Dominant Fault Research Articles

Numerical and experimental analysis of vibratory signals for rolling bearing fault diagnosis

The detecting technique of the mechanical anomaly has been a hot topic in the scientific and engineering community. Vibration analysis has been frequently applied in the condition monitoring and fault diagnosis of rolling element bearings. Unfortunately, the vibration signals collected from a faulty bearing are generally nonstationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method that combines the singular value decomposition (SVD) and local mean decomposition (LMD) is proposed. SVD is a non-parametric technique which has been widely used to eliminate the noise and enhance the impulsive features, then LMD decompose the purified signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. Finally, the proposed method is applied to experimental data and the results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing.DOI: http://dx.doi.org/10.5755/j01.mech.22.3.11962

Reliability Assessment of the Switched Reluctance Motor Drive Under Single Switch Chopping Strategy

In this paper, a detailed set of reliability prediction methodology for switched reluctance motor drive (SRD) considering system specific control strategy and component fault classification is elaborated from component level to system level. At component level, introduction to SRD and its single switch chopping strategy is presented to capture characteristics of SRD reliability under a certain control strategy. The unique fault modes classification and summary methods which are tailored to SRD are applied for identification of system dominant fault modes. Then at system level, binary models (reliability block diagram and part-count model) and multivariate model (Markov model) are adopted to build systematic SRD reliability model, respectively. Especially in the Markov model, state transition diagram and state probability matrix P ( t ) are built in detailed description to constitute the graphical and numerical Markov reliability model. Conclusions can be drawn that compared with RBD, Markov model can capture the effect of specific control strategy on system reliability, and further demonstrates the stronger consistency with SRD practical operation. Fault simulation and experiments are conducted to illustrate the influence on system operation state caused by a control strategy. Also, the results verify the state assessment of system performance after component-level fault occurs.

Insights from heterogeneous structures of the 1999 Mw 7.6 Chi‐Chi earthquake thrust termination in and near Chushan excavation site, Central Taiwan

AbstractWe describe and analyze the surface and subsurface deformation of the 1999 Chi‐Chi earthquake thrust termination of the Chelungpu fault in and near the Chushan excavation site, central Taiwan. In order to minimize damage to engineering structures within a deformation zone and formulate regulations for earthquake fault zones and fault setbacks, one needs to know the characteristics of the deformation zone and the connection between surface and subsurface deformation. The surface deformation zone of high strain induced by the earthquake faulting ranges from 15 to 70 m in width, and characterized by a 0.5 to 2 m high escarpment is much wider in the hanging wall. Exposures in the trench, 40 m long and 10 m deep, excavated across the earthquake ground rupture, show on the one hand the heterogeneous structure of a steep, monocline‐like fold. On the other hand, strikingly different surface deformation profiles on either side of the 14 m wide trench, i.e., semiparabola‐like and monoclinal, do not reflect in any obvious way the subsurface structure. The analysis of our detailed mapping results along with well logs suggests that the significant width variation of the surface deformation zone is attributed to secondary faults branching from the dominant fault at several tens of meters depth. The surface profile of the escarpment is controlled by the depth of the relevant fault tip, and the subsurface heterogeneous structure formed during at least four earthquakes.

Bearing fault diagnosis using Hilbert-Huang transform (HHT) and support vector machine (SVM)

This work presents the application of the Hilbert-Huang transform and its marginal spectrum, for the analysis of the stator current signals for bearing faults diagnosis in asynchronous machines. Firstly, the current signals are decomposed into several intrinsic mode functions (IMFs) using the empirical mode decomposition (EMD). The Hilbert Huang spectrum for each IMF is an energy representation in the time-frequency domain using the instantaneous frequency. The marginal spectrum of each IMF can then be obtained. Secondly, the IMFs that includes dominant fault information are modeled using an autoregressive (AR) model. Finally, the AR model parameters serve as the input fault feature vectors to support vector machine (SVM) classifiers. Experimental studies show that the marginal spectrum of the second IMF can be used for the detection and classification of bearing faults. The proposed approach provides a viable signal processing tool for an online machine health status monitoring.

