scholarly journals A Three-Phase Current Tacholess Envelope Order Analysis Method for Feature Extraction of Planetary Gearbox under Variable Speed Conditions

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5714
Author(s):  
Xun Zhang ◽  
Guanghua Xu ◽  
Jiachen Kuang ◽  
Lin Suo ◽  
Sicong Zhang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Speed Estimation ◽  
Analysis Method ◽  
Variable Speed ◽  
Extraction Technology ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Phase Current ◽  
Order Analysis ◽  
Three Phase

Planetary gearboxes are the key components of large equipment, such as wind turbines, shield machines, etc. The operating state of the planetary gearbox is related to the safety of the equipment as a whole, and its feature extraction technology is essential. In assessing the problem of the non-stationarity of the current signal under variable speed conditions and the difficulty of evaluating the operating state of the planetary gearbox under a tacholess condition, a three-phase current, variable-speed tacholess envelope order analysis method is proposed. Firstly, a tacholess rotation speed estimation is completed by extracting the trend term of the instantaneous frequency of the asynchronous motor’s three-phase currents. The motor slip rate is assumed to be constant. Then, the envelope order analysis signal is obtained by re-sampling in the angular domain. Finally, the features of the envelope order signal are extracted, and a linear discriminant analysis (LDA) algorithm is used to fuse multiple indexes to generate a comprehensive feature reflecting the operating status of the planetary gearbox. The results of the simulation analysis and experimental verification show that the proposed method is effective in evaluating the operating state of the planetary gearbox under variable speed conditions. Compared with the traditional time–frequency ridge extraction method, the tacholess speed estimation method can improve the instantaneous speed estimation accuracy. The comprehensive index of envelope order completes the planetary gearbox state identification process, and a 95% classification accuracy rate is achieved.

Variable Speed Estimation for Wheel-Bearings with STFT and BP Neural Network

2014 ◽  
Vol 945-949 ◽  
pp. 1112-1115
Author(s):  
Yuan Zhou ◽  
Bin Chen ◽  
Bao Cheng Gao ◽  
Si Jie Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Background Noise ◽  
Speed Estimation ◽  
Variable Speed ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Novel Method ◽  
Instantaneous Frequencies ◽  
Short Time

For the variable speed estimation of wheel-bearings in strong background noise, a novel method with the short-time Fourier transform and BP neural network (STFT-BPNN) is proposed. In the method, it calculates the time-frequency spectrum with STFT technique. Then the instantaneous frequency is estimated by peak detection. Taking the instantaneous frequencies as the input vectors, the BP neural network is trained to fit the discrete instantaneous frequencies. The effectiveness of proposed method is demonstrated by simulation. Experimental results show that proposed method provides better performance on variable speed estimation for wheel-bearings.

scholarly journals SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4457 ◽  
Author(s):  
She ◽  
Zhu ◽  
Tian ◽  
Wang ◽  
Yokoi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Recognition Accuracy ◽  
Hand Movement ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Movement Recognition ◽  
S Transform

Feature extraction, as an important method for extracting useful information from surfaceelectromyography (SEMG), can significantly improve pattern recognition accuracy. Time andfrequency analysis methods have been widely used for feature extraction, but these methods analyzeSEMG signals only from the time or frequency domain. Recent studies have shown that featureextraction based on time-frequency analysis methods can extract more useful information fromSEMG signals. This paper proposes a novel time-frequency analysis method based on the Stockwelltransform (S-transform) to improve hand movement recognition accuracy from forearm SEMGsignals. First, the time-frequency analysis method, S-transform, is used for extracting a feature vectorfrom forearm SEMG signals. Second, to reduce the amount of calculations and improve the runningspeed of the classifier, principal component analysis (PCA) is used for dimensionality reduction of thefeature vector. Finally, an artificial neural network (ANN)-based multilayer perceptron (MLP) is usedfor recognizing hand movements. Experimental results show that the proposed feature extractionbased on the S-transform analysis method can improve the class separability and hand movementrecognition accuracy compared with wavelet transform and power spectral density methods.

