Nonlinear Squeezing Time-Frequency Transform and Application in Rotor Rub-Impact Fault Diagnosis

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Shibin Wang ◽  
Laihao Yang ◽  
Xuefeng Chen ◽  
Chaowei Tong ◽  
Baoqing Ding ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Periodic Oscillation ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Rotor Systems ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Comparison Results ◽  
Vibration Signal Analysis ◽  
Simulation Signal

Vibration signal analysis has been proved as an effective tool for condition monitoring and fault diagnosis for rotating machines in the manufacturing process. The presence of the rub-impact fault in rotor systems results in vibration signals with fast-oscillating periodic instantaneous frequency (IF). In this paper, a novel method for rotor rub-impact fault diagnosis based on nonlinear squeezing time-frequency (TF) transform (NSquTFT) is proposed. First, a dynamic model of rub-impact rotor system is investigated to quantitatively reveal the periodic oscillation behavior of the IF of vibration signals. Second, the theoretical analysis for the NSquTFT is conducted to prove that the NSquTFT is suitable for signals with fast-varying IF, and the method for rotor rub-impact fault diagnosis based on the NSquTFT is presented. Through a dynamic simulation signal, the effectiveness of the NSquTFT in extracting the fast-oscillating periodic IF is verified. The proposed method is then applied to analyze an experimental vibration signal collected from a test rig and a practical vibration signal collected from a dual-rotor turbofan engine for rotor rub-impact fault diagnosis. Comparisons are conducted throughout to evaluate the effectiveness of the proposed method by using Hilbert–Huang transform, wavelet-based synchrosqueezing transform (SST), and other methods. The application and comparison results show that the fast-oscillating periodic IF of the vibration signals caused by rotor rub-impact faults can be better extracted by the proposed method.

Gearbox Fault Diagnosis Using Vibration Signal with Wavelet De-Noising

2011 ◽  
Vol 86 ◽  
pp. 735-738
Author(s):  
Zhi Feng Dong ◽  
Hui Cheng ◽  
Hui Jia Yang ◽  
Wei Fu ◽  
Ji Wei Chen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Frequency Domain Analysis ◽  
Sensor Data ◽  
Spectrum Estimation ◽  
Vibration Signals ◽  
Fault Features ◽  
Vibration Signal Analysis ◽  
Simulation Signal ◽  
The Time Domain

This paper dealt with the gearbox fault diagnosis with vibration signal analysis. The vibration signals from experiment contained a lot of noises which result from motor, gears, bears and box, and were collected through accelerate sensor, data collector and computer. The wavelet de-noising stratification was used to de-noise the vibration signals before the frequency-domain analysis was done. The effects of the simulation signal de-noising was contrasted, and the noise cancellation the power spectrum estimation was carried out. The experimental and analytical results show that the different features are indicated with vibration signal of the normal gearbox and the signal with bolts loosened of the gearbox. The gearbox fault with bolts loosened can be diagnosed by extracting the time-domain fault features of vibration signals.

Gearbox Fault Diagnosis Based on EMD and Coefficient-Energy Value

2012 ◽  
Vol 542-543 ◽  
pp. 234-237
Author(s):  
Ping Wang ◽  
De Xiang Zhang ◽  
Yan Li Liu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Vibration Signals ◽  
Energy Value ◽  
Mode Function ◽  
Mode Decomposition ◽  
Gearbox Vibration ◽  
Vibration Signal Analysis ◽  
Fault Information

This paper applies the empirical mode decomposition (EMD) methods to gearbox vibration signal analysis capture from vibrating acceleration sensor for gearbox fault diagnosis. The original modulation fault vibration signals are firstly decomposed into a number of intrinsic mode function (IMF) by the EMD method. Then the fault information diagnosis of the gearbox vibration signals can be extracted from the coefficient-energy value of intrinsic mode function. Experiment result has shown the feasibility and efficiency of the EMD algorithms and energy characteristic method in fault diagnosis and fault message abstraction. It is significant for the monitor operating state of gearbox and detects incipient faults as soon as possible.