Fault plane orientations of intermediate‐depth and deep‐focus earthquakes in the Japan‐Kuril‐Kamchatka subduction zone

AbstractIn the northwestern Pacific, the Pacific plate subducts to the west at the Japan, Kuril, and Kamchatka trenches. Throughout most of the subduction zone, the subducting slab is planar and dipping at an angle of 30°–60°, with the exception of a fold in the southern Kuril segment. To investigate how the slab deforms in response to the applied forces and which mechanism generates the earthquakes, we analyze the rupture properties of 111 large (MW≥5.7) intermediate‐depth and deep‐focus earthquakes (60–656 km depth) from 1990 to 2014 in the Japan‐Kuril‐Kamchatka subduction zone. For each earthquake, we use rupture directivity to estimate rupture direction and rupture speed and to distinguish the fault plane from the auxiliary plane of the focal mechanism. Seventy six percent of the earthquakes with sufficient station coverage are well modeled by unilateral rupture propagation. The estimated rupture speeds range from zero to supershear. The estimated rupture directions allow identification of the fault plane as the more horizontal nodal plane for 30 earthquakes, while an additional 11 earthquakes rupture toward the intersection of the nodal planes, so the fault plane cannot be identified. Combining our newly identified fault planes with previously identified fault planes in the region, we observe that in planar slab segments, most earthquakes slip along a dominant fault orientation. For a steeply dipping slab, this orientation is subhorizontal. In more sharply bent slab segments, such as the Kuril fold, deformation is accommodated along more variable fault orientations, including subvertical faults. The correlation of slab geometry with fault orientation suggests that the local stress field controls fault orientations.

Inversion of fault data with low diversity for stress: Principles and applications

Although a minimum of four independent, single-phase fault data are required to solve for a unique reduced stress tensor, we prove in this paper that a smaller number of fault data are sufficient in some instances to solve for part of the reduced stress tensor. One of the principal stress directions is determinable from either two faults with a common null shear direction on the fault planes or three faults with a common intersection in a principal stress plane of the fault planes. This direction is combined with the fault data to determine the possible ranges of other principal stress directions. Determining whether the direction is for the maximum, intermediate or minimum principal stress depends upon constraints provided by slip tendency or more fault data. This approach can also be applied to a set of four or more fault data with low orientation diversity. This new method is finally applied to two different sets of fault data from along the active Chelungpu fault, western Taiwan. The stress orientations determined from the method lie in acceptable ranges for the maximum/minimum principal stresses using other existing and comparable methods, such as the right dihedra/trihedra methods. They differ moderately in the maximum/minimum principal stress directions when compared to the moment tensor method for fault kinematic analysis. The new method has advantages over the right dihedra/trihedra methods in the accuracy of stress estimate and the independence of stress estimate upon the small number of faults that are not parallel to the dominant fault set(s).

Bearing degradation state recognition based on kernel PCA and wavelet kernel SVM

In order to effectively recognize the bearing’s running state, a new method based on kernel principal component analysis (KPCA) and the Morlet wavelet kernel support vector machine (MWSVM) was proposed. First, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD) to obtain the corresponding intrinsic mode function (IMF). The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. Therefore, the nonlinear feature extraction method KPCA was introduced to extract the characteristic features and to reduce the dimension. The extracted characteristic features were inputted into the MWSVM to train and construct the running state identification model, and the bearing’s running state identification was thereby realized. Cases of test and actual were analyzed. The results validate the effectiveness of the proposed algorithm.

