Fault-related Features Research Articles

Fault Detection and Diagnosis in Gears Using Wavelet Analysis Techniques and Comparison on their Diagnostic Capability<sup></sup>

The concept of vibration based condition monitoring technology has been developing at a rapid stage in the recent years suiting to the maintenance of sophisticated and complicated machines. Nowadays, wavelet analysis based signal processing technique is applied as effective tool for condition monitoring. The experimental studies were conducted on the gear testing apparatus to obtain the vibration signal from a healthy gear and an induced faulty gear. In this paper, two different techniques using Laplace wavelet as base function are used to characterize the fault in the gear signals, specifically wavelet enveloped power spectrum and wavelet kurtosis. The wavelet parameters are optimized using genetic algorithm to select most fault related features. A comparative study detailing features of fault characterization is also given in order to understand the effectiveness of both the wavelet based signal processing methods and their fault diagnosis capability.

Diagnostics of gear deterioration using EEMD approach and PCA process

The research objective is to evaluate the diagnostic effectiveness of the gear deterioration through the feature extraction process as well as the feature sifting approach. The gear fault-related features in the vibration signals are extracted by using the ensemble empirical mode decomposition method (EEMD) and the marginal Hilbert spectrum analysis. The extracted features in different scales of time domain and frequency domain are sifted through the principal component analysis (PCA). The features of high priority in the principal space represent the majority of the dynamical characteristics of the faulted gears. The artificial neural network (ANN) is employed to classify the selected dominated principal components for the purpose of diagnosing the gear deterioration. The diagnostic results obtained using the ANN classifier show that the different types and levels of gear malfunctions can be identified effectively by the proposed approach. It is also noted that the PCA process can simultaneously enhance the accuracy of gear fault diagnosis and reduce the feature dimensionality for the purpose of increasing the computational efficiency.

Dynamic linear models-based time series decomposition and its application on bearing fault diagnosis

Although various methods have been presented in the literature for rotating machine fault detection, it still remains a huge challenge to accurately extract features from non-stationary vibration signals with a high noise level, typically in the case of rolling element bearings faults diagnosis. Due to its random and non-stationary nature, fault related features are difficult to extract by common techniques and are usually overwhelmed by noise and macro-structural vibrations. In this paper, a new time series decomposition method based on a dynamic linear model is proposed, which provides time domain decomposition of a non-stationary signal into collections of latent components. The signal of a damaged bearing consists of exponentially decaying ringing that occurs periodically at the bearing characteristic defect frequency. Dynamic linear models with time-varying cyclical components provide a more generalized and adaptive description on rolling element bearings’ vibration signal with time-varying cyclical behavior. This allows the precise isolation of latent, quasi-cyclical bearing fault components via inferences on the time-varying parameters which characterize these components. An accelerated whole lifetime test of bearing has been performed to collect vibration data, which is utilized to validate the effectiveness of the proposed method.

Fault diagnosis of induction motors utilizing local binary pattern-based texture analysis

Abstract Fault diagnosis of induction motors in the practical industrial fields is always a challenging task due to the difficulty that lies in exact identification of fault signatures at various motor operating conditions in the presence of background noise produced by other mechanical subsystems. Several signal processing approaches have been adopted so far to mitigate the effect of this background noise in the acquired sensor signal so that fault-related features can be extracted effectively. Addressing this issue, this paper proposes a new approach for fault diagnosis of induction motors utilizing two-dimensional texture analysis based on local binary patterns (LBPs). Firstly, time domain vibration signals acquired from the operating motor are converted into two-dimensional gray-scale images. Then, discriminating texture features are extracted from these images employing LBP operator. These local feature descriptors are later utilized by multi-class support vector machine to identify faults of induction motors. The efficient texture analysis capability as well as the gray-scale invariance property of the LBP operators enables the proposed system to achieve impressive diagnostic performance even in the presence of high background noise. Comparative analysis reveals that LBP8,1 is the most suitable texture analysis operator for the proposed system due to its perfect classification performance along with the lowest degree of computational complexity.

Multi-Scale Analysis Based Ball Bearing Defect Diagnostics Using Mahalanobis Distance and Support Vector Machine

The objective of this research is to investigate the feasibility of utilizing the multi-scale analysis and support vector machine (SVM) classification scheme to diagnose the bearing faults in rotating machinery. For complicated signals, the characteristics of dynamic systems may not be apparently observed in a scale, particularly for the fault-related features of rotating machinery. In this research, the multi-scale analysis is employed to extract the possible fault-related features in different scales, such as the multi-scale entropy (MSE), multi-scale permutation entropy (MPE), multi-scale root-mean-square (MSRMS) and multi-band spectrum entropy (MBSE). Some of the features are then selected as the inputs of the support vector machine (SVM) classifier through the Fisher score (FS) as well as the Mahalanobis distance (MD) evaluations. The vibration signals of bearing test data at Case Western Reserve University (CWRU) are utilized as the illustrated examples. The analysis results demonstrate that an accurate bearing defect diagnosis can be achieved by using the extracted machine features in different scales. It can be also noted that the diagnostic results of bearing faults can be further enhanced through the feature selection procedures of FS and MD evaluations.