A New Kind of FRFT Analysis Method for Multi-Component LFM Signals

2011 ◽  
Vol 204-210 ◽  
pp. 973-978
Author(s):  
Qiang Guo ◽  
Ya Jun Li ◽  
Chang Hong Wang
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Fractional Fourier Transform ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Low Snr ◽  
The Time Domain ◽  
Signal Analysis Method

To effectively detect and recognize multi-component Linear Frequency-Modulated (LFM) emitter signals, a multi-component LFM emitter signal analysis method based on the complex Independent Component Analysis(ICA) which was combined with the Fractional Fourier Transform(FRFT) was proposed. The idea which was adopted to this method was the time-domain separation and then time-frequency analysis, and in the low SNR cases, the problem which is generally plagued by noised of feature extraction of multi-component LFM signal based on FRFT is overcame. Compared to the traditional method of time-frequency analysis, the computer simulation results show that the proposed method for the multi-component LFM signal separation and feature extraction was better.

Signal Feature Extraction about the Oil Whirl and Oil Oscillation Based on EMD

2011 ◽  
Vol 141 ◽  
pp. 483-487
Author(s):  
Bao Jie Xu ◽  
Ran Liu
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Oil Whirl ◽  
Adaptive Time

The article discusses the EMD and adaptive time-frequency analysis method based on EMD, and explains the characteristics about oil whirl, oil oscillation. Apply EMD and Hilbert-Huang t transformation to extract characteristics, which verifies applying EMD into feature extraction is effective.

A Time-Frequency Analysis Approach Based on Plane Partition for Fault Diagnosis of the Planetary Gearbox Under Nonstationary Conditions

2018 ◽  
Author(s):  
Yue Hu ◽  
Xiaotong Tu ◽  
Fucai Li
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Repair Costs ◽  
Important Research Topic ◽  
Planet Gear ◽  
Nonstationary Conditions

The planetary gearbox is one of the key components in the rotating machinery. The planetary gearbox is prone to malfunction, which increases downtime and repair costs. Hence, the fault diagnosis of the planetary gearbox is an important research topic. The acquired signal from the planetary gearbox exhibit strongly time-variant and nonstationary features since the planetary gearbox usually works at time-varying speeds. In this study, a new time-frequency analysis method is proposed. This method takes the spectrum shape into account and partitions the time-frequency into several components. Then the fault feature of the planetary gearbox is detected by analyzing the decomposed components. The simulated signal and the experimental signals under nonstationary conditions are analyzed to verify the effectiveness the proposed method. Results show that the proposed method can efficiently extract the fault feature of the planet gear.

scholarly journals A Tacholess Order Analysis Method for PMSG Mechanical Fault Detection with Varying Speeds

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 418
Author(s):  
Erik Etien ◽  
Abdallah Allouche ◽  
Laurent Rambault ◽  
Thierry Doget ◽  
Sebastien Cauet ◽  
...  
Keyword(s):  
Current Measurement ◽  
Synchronous Machines ◽  
Analysis Method ◽  
Variable Speed ◽  
Position Measurement ◽  
Order Analysis ◽  
Order Tracking ◽  
Spectral Components ◽  
Mechanical Faults ◽  
Experimental Test Bench

This article deals with the detection of mechanical faults in synchronous machines from single current measurement at variable speed. The proposed approach is based on an order tracking method in which the analysis signal is sampled as a function of the mechanical angle. In this case, the spectral components become independent of the speed and the frequency analysis can be exploited. Order tracking is generally implemented from a position measurement. In this work we present a method that allows us to estimate this position and the analysis signal from only one current measurement. The proposed approach allows an intuitive adjustment of the algorithm parameters. Secondly, a statistical method is used to finalize the diagnosis. At variable speed, this type of method is difficult to implement and we show that order tracking makes it possible to simplify the analysis. The procedure is tested in simulation and on a experimental test bench.

Fracture Images Classification Based on Fractional Cosine Transform and Markov Mode

2011 ◽  
Vol 311-313 ◽  
pp. 970-973
Author(s):  
Yong Liang Zhang ◽  
Li Xin Gao ◽  
Ling Li
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Analysis Method ◽  
Cosine Transform ◽  
Time Frequency ◽  
Fracture Failure ◽  
Metal Fracture ◽  
Correct Recognition Rate ◽  
Better Than

Fracture images automatic classification and recognition is an important one of fracture failure intelligent diagnosis, and in which feature extraction is a key issue. In this paper, fractional cosine transform, which is a useful time-frequency analysis method, is used in feature extraction of fracture images, and then the classification of fatigue, dimples, intergranular and cleavage is performed by Hidden markov model (HMM). For metal fracture images classification, experiment shows that fractional cosine transform is better than the cosine transform in fracture images feature description, and the correct recognition rate can be achieved 98.8% based on HMM classification mode

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