Dominant fault plane orientations of intermediate‐depth earthquakes beneath South America

AbstractThe South American subduction zone exhibits considerable variation: the subduction angle alternates between flat and steep; the subducting plate has complex structures; and arc volcanism in the overlying plate has gaps. I investigate the effect of these differences in incoming plate structure and slab geometry on intermediate‐depth earthquakes, specifically their fault orientations and rupture characteristics, and find that slab geometry has the largest impact on fault orientation. I use rupture directivity to estimate rupture direction and rupture velocity and to distinguish the fault plane from the auxiliary plane of the focal mechanism. From analysis of 163 large (Mw≥5.7) intermediate‐depth (60–360 km depth) earthquakes from along the length of South America, estimated rupture azimuths and plunges show no trends, appearing to be randomly distributed on the determined population of fault plane orientations, and a majority of earthquakes are made up of multiple subevents. As seen in other subduction zones, subduction segments descending at normal angles have predominantly subhorizontal faults. Flat slab segments also have a dominant fault orientation, but those earthquakes slip along the conjugate nodal plane of the focal mechanism. In strongly curved slab segments, such as at the downdip edge of flat segments where the slab resubducts, earthquakes may slip along either nodal plane orientation. While both fault orientations could be consistent with the reactivation of fossil outer rise faults, the fault orientations are also consistent with expectations for newly created faults in agreement with the ambient stress field. Fault reactivation alone does not explain why different fault orientations are active in segments with different geometries, so the preferred explanation for having regionally consistent fault orientations is that they minimize the total work of the system. The previously observed predominance of subhorizontal faults appears to be a consequence of slab geometry.

Bearing fault diagnosis of wind turbine based on intrinsic time-scale decomposition frequency spectrum

In order to better identify the complex running conditions of wind turbine main bearings, we developed a bearing fault diagnosis method based on intrinsic time-scale decomposition frequency spectrum. The main bearing acceleration vibration signal from wind turbine is captured under four conditions—good bearing, outer race fault, inner race fault and roller fault. The proposed method consists of the following steps. First, the main bearing acceleration vibration signal is decomposed into several proper rotation components by using the intrinsic time-scale decomposition method. Second, the frequency spectrum of the first few proper rotation components (containing dominant fault features) is analyzed. The dominant resonant frequency range of each analyzed rotation component is derived, and then, the sum of frequency amplitude in said frequency range can be obtained. This sum is regarded as the fault feature vectors. Finally, the fault feature vectors are input to the least square support vectors machine, and the faults of wind turbine main bearing then can be diagnosed. The experiment results show that the proposed method can diagnose failures of wind turbine bearings quickly and more accurately.

Mineral assemblage anomalies in the slip zone of the 1999 Taiwan Chi-Chi earthquake: Ultrafine particles preserved only in the latest slip zone

We determined mineral assemblages of samples from the Taiwan Chelungpu fault and from milling and heating experiments by using X-ray diffraction and scanning and transmission electron microscopy. The fault system contains three dominant fault zones, the shallowest of which slipped during the 1999 Chi-Chi earthquake. The quartz and clay mineral contents of the primary slip zone were low, and it contained partly amorphous ultrafine particles (several tens of nanometers). Up to 30 weight percent of materials in that zone could not be fit to standard diffraction patterns, whereas nearly 100 weight percent of those in surrounding samples could be. The unfitted component could be attributed to the observed ultrafine particles produced by comminution during the earthquake, because weak diffraction intensities are caused from mineral lattice distortion, granulation, and amorphous coatings. Such particles are a potential proxy for identifying the slip zone of the most recent earthquake along a fault.

A Fault Diagnosis Method for Rotating Machinery Based on PCA and Morlet Kernel SVM

A novel method to solve the rotating machinery fault diagnosis problem is proposed, which is based on principal components analysis (PCA) to extract the characteristic features and the Morlet kernel support vector machine (MSVM) to achieve the fault classification. Firstly, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD) to obtain the corresponding intrinsic mode function (IMF). The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. So, the PCA is introduced to extract the characteristic features and reduce the dimension. The characteristic features are input into the MSVM to train and construct the running state identification model; the rotating machinery running state identification is realized. The running states of a bearing normal inner race and several inner races with different degree of fault were recognized; the results validate the effectiveness of the proposed algorithm.