Detection and diagnosis of surface wear failure in a spur geared system using EEMD based vibration signalanalysis

Gears are used for transmission of power, motion or both. Under increased power and higher speeds, tribological failures such as scuffing, pitting, mild wear and tooth breakage are of major concern. This paper presents the results of experimental investigations carried out to assess wear in spur gears of a back-to-back gear box under accelerated test conditions. The studies considered the estimation of specific lubricant film thickness and its effects on the fault growth on gear teeth surface. Ensemble empirical mode decomposition (EEMD) technique is used to extract the fault related features from the vibration signals acquired from the gearbox. The results highlight the advantage of EEMD technique for effective assessment of wear in spurgears.

An energy kurtosis demodulation technique for signal denoising and bearing fault detection

Rolling element bearings are commonly used in rotary machinery. Reliable bearing fault detection techniques are very useful in industries for predictive maintenance operations. Bearing fault detection still remains a very challenging task especially when defects occur on rotating bearing components because the fault-related features are non-stationary in nature. In this work, an energy kurtosis demodulation (EKD) technique is proposed for bearing fault detection especially for non-stationary signature analysis. The proposed EKD technique firstly denoises the signal by using a maximum kurtosis deconvolution filter to counteract the effect of signal transmission path so as to highlight defect-associated impulses. Next, the denoised signal is modulated over several frequency bands; a novel signature integration strategy is proposed to enhance feature characteristics. The effectiveness of the proposed EKD fault detection technique is verified by a series of experimental tests corresponding to different bearing conditions.

Is Australia a tectonically stable continent? Analysis of a myth and suggested morphological evidence of tectonism

Occasional references to the relative tectonic instability of the Australian continent have been published over the last hundred years or so. Youthful tectonic forms were described from various parts of the continent throughout that period. Despite this, it was repeatedly claimed that the shield lands in particular were tectonically stable, and as recently as this century reference has been made to a concept embracing a tectonically inert continent. However, some 60 years ago, the accumulated evidence convinced E.S. Hills that in Australia all land surfaces, including the shield lands, and even recent alluvial plains, were tectonically disturbed. This conclusion was reinforced by analyses of seismicity and faulting; by regional geological mapping that revealed widely distributed tectonic forms and especially fault-related features, many of them of neotectonic age; by technological advances that allow faulting episodes to be closely dated; by the recognition of underprinting; and by the realization that many minor forms, previously unrecognized or attributed to other mechanisms or processes, are associated with crustal stress and are of tectonic origin. Thus, while Australia is a relatively stable continent, it is subject to widespread small-magnitude earth movements. Ironically, in view of earlier thinking, neotectonic forms may be better developed and preserved on the shields than elsewhere.

An adaptive artificial immune system for fault classification

Fault diagnosis is very important in ensuring safe and reliable operation in manufacturing systems. This paper presents an adaptive artificial immune classification approach for diagnosis of induction motor faults. The proposed algorithm uses memory cells tuned using the magnitude of the standard deviation obtained with average affinity variation in each generation. The algorithm consists of three steps. First, three-phase induction motor currents are measured with three current sensors and transferred to a computer by means of a data acquisition board. Then feature patterns are obtained to identify the fault using current signals. Second, the fault related features are extracted from three-phase currents. Finally, an adaptive artificial immune system (AAIS) is applied to detect the broken rotor bar and stator faults. The proposed method was experimentally implemented on a 0.37 kW induction motor, and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of broken bar and stator faults in induction motors.

Fault-related Features and Restoration of Fault Slips revealed by Airborne Laser Imagery along the Senya Fault and Adera Fault

Fault-related Features and Restoration of Fault Slips revealed by Airborne Laser Imagery along the Senya Fault and Adera Fault

Editing faults within tetrahedral volume models in real time

Abstract Accurately positioning faults in a geological model is a major concern because they are responsible for offsets of geological sequences. In the tetrahedral models studied in this paper, faults are discontinuities: faces of tetrahedra on either side of a fault are disconnected. Building tetrahedral models can require a large amount of time, especially when there are many faults. We present a tool for making small, real-time, modifications of faults in tetrahedral models arising from geometrical changes required either by new subsurface data or by new interpretations of existing subsurface data. Fault editing is achieved by moving control points on the fault in the tetrahedral grid and by computing a distortion property over an editable volume relative to the control point and spreading this distortion to neighbouring points using the Discrete Smooth Interpolation technique. The editable volume in which tetrahedron vertices are allowed to move is defined by a given distance to the fault. This approach provides a means of editing faults and fault-related features, such as branch-lines.

