◽  
Vibration Signal ◽  
Difference Spectrum ◽  
Energy Difference ◽  
Frequency Characteristics ◽  
Mechanical Equipment ◽  
Frequency Bands ◽  
Noise Interference

The gear is the most important part of the transmission system of mechanical equipment, and the monitoring and diagnosis of it can improve the reliability of mechanical equipment. However, mechanical equipment generally works in harsh working conditions. The gear vibration signal is subjected to strong noise interference in working conditions, which brings great challenges for the effective diagnosis of gear faults. This paper proposed a noise reduction method based on the dual-tree complex wavelet transform (DTCWT) and cyclic singular energy difference spectrum. First, the gear vibration signal containing strong noise interference is decomposed into a series of signal components with different frequency characteristics by using the time-frequency analysis ability of DTCWT. Then, cyclic singular energy difference spectrum is proposed based on the idea of a cascaded cycle and the successive elimination of noise interference to process each signal component with different frequency characteristics, and the termination conditions of cyclic singular energy difference spectrum can be set according to the noise interference distribution characteristics in different frequency bands. The final noise reduction of the original gear vibration signal can be realized based on signal reconstruction after the noise reduction processing of each signal components with different frequency bands. Finally, experiments are carried out to verify the effectiveness of the proposed method, which is effective and suitable for the noise reduction of the vibration signal.

A compact guided-wave EMAT with pulsed electromagnet for ferromagnetic tube inspection

2020 ◽  
Vol 64 (1-4) ◽  
pp. 951-958
Author(s):  
Tianhao Liu ◽  
Yu Jin ◽  
Cuixiang Pei ◽  
Jie Han ◽  
Zhenmao Chen
Keyword(s):  
Power Plants ◽  
Signal To Noise Ratio ◽  
Small Diameter ◽  
Performance Testing ◽  
Guided Wave ◽  
High Signal ◽  
Receiver Coil ◽  
Signal To Noise ◽  
Corrosion Defects

Small-diameter tubes that are widely used in petroleum industries and power plants experience corrosion during long-term services. In this paper, a compact inserted guided-wave EMAT with a pulsed electromagnet is proposed for small-diameter tube inspection. The proposed transducer is noncontact, compact with high signal-to-noise ratio and unattractive to ferromagnetic tubes. The proposed EMAT is designed with coils-only configuration, which consists of a pulsed electromagnet and a meander pulser/receiver coil. Both the numerical simulation and experimental results validate its feasibility on generating and receiving L(0,2) mode guided wave. The parameters for driving the proposed EMAT are optimized by performance testing. Finally, feasibility on quantification evaluation for corrosion defects was verified by experiments.

Application of Novel Low Current OBIRCH Amplifier and Nanoprobing to Identify Subtle Leakages in Advanced Node Technologies

2018 ◽  
Author(s):  
Satish Kodali ◽  
Liangshan Chen ◽  
Yuting Wei ◽  
Tanya Schaeffer ◽  
Chong Khiam Oh
Keyword(s):  
Signal To Noise Ratio ◽  
Semiconductor Industry ◽  
Fault Isolation ◽  
Ring Oscillator ◽  
High Signal ◽  
The Novel ◽  
Resistance Change ◽  
Three Samples ◽  
Relative Threshold ◽  
First Time

Abstract Optical beam induced resistance change (OBIRCH) is a very well-adapted technique for static fault isolation in the semiconductor industry. Novel low current OBIRCH amplifier is used to facilitate safe test condition requirements for advanced nodes. This paper shows the differences between the earlier and novel generation OBIRCH amplifiers. Ring oscillator high standby leakage samples are analyzed using the novel generation amplifier. High signal to noise ratio at applied low bias and current levels on device under test are shown on various samples. Further, a metric to demonstrate the SNR to device performance is also discussed. OBIRCH analysis is performed on all the three samples for nanoprobing of, and physical characterization on, the leakage. The resulting spots were calibrated and classified. It is noted that the calibration metric can be successfully used for the first time to estimate the relative threshold voltage of individual transistors in advanced process nodes.

scholarly journals VS2 as saturable absorber for Q-switched pulse generation

Nanophotonics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 2569-2576 ◽  
Author(s):  
Lu Li ◽  
Lihui Pang ◽  
Qiyi Zhao ◽  
Yao Wang ◽  
Wenjun Liu
Keyword(s):  
Saturable Absorber ◽  
Nonlinear Optical ◽  
Modulation Depth ◽  
Signal To Noise Ratio ◽  
Transition Metal Dichalcogenides ◽  
Laser Cavity ◽  
Pulse Generation ◽  
High Signal ◽  
Power Pulse ◽  
Metal Dichalcogenides

AbstractTransition metal dichalcogenides have been widely utilized as nonlinear optical materials for laser pulse generation applications. Herein, we study the nonlinear optical properties of a VS2-based optical device and its application as a new saturable absorber (SA) for high-power pulse generation. Few-layer VS2 nanosheets are deposited on the tapered region of a microfiber to form an SA device, which shows a modulation depth of 40.52%. After incorporating the microfiber-VS2 SA into an Er-doped fiber laser cavity, passively Q-switched pulse trains could be obtained with repetition rates varying from 95 to 233 kHz. Under the pump power of 890 mW, the largest output power and shortest pulse duration are measured to be 43 mW and 854 ns, respectively. The high signal-to-noise ratio of 60 dB confirms the excellent stability of the Q-switching state. To the best of our knolowdge, this is the first illustration of using VS2 as an SA. Our experimental results demonstrate that VS2 nanomaterials have a large potential for nonlinear optics applications.

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