A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

Gear Fault Diagnosis Method Using Ensemble Empirical Mode Decomposition Energy Distribution and Grey Similar Incidence

Focusing on some correspondence between energy distribution of different frequency bands and fault types when gear fault occurs, a new comprehensive fault diagnosis method is proposed based on ensemble empirical mode decomposition and grey similar incidence. The idea of circle statistics is introduced to improve the shortcoming of traditional morphological filter; and the rank-order morphological filter is defined. The line structure element is selected for rank-order morphological filter to denoise the original acceleration vibration signal. Denoised vibration signals are decomposed into a finite number of stationary intrinsic mode functions and some containing the most dominant fault information are calculated the energy distribution. Due to the grey similar incidence has good classify capacity for small sample pattern identification; these energy distributions could serve as the feature vectors, the grey incidence of different gear vibration signals is calculated to identify the fault pattern and condition. Practical results show that the proposed method can be used in gear fault diagnosis effectively.

The support vector machine parameter optimization method based on artificial chemical reaction optimization algorithm and its application to roller bearing fault diagnosis

The accuracy of a support vector machine (SVM) classifier is decided by the selection of optimal parameters for SVM. An artificial chemical reaction optimization algorithm (ACROA) is a new method to solve the global optimization problem and is adapted to optimize SVM parameters. In this paper, a SVM parameter optimization method based on ACROA (ACROA-SVM) is proposed. Furthermore, the ACROA-SVM is applied to diagnose roller bearing faults. Firstly, the original modulation roller bearing vibration signals are decomposed into product functions (PFs) by using the local mean decomposition (LMD) method. Secondly, the ratios of amplitudes at the different fault characteristic frequencies in the envelope spectra of some PFs that include dominant fault information are defined as the characteristic amplitude ratios. Finally, the characteristic amplitude ratios are used as input to the ACROA-SVM classifiers, and the fault patterns of the roller bearing are identified. The result shows that the combination of this ACROA-SVM classifiers and LMD method can effectively improve the accurate rate of fault diagnosis and reduce cost time.

Gear Incipient Diagnosing Based on EEMD and Genetic-Support Vector Machine

Due to the incipient fault attributes of gear are not obvious, So a hybrid diagnosis model to gear diagnosing based on Ensemble Empirical Mode Decomposition (EEMD) and Genetic-Support Vector Machine (GSVM) was proposed. With the method of EEMD,the gear vibration signals are adaptively decomposed into a finite number of Intrinsic Mode Function (IMF),which can alleviate model mixing that may appear in conventional EMD method. It calculates the energy character vectors of every IMF components,energy feature extracted from a number of IMFs that contained the most dominant fault information could serve as input genetic-vectors of support vector machine. The proposed model is applied to the gear testing system, and the results show that the diagnosis approach was effectively.

Application of EEMD Energy Distribution and Grey Incidence in Gearbox Fault Diagnosis

In this paper, a new comprehensive gearbox fault diagnosis method was proposed based on rank-order morphological filter, ensemble empirical mode decomposition (EEMD) and grey incidence. Firstly, the rank-order morphological filter was defined and the line structure element was selected for rank-order morphological filter to de-noise the original acceleration vibration signal. Secondly, de-noised gearbox vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMF) and some IMFs containing the most dominant fault information were calculated the energy distribution. Finally, due to the grey incidence has good classify capacity for small sample pattern identification; these energy distributions could serve as the feature vectors, the grey incidence of different gearbox vibration signals was calculated to identify the fault pattern and condition. Practical results show that the proposed method can be used in gear fault diagnosis effectively.

A New Rotor Fault Diagnosis Method Based on EEMD Sample Entropy and Grey Relation Degree

In this paper, a new rotor fault diagnosis method was proposed based on rank-order morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey relation degree. Firstly, the sampled data was de-noised by rank-order morphological filter. Secondly, the de-noised signal was decomposed into a finite number of stationary intrinsic mode functions (IMFs). Thirdly, some IMFs containing the most dominant fault information were calculated the sample entropy for four rotor conditions. Finally, the grey relation degree between the symptom set and standard fault set was calculated as the identification evidence for fault diagnosis. The practical results show that this method is quite effective in rotor fault diagnosis. Its suitable for on-line monitoring and diagnosis of rotating machinery.