Seafloor morphology of the Sumatran subduction zone: Surface rupture during megathrust earthquakes?

High-resolution multibeam bathymetry data from the Sumatran subduction zone reveal the regional and local morphology, including small-scale fault-related features and landslides that may be linked to earthquakes in the recent geological past. The accretionary prism is steeply sloped and pervasively eroded, with evidence of unusual landward vergence (seaward fault dip) of the frontal thrusts. Small-scale (5–100 m height) fault scarps, folds, and troughs are common along the seaward edge of the frontal thrust at the deformation front. A model of back-thrust fault slip or bending moment folding during plate-boundary slip, such as during the 2004 M9.2 megathrust earthquake, can explain the position of these features on the seaward fold limb of a seaward-dipping thrust. We infer that in major Sumatran (and other similar settings) plate-boundary earthquakes, coseismic surface rupture may occur at the prism toe.

Classification of gear faults using cumulants and the radial basis function network

Every tooth in a gearbox is alternately meshing and detaching during its operation. Hence, the loading condition of the tooth is alternately changing. Such a condition will make the tooth easily subject to spalling and worn. Moreover, Gaussian type of noise which is always embedded in the measurements makes the signal-to-noise ratio (SNR) of the collected data low and difficult to extract in fault-related features. This paper aims to propose an approach for gear fault classification by using cumulants and the radial basis function (BRF) network. The use of cumulants can minimize Gaussian noise and increase the SNR. The RBF network has proven to be superior to back-propagation networks. The RBF network provides better functions to approximate non-linear inputs and faster in convergence. In this paper, experiments have been conducted on a real gearbox. The cumulants calculated from the vibration signal collected from the inspected gearbox are used as input features. The RBF network is then used as a classifier for various kinds of operating conditions of the gearbox. Results show that the method of classification by combining cumulants and the RBF network is promising and achieved better accuracy.

MULTIPLE BAND-PASS AUTOREGRESSIVE DEMODULATION FOR ROLLING-ELEMENT BEARING FAULT DIAGNOSIS

This paper presents a novel method to enhance the detection and diagnosis of low-speed rolling-element bearing faults based on discrete wavelet packet analysis (DWPA). The method involves the automatic extraction of wavelet packets containing bearing fault-related features from the discrete wavelet packet analysis representation of machine vibrations. Automated selection of the wavelet packets of interest is achieved via an adaptive network-based fuzzy inference system (ANFIS), which can be implemented on-line. The resultant signal extracted by this technique is essentially an optimal multiple band-pass filter of the high-frequency bearing impact transients. Used in conjunction with the autoregressive (AR) spectrum of the envelope signal, a sensitive diagnosis of the bearing condition can be made. The discrete wavelet packet analysis multiple band-pass filtering of the signal results in a significantly improved signal-to-noise ratio compared to its high-pass counterpart, with an exceptional capacity to exclude contaminating sources of vibration. A more modest increase in the signal-to-noise ratio is achieved when compared to digital band-pass filtering, with the filter range adjusted to obtain the best possible isolation of the bearing transients.

Construction and Validation of a Numerical Model of a Reservoir Consisting of Meandering Channels

Summary This paper discusses the modeling of a fluvial reservoir system. Construction of a detailed reservoir model was followed by a coarser numerical simulation model. During the history matching of the latter, we found that fluid transmissibilities had to be reduced significantly to reproduce the observed pressure differentials. Fault-related features are thought to be the reason that the sand continuity is so much lower than that predicted with sedimentological considerations only.

Geology of Salt Lake City, Utah, United States of America

Geology of Salt Lake City, Utah, United States of America

Frequency dependence of scattering coefficients in soft tissues: C. M. Sehgal and J. F. Greenleaf, Department of Physiology and Biophysics, Biodynamics Research Unit, Mayo Clinic, Rochester, MN 55905

The research objective is to evaluate the diagnostic effectiveness of the gear deterioration through the feature extraction process as well as the feature sifting approach. The gear fault-related features in the vibration signals are extracted by using the ensemble empirical mode decomposition method (EEMD) and the marginal Hilbert spectrum analysis. The extracted features in different scales of time domain and frequency domain are sifted through the principal component analysis (PCA). The features of high priority in the principal space represent the majority of the dynamical characteristics of the faulted gears. The artificial neural network (ANN) is employed to classify the selected dominated principal components for the purpose of diagnosing the gear deterioration. The diagnostic results obtained using the ANN classifier show that the different types and levels of gear malfunctions can be identified effectively by the proposed approach. It is also noted that the PCA process can simultaneously enhance the accuracy of gear fault diagnosis and reduce the feature dimensionality for the purpose of increasing the computational efficiency.