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.

Enhanced deep residual network with multilevel correlation information for fault diagnosis of rotating machinery

2020 ◽  
pp. 107754632094971 ◽  
Author(s):  
Shoucong Xiong ◽  
Shuai He ◽  
Jianping Xuan ◽  
Qi Xia ◽  
Tielin Shi
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Learning Algorithms ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Performance ◽  
Residual Network ◽  
Time Frequency ◽  
Correlation Information

Modern machinery becomes more precious with the advance of science, and fault diagnosis is vital for avoiding economical losses or casualties. Among massive diagnosis methods, deep learning algorithms stand out to open an era of intelligent fault diagnosis. Deep residual networks are the state-of-the-art deep learning models which can continuously improve performance by deepening the network structures. However, in vibration-based fault diagnosis, the transient property instability of vibration signal usually calls for time–frequency analysis methods, and the characters of time–frequency matrices are distinct from standard images, which brings some natural limitations for the diagnosis performance of deep learning algorithms. To handle this issue, an enhanced deep residual network named the multilevel correlation stack-deep residual network is proposed in this article. Wavelet packet transform is used to preprocess the sensor signal, and then the proposed multilevel correlation stack-deep residual network uses kernels with different shapes to fully dig various kinds of useful information from any local regions of the processed input. Experiments on two rolling bearing datasets are carried out. Test results show that the multilevel correlation stack-deep residual network exhibits a more satisfactory classification performance than original deep residual networks and other similar methods, revealing significant potentials for realistic fault diagnosis applications.

Vibration-Based Fault Diagnosis of Pump Using WPT

2008 ◽  
Author(s):  
Pan Hong ◽  
Zheng Yuan
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Accurate Method ◽  
Singularity Analysis ◽  
Good Time ◽  
Signal Denoising ◽  
Good Tool ◽  
Time Frequency ◽  
Diagnosis Method

A vibration-based fault diagnosis method of pump units based on wavelet packet transform (WPT) is proposed in this paper. Compared with Fourier transform (FT) and wavelet transform (WT), WPT can subdivide the whole time-frequency domain. It can perform signals with good time resolution at high frequency and vice versa. WPT is considered as a good tool to signal denoising, accounting for its perfect ability in decomposing and reconstructing signal and its characteristic of no redundancy and divulges after denoising. In addition, WPT modulus maximal coefficient provides a simple but accurate method in calculating the Lipschitz exponents, which is the measurement of signal singularity. According to the singularity analysis results of vibration signal, we can recognize the fault pattern of pump units. This paper makes a detail research on signal denoising and singularity analysis based on WPT. Taking the main shaft and thrust bearing vibration signal for example, the experimental results show that WPT is effectively in the fault diagnosis system of pump unit.

Research on the Gear Box Fault Diagnosis of Self-Propelled Artillery Based on Vibration Signal Analysis

2014 ◽  
Vol 940 ◽  
pp. 136-139
Author(s):  
Ren Bin Zhou ◽  
Yong Feng Zhang ◽  
Jie Min Yang ◽  
Feng Ling
Keyword(s):  
Fault Diagnosis ◽  
Data Acquisition ◽  
Real Time ◽  
Signal Analysis ◽  
Great Influence ◽  
Vibration Signal ◽  
Vibration Data ◽  
Gearbox Vibration ◽  
Gear Box ◽  
Vibration Signal Analysis

As a universal component connection and power transmission gear box, is widely used in the modern industrial equipment, but also an easy failure parts, has a great influence on the running state of the working performance of the whole machine. This paper first analyzes the gear box fault form and characteristics, the gear box fault diagnosis method based on vibration signal analysis, and analysis of the vibration signal processing method for gear vibration signal analysis in time domain, including parameters, resonance demodulation method and cepstrum analysis method. Then using Visual C + + language and data acquisition card for real-time acquisition of gearbox vibration data software, including parameter setting, data acquisition module, signal real-time display module and data storage module. The data acquisition program is developed, the actual acquisition of gearbox vibration data of gear fault and bearing fault, and analyzed.

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