Wavelets applied to early fault analysis of hoist gear box

Using wavelets, the vibration signal of a certain mine hoist gear box was analyzed. By multiple comparison analysis, the rational wavelet basis function was determined. Fault characteristic frequencies of hoist gear box were identified. The research indicates that the hoist＇s fault information is non-stationary, and non-stationary signal is clearly extracted by using db20 wavelet as basis function. The db20 wavelet is the proper wavelet base for vibration signal analysis of the hoist gear box.

Weak characteristic information extraction from early fault of wind turbine generator gearboxKeywords wind turbine generator gearbox, B-singular value decomposition, local mean decomposition, weak characteristic information extraction, early fault warning

Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition （SVD）-based denoising may result in loss of useful information. A weak characteristic information extraction based on μ-SVD and local mean decomposition （LMD） is developed to address this problem. The basic principle of the method is as follows： Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and μ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.

Early Sensor Fault Detection Based on PCA and Clustering Analysis

This paper proposes a novel scoring index for the early sensor fault detection in order to make full use of massive archived spacecraft telemetry data.The early detection of sensor faults is made by using the index constructed by the K-means algorithm and PCA model.The sensor fault detection includes the learning phase and monitoring phase.The amplitude of sensor fault has been always increasing when the performance of sensors deteriorates during a period.The proposed index can detect the smaller sensor faults than the squared prediction error( SPE) index which means it can discover the sensor faults earlier than the later.The simulation results demonstrate the effectiveness and feasibility of the proposed index which can decrease the check-limit as much as 40% than SPE in the same magnitude of bias sensor fault.

Discovery of the Early Devonian Sinistral Shear in the Jiangshan-Shaoxing Fault Zone and its Tectonic Significance

The Jiangshan-Shaoxing fault zone （JSFZ） was formed by the amalgamation of the Yangtze and Cathaysia blocks in the Neoproterozoic.Since the Paleozoic,the JSFZ has experienced three episodes of tectonic activities：the Early Paleozoic ductile strike-slip shear,Early Mesozoic thrust,and the Late Mesozoic extension.

Online fault recognition of electric power cable in coal mine based on the minimum risk neural network

Firstly,the concepts of the traveling wave entropy and the feature function of traveling wave entropy were defined.Then the statistic characters of the traveling wave entropy feature function,mean value and variance were analyzed after the zero-order component of the traveling wave of online cable was selected to serve as the observed object.Finally,the new recognition algorithm of minimum risk neural network was pre- sented.The simulation experiments show that the recognitions of the early fault states can be completed correctly by using the proposed recognition algorithm.The classes of cable faults include in 1-phase ground faults,and the 2-phase short circuit faults or ground faults and the 3-phase short circuit faults or ground faults,open circuit.The fault resistance range is 1×10^(-1)～1×10~9Ω.

DETECTION OF EARLY RUB-IMPACT IN ROTORS VIA HIGHER-ORDER TIME-FREQUENCY ENTROPY

Due to the calculation problem of classical methods (such as Lyapunovexponent) for chaotic behavior, a new method of identifying nonlinear dynamics with higher-ordertime-frequency entropy (HOTFE) based on time-frequency analysis and information theorem is proposed.Firstly, the meaning of HOTFE is defined, and then its validity is testified by numericalsimulation. In the end vibration data from rotors are analyzed by HOTFE. The results demonstratethat it can indeed identify the early rub-impact chaotic behavior in rotors and also is simpler tocalculate than previous methods.

Tectono-paleogeographic study of the Early Cretaceous in the Songliao Basin

Based on first-hand material from the geological exploration of petroleum,we made a detailed study of the tectonic development in the Early Cretaceous and the distribution of basement rifts in the Songliao Basin.The sedimentary characteristics of this epoch and the tectono-paleogeography of the basin were expounded.The results show that in its early stages,the Songliao Basin was characterized by a detached faulted basin in which mainly lake facies developed among mountains.It became gradually one lake during the late stages of the Early Cretaceous.During this period,the fault activity in the Songliao Basin changed from a turbulent to a quiet development,the water area from small separated lakes to one large lake,in which the sedimentary facies were divided into asymmetric eastern and western parts.In the basin a volcanic clastic rock-alluvial fan system developed and a fan delta-lake-small delta-river system was mainly deposired.Our research also shows that the basement rifts not only controlled the distribution of fault depressions and the tectonic development in the Early Cretaceous,but had also an effect on the orientation of sedimentation,source area and river system,which determine the tectonopaleogeography of the Early Cretaceous.

A prediction method of operation trend for large axial-flow fan based on vibration-electric information fusion

As the critical equipment,large axial-flow fan(LAF)is used widely in highway tunnels for ventilating.Note that any malfunction of LAF can cause severe consequences for traffic.Specifically,fault deterioration is suppressed tremendously when an abnormal state is detected in the stage of early fault.Thus,the monitoring of the early fault characteristics is very difficult because of the low signal amplitude and system disturbance(or noise).In order to overcome this problem,a novel early fault judgment method to predict the operation trend is proposed in this paper.The vibration-electric information fusion,the support vector machine(SVM)with particle swarm optimization(PSO),and the cross-validation(CV)for predicting LAF operation states are proposed and discussed.Finally,the results of the experimental study verify that the performance of the proposed method is superior to that of the contrast models.