Generalized reasoning about faults based on the diagnostic matrix

This paper introduces a set of comprehensive general reasoning rules about single faults based on a diagnostic matrix. The reasoning scheme unifies inference about faults based on a conventional binary diagnostic matrix, a two- and three-valued fault isolation system as well as on their fuzzy counterparts. There are introduced and defined notions of alternative and dominant fault signatures, fuzzy fault signatures as well as a matrix of alternative signatures. This matrix is supposed to be used instead of the classic diagnostic one. It is also shown that dominant fault signatures are transformable into alternative ones. Finally, three variants of concise general reasoning rules of faults are given. Three examples illustrate key point issues of the paper. The first example refers to a medical diagnostic case. It shows an instance of dominant fault signatures and, in fact, proposes a rational approach for planning diagnostic tests. The other examples describe the fuzzy reasoning approach employing a matrix of fuzzy alternative signatures applicable for use with multi-valued fuzzy diagnostic signals. Future works are outlined in the summary section.

GeoBioScience: Red Wood Ants as Bioindicators for Active Tectonic Fault Systems in the West Eifel (Germany).

Simple SummaryIn a 1.140 km² study area of the volcanic West Eifel, approx. 3,000 Red Wood Ant (RWA; Formica rufa-group) mounds had been identified and correlated with tectonically active gas-permeable faults, mostly strike-slip faults. Linear alignment of RWA mounds and soil gas anomalies distinctly indicate the course of these faults, while clusters of mounds indicate crosscut zones of fault systems, which can be correlated with voids caused by crustal block rotation. This demonstrates that RWA are bioindicators for identifying active fault systems and useful where information on the active regime is incomplete or the resolution by technical means is insufficient.In a 1.140 km² study area of the volcanic West Eifel, a comprehensive investigation established the correlation between red wood ant mound (RWA; Formica rufa-group) sites and active tectonic faults. The current stress field with a NW-SE-trending main stress direction opens pathways for geogenic gases and potential magmas following the same orientation. At the same time, Variscan and Mesozoic fault zones are reactivated. The results showed linear alignments and clusters of approx. 3,000 RWA mounds. While linear mound distribution correlate with strike-slip fault systems documented by quartz and ore veins and fault planes with slickensides, the clusters represent crosscut zones of dominant fault systems. Latter can be correlated with voids caused by crustal block rotation. Gas analyses from soil air, mineral springs and mofettes (CO2, Helium, Radon and H2S) reveal limiting concentrations for the spatial distribution of mounds and colonization. Striking is further the almost complete absence of RWA mounds in the core area of the Quaternary volcanic field. A possible cause can be found in occasionally occurring H2S in the fault systems, which is toxic at miniscule concentrations to the ants. Viewed overall, there is a strong relationship between RWA mounds and active tectonics in the West Eifel.

Accelerating incremental checkpointing for extreme-scale computing

Concern is beginning to grow in the high-performance computing (HPC) community regarding the reliability of future large-scale systems. Disk-based coordinated checkpoint/restart has been the dominant fault tolerance mechanism in HPC systems for the past 30 years. Checkpoint performance is so fundamental to scalability that nearly all capability applications have custom checkpoint strategies to minimize state and reduce checkpoint time. One well-known optimization to traditional checkpoint/restart is incremental checkpointing, which has a number of known limitations. To address these limitations, we describe libhashckpt, a hybrid incremental checkpointing solution that uses both page protection and hashing on GPUs to determine changes in application data with very low overhead. Using real capability workloads and a model outlining the viability and application efficiency increase of this technique, we show that hash-based incremental checkpointing can have significantly lower overheads and increased efficiency than traditional coordinated checkpointing approaches at the scales expected for future extreme-class systems.