Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression

Soualhi, Abdenour; Medjaher, Kamal; Zerhouni, Noureddine

doi:10.1109/tim.2014.2330494

Cited by 544 publications

(201 citation statements)

References 30 publications

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“…In the process of empirical mode decomposition, each IMF component must satisfy the following two conditions: firstly, the number of the extreme points and the zero points should be equal to or not more than one; secondly, the mean of the maximum and minimum of extreme points is zero at any point in the signal curve [10]. Since most of the nonstationary signals do not satisfy the IMF conditions directly, Huang made the following assumptions [11]: any complex signal is composed of IMFS which are independent of each other and each IMF component can be linear or nonlinear. For the signal ( ), its empirical mode decomposition equation can be expressed as…”

Section: Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

Research on Fault Diagnosis Based on Singular Value Decomposition and Fuzzy Neural Network

Gai

2018

Shock and Vibration

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A method based on singular value decomposition (SVD) and fuzzy neural network (FNN) was proposed to extract and diagnose the fault features of diesel engine crankshaft bearings efficiently and accurately. Firstly, vibration signals of crankshaft bearings in known state under the same working condition were decomposed by EMD to obtain the modal components containing faultfeature information. Then, the singular values of modal components which include the main fault features were used as the initial vector matrix, where the eigenvectors were decomposed to form a fault characteristic matrix. At last, the fault features matrix was trained by the fuzzy neural network, in order to realize the diagnosis and identification of the crankshaft bearings in different states in the form of numerical values. The experiment showed that the numerical identification of the fuzzy neural network based on the singular value had high fault diagnosis accuracy and stability. This method can also reflect the gradual change of the crankshaft bearings' fault to some extent, so it has the desired reliability and value.

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Section: Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

Research on Fault Diagnosis Based on Singular Value Decomposition and Fuzzy Neural Network

Gai

2018

Shock and Vibration

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“…Through current (29) and voltage signals, the negative sequence impedance values are obtained, and the magnitude of the difference of these with respect to the average values of the machine indicates the severity of the failure. An effective procedure through the negative current sequence should distinguish between failures due to voltage unbalance, saturation, winding asymmetries, eccentricity and short circuits; however, a short circuit between turns is a failure that is difficult to detect, and generally, the machine will continue to work until it is out of service, Bellini et al [23].…”

Section: Stator Asymmetrymentioning

confidence: 99%

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

2017

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Despite the complex mathematical models and physical phenomena on which it is based, the simplicity of its construction, its affordability, the versatility of its applications and the relative ease of its control have made the electric induction motor an essential element in a considerable number of processes at the industrial and domestic levels, in which it converts electrical energy into mechanical energy. The importance of this type of machine for the continuity of operation, mainly in industry, is such that, in addition to being an important part of the study programs of careers related to this branch of electrical engineering, a large number of investigations into monitoring, detecting and quickly diagnosing its incipient faults due to a variety of factors have been conducted. This bibliographic research aims to analyze the conceptual aspects of the first discoveries that served as the basis for the invention of the induction motor, ranging from the development of the Fourier series, the Fourier transform mathematical formula in its different forms and the measurement, treatment and analysis of signals to techniques based on artificial intelligence and soft computing. This research also includes topics of interest such as fault types and their classification according to the engine, software and hardware parts used and modern approaches or maintenance strategies.

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“…The Recursive Mean Separation Histogram Equalization (RMSHE) [4] enhances image by iterating BBHE technique. Recently, the gain controllable clipped histogram equalization (GC-CHE) [9] method performs clipped histogram equalization for preserving the brightness of an image. In Demerol's method of contrast enhancement the singular value decomposition matrix [10][11] was applied to the low level sub band.…”

Section: Proposed Algorithm For Image Enhancementmentioning

confidence: 99%

An Image Enhancement Framework for Fault Diagnosis and Feature Extraction

2017

IJSR

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Image enhancement refers to accentuation, or sharpening, of image features such as edges, boundaries, or contrast to make a graphic display more useful for display and analysis. The enhancement process does not increase the inherent information content in the data. But it does increase the dynamic range of the chosen features so that they can be detected easily. The greatest difficulty in image enhancement is quantifying the criterion for enhancement. Therefore, a large number of image enhancement techniques are empirical and require interactive procedures to obtain satisfactory results. Many image enhancement techniques are based on spatial operations performed on local neighborhoods of input pixels. Often, the image is convolved with a finite impulse response filter called spatial mask. This study will highlight various image enhancement techniques along with their benchmark results.Image enhancement technologies have attracted much attention during the diagnosis process. The principle objective of image enhancement techniques is to process an input image so that the resultant image is more suitable than the original image for specific application. Traditional global histogram equalization usually causes excessive contrast. Enhancement while local histogram equalization may cause block effect. To overcome these problems, a new method for image contrast enhancement is developed. The novelty of the proposed method is that the weighted average of the histogram equalized, gamma corrected and the original image are combined to obtained the enhanced processed image .The proposed algorithm not only achieve contrast enhancement but also preserves the brightness level. Experimental results show that the proposed algorithm has good performance on enhancing contrast and visibility for a majority of images.

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Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression

Cited by 544 publications

References 30 publications

Research on Fault Diagnosis Based on Singular Value Decomposition and Fuzzy Neural Network

Research on Fault Diagnosis Based on Singular Value Decomposition and Fuzzy Neural Network

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

An Image Enhancement Framework for Fault Diagnosis and Feature Extraction

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