NEW METHOD OF EXTRACTING WEAK FAILURE INFORMATION IN GEARBOX BY COMPLEX WAVELET DENOISING

Because the extract of the weak failure information is always the difficulty and focus of fault detection. Aiming for specific statistical properties of complex wavelet coefficients of gearbox vibration signals, a new signal-denoising method which uses local adaptive algorithm based on dual-tree complex wavelet transform （DT-CWT） is introduced to extract weak failure information in gear, especially to extract impulse components. By taking into account the non-Gaussian probability distribution and the statistical dependencies among wavelet coefficients of some signals, and by taking the advantage of near shift-invariance of DT-CWT, the higher signal-to-noise ratio （SNR） than common wavelet denoising methods can be obtained. Experiments of extracting periodic impulses in gearbox vibration signals indicate that the method can extract incipient fault feature and hidden information from heavy noise, and it has an excellent effect on identifying weak feature signals in gearbox vibration signals.

Seismites in the Dasheng Group： New evidence of strong tectonic and earthquake activities of the Tanlu Fault Zone

More than 80 layers of seismites were recognized from the Early Cretaceous Dasheng Group in the Mazhan and Tancheng graben basins in the Tanlu Fault Zone, eastern China. The responsible seismic events took place about 110–100 Ma in the Early Cretaceous. The fault zone was affected at the time by strong tectonics, due to tension-related stretching and scattered squeezing by strike-slip faults. These tectonic activities induced a series of strong earthquakes with Richter magnitudes(M) of 5–8.5. The earthquakes affected saturated or semi-consolidated flood and lake sediments, and produced intra-layer deformations by several processes, including liquefaction, thixotropy, drop, faulting, cracking, filling and folding, which resulted in the formation of various soft-sediment deformation structures, such as dikes and veins of liquefied sand, liquefied breccias, liquefied homogeneous layers, load structures, flame structures, ball-and-pillow structures, boudinage, diapirs, fissure infillings, a giant conglomerate wedge, and syn-sedimentary faults. The seismites are new evidence of tectonic and seismic activities in the Tanlu Fault Zone during the Early Cretaceous; the series of strong seismic events that can be deduced from them must be considered as a response to the destruction of the North China Craton.

Deep anomaly detection in horizontal axis wind turbines using GraphConvolutional Autoencoders for Multivariate Time series

Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonizationprocess. However, wind turbines are subjected to a wide range of dynamic loads which can cause more frequentfailures and downtime periods, leading to ever-increasing attention to effective Condition Monitoring strategies.In this paper, we propose a novel unsupervised deep anomaly detection framework to detect anomalies in windturbines based on SCADA data. We introduce a promising neural architecture, namely a Graph ConvolutionalAutoencoder for Multivariate Time series, to model the sensor network as a dynamical functional graph. Thisstructure improves the unsupervised learning capabilities of Autoencoders by considering individual sensormeasurements together with the nonlinear correlations existing among signals. On this basis, we developeda deep anomaly detection framework that was validated on 12 failure events occurred during 20 months ofoperation of four wind turbines. The results show that the proposed framework successfully detects anomaliesand anticipates SCADA alarms by outperforming other two recent neural approaches.

Super-harmonic responses analysis for a cracked rotor system considering inertial excitation

In this paper, an investigation on the nonlinear vibration, especially on the super-harmonic resonances, in a cracked rotor system is carried out to provide a novel idea for the detection of crack faults in rotor systems. The motion equations of the system are formulated with the consideration of the additional excitation from an inertial environment as well as the forced excitation of the rotor unbalance. By using the harmonic balance method, the analytical solutions of the equations with four orders of harmonic exponents are obtained to analyze the nonlinear response of the system. Then through numerical calculations, the vibration responses affected by system parameters including the inertial excitation, the forced excitation, the crack and damping factors are investigated in detail. The results show that the occurrence of the super-harmonic resonances of the rotor system is due to the interaction between crack breathing and the inertial excitation. Correspondingly, the super-harmonic responses are significantly affected by the inertial excitation and the crack stiffness(or depth). The rotor unbalance, however, does not make apparent effects on the super-harmonic responses. Consequently, the super-harmonic resonances peaks can be viewed as an identification signal of the crack fault due to the application of the inertial excitation. By utilizing the inertial excitation, the super-harmonic response signals in rotor systems with early crack faults can be amplified and detected more easily.

Structure, evolution and regional tectonic implications of the Queshan metamorphic core complex in eastern Jiaodong Peninsula of China

The Queshan MCC is an important example of a crustal extensional structure in the eastern Jiaodong Peninsula along the southeastern margin of the NCC in the Early Cretaceous. The MCC is a typical Cordilleran-type core complex with a three-layered structure:(1) the upper plate is constituted by the Cretaceous supradetachment basin and Paleoproterozoic basement;(2) the lower plate comprises the Neoarchean high-grade metamorphic complexes and late Mesozoic granitic intrusions; and(3) the two plates are separated by a master detachment fault. A series of late NEN-oriented brittle faults superimposed on and destructed the early MCC. Petrology, geometry, kinematics, macro- and micro-structures and quartz c-axis fabrics imply that the MCC has a progressive exhumation history from middle-lower to subsurface level(via middle-upper crustal level) under the nearly WNW-ESE regional extensional regime. We present structural and geochronological evidence to constrain the exhumation of the Queshan MCC from ca. 135 to 113 Ma. Based on the comprehensive analysis of the different patterns of extensional structures in the Jiaodong and Liaodong Peninsula, we have defined the Jiao-Liao Early Cretaceou extensional province and further divided the crustal extension of it into two stages: the first stage was the intense flow of the middle-lower crust and the second stage was the extension of the middle-upper crust. Combining the tectonic setting, the lithosphere thinning in the Jiao-Liao Early Cretaceous extensional province can be considered a typical model for the response of crust-mantle detachment faulting under regional extension in East Asia.