Mixed-Mode Signal Detection of Road Vehicle Vibration Using Hilbert-Huang Transform

2015 ◽  
Author(s):  
Julien Lepine ◽  
Michael Sek ◽  
Vincent Rouillard
Keyword(s):  
Mixed Mode ◽  
Vibration Signal ◽  
Significance Test ◽  
Mathematical Framework ◽  
Vehicle Speed ◽  
Road Vehicle ◽  
Vibration Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Vibration

The Hilbert-Huang Transform (HHT) is a fully adaptive time-frequency analysis method which is applicable to nonlinear and nonstationary processes. However, this promising method is fairly new and its range of applications is not well known. Furthermore, its mathematical framework is not yet fully developed. So far, the HHT has yielded interesting results for many applications such as biomedical, geophysical, meteorological and health monitoring, but there is no evidence of its application on complex mixed-mode vibration signals. To fill that gap, this paper investigates the application of the HHT to detect the different modes of road vehicle vibration signals. These modes originate from road roughness variation and vehicle speed which create nonstationary random vibration. Other modes are due to road surface aberrations which create transient events and the engine and drive train system of the vehicle which create harmonic vibrations. The energy density/average period significance test based on the HHT is assessed to detect these modes. The results, based on purposefully created synthetic test signals, reveal the limitations and shortcomings of the HHT based technique to detect and separate the various components of the mixed-mode vibration signals such as vehicle vibration signal.

Vibration Signal Analysis and Fault Diagnosis of Gears Based on Hilbert Marginal Spectrum

2014 ◽  
Vol 909 ◽  
pp. 121-126 ◽  
Author(s):  
Jiang Ping Wang ◽  
Jin Cui
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Operating Conditions ◽  
Characteristic Time Scale ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Vibration Signal Analysis ◽  
Non Stationary Signal

Hilbert-Huang transform is a new method of signal processing, which is very suitable for dealing with nonlinear and non-stationary signal. In this article, a gear fault diagnosis method based on Hilbert marginal spectrum is proposed in view of the non-stationary characteristics of gear vibration signal. First the original vibration signal is decomposed into several intrinsic mode functions (IMF) of different characteristic time scale smoothly by means of empirical mode decomposition (EMD) method. Then the Hilbert-Huang transform is carried out for IMF and the Hilbert marginal spectrum under different operating conditions are obtained. Gear faults can be judged through the analysis of the marginal spectrum. The experimental results show that this method can effectively diagnose the gear faults.

Identification and Modeling of Vibration Signals in Startup Stage of Electric Driven Reciprocating Pump

2012 ◽  
Vol 184-185 ◽  
pp. 256-262
Author(s):  
Miao Rong Lv ◽  
Mei Li ◽  
Shi Gang Shen ◽  
Bao Jian Wei
Keyword(s):  
Time Consistency ◽  
Vibration Signal ◽  
Good Time ◽  
Vibration Signals ◽  
Time Frequency ◽  
Characteristic Extraction ◽  
Signal Simulation ◽  
Vibration Signal Analysis ◽  
Reciprocating Pump ◽  
Moving Parts

How to realize signal modeling and vibration signal characteristic extraction is a very significant topic. A large amount of drilling pump vibration signals were acquired from the indoor tests. The startup signals with time consistency were segmented from these measurement signals and analyzed in detail. There are mainly such five types of vibrations in the startup signals as the pump body’s vibration, whistle, shocks in moving parts, and impacts of the value lifted off or dropped on the seat, friction or grinding between moving parts. The pump body’s vibration and whistle have good time-frequency characteristics and change very regularity, which are defined as the startup vibration in this paper. The pump body’s vibration signals are modeled by OFMM method. After to exclude the OFMM modeling signal, the remaining signal was separated into different integrated components according to their vibration sources by PFM method, a HMM whistle vibration model based on PFM parameters was achieved. Furthermore, A combination of OFMM and HMM model is used to describe the pump startup vibration. Realistic simulation on the pump’s startup vibration has been achieved. Signal simulation was also carried out by use of this combination model. This approach is expected to become a powerful tool for drilling pump’s startup vibration signal analysis and modeling.

Locomotive Engine Cylinder Head and Fuel Injecting Vibration Signal Analysis

2011 ◽  
Vol 66-68 ◽  
pp. 608-613
Author(s):  
Qing Song Hu ◽  
G. X. Li ◽  
Shou Qi Cao ◽  
Li Hong Xu
Keyword(s):  
Fault Diagnosis ◽  
Cylinder Head ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Engine Cylinder ◽  
Locomotive Engine ◽  
Rich Information ◽  
Vibration Signal Analysis ◽  
Transportation Company

Cylinder head and fuel injecting vibration consist of rich information about the working state of the locomotive engine, which shows important potential on the engine fault online diagnosis and regular maintenance. Through acquiring the cylinder head and fuel injecting vibration signals, comparing with the standard signals, fault diagnosis strategy is researched. By analyzing the cylinder head vibration curve with the method of vibration comparison value, the working states of all the cylinders are obtained. With the vibration curve of the fuel injecting, the problem of the injectors is found which helps to locate the fault. The strategy is applied to the No.0015 locomotive of the Rizhao Seaport Transportation Company. The matching between the analysis result and practical locomotive state shows the validity of the strategy which can dramatically improve the maintaining efficiency as well as decrease the degree of over maintenance and lack of maintenance.

Blasting Vibration Signal Analysis Based on Hilbert-Huang Transform

2011 ◽  
Vol 474-476 ◽  
pp. 2279-2285 ◽  
Author(s):  
Dong Li ◽  
Fang Xiang ◽  
Hao Quan Liu ◽  
Tao Guo ◽  
Guang Hua Wu
Keyword(s):  
Vibration Signal ◽  
Blasting Vibration ◽  
Matlab Simulation ◽  
Base Function ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Signal Change ◽  
Vibration Signal Analysis ◽  
Non Stationary Signal

This paper introduces the empirical mode decomposition and Hilbert transform principle. The validity and superiority of Hilbert—Huang transform is proved by MATLAB simulation experiment on computer. Finally, HHT method is used to analyze the collected blasting vibration signal as an example. Research shows that EMD method can process this kind of non-stationary signal such as blasting vibration effectively. Each IMF component decomposed by EMD has clear physical meaning. IMF is determined by signal itself. It has no base function and is adaptive. It can extract main characteristics of signal change and is suitable for analysis of blasting vibration signal which has the features of fast mutation and attenuation. The distribution of time-frequency-energy can be quantitatively described by HHT.

Marine Diesel Engine Fault Diagnosis by Using an Improved Hilbert Spectrum

2006 ◽  
Vol 50 (04) ◽  
pp. 378-387
Author(s):  
Hongkun Li ◽  
Peilin Zhou ◽  
Xiaojiang Ma
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Injection System ◽  
Distribution Method ◽  
Hilbert Spectrum ◽  
Time Frequency ◽  
Marine Diesel ◽  
Vibration Signal Analysis

Vibration signal analysis is a useful method for recognizing the pattern of a machine's working condition. However, it is difficult to recognize nonstationary and nonlinear vibration signal patterns satisfactorily with the traditional Fourier spectrum method. This paper introduces a novel time-frequency distribution method: an improved Hilbert spectrum (HS) for nonstationary vibration signal analysis, that is, applying a wavelet packet de-noise method as a preprocess of the HS. The HS is developed according to instantaneous frequency analysis by using a new kind of signal analysis method called empirical mode decomposition (EMD), which is highly accurate in analyzing various nonstationary and nonlinear signals. Due to a self-adaptive decomposition process, noise has a great effect on the accuracy of the EMD process and the corresponding HS. This has limited the application of the HS on real vibration analysis. In this study, a wavelet packet de-noising technique is employed as a preprocessing to improve the signal-to-noise ratio and the accuracy of the HS. Experimental data of a marine diesel fuel injection system are used to evaluate the improved methodology for system pattern recognition and fault diagnosis.

scholarly journals Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Bo Wu ◽  
Zhe Tong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Optimal Number ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

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