Testing and Analysis of Induction Motor Electrical Faults Using Current Signature Analysis

The proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) to diagnosis the stator faults of Induction Motors. The performance of the proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) and the Zero-Sequence Voltage Component (ZSVC) to diagnose the stator faults of Induction Motors. The unalleviated study of the robustness of the industrial appliances is obligatory to verdict the fault of the machines at precipitate stages and thwart the machine from brutal damage. For all kinds of industry, a machine failure escorts to a diminution in production and cost increases. The Motor Current Signature Analysis (MCSA) is referred as the most predominant way to diagnose the faults of electrical machines. Since the detailed analysis of the current spectrum, the method will portray the typical fault state. This paper aims to present dissimilar stator faults which are classified under electrical faults using MCSA and the comparison of simulation and hardware results. The magnitude of these fault harmonics analyzes in detail by means of Finite-Element Method (FEM). The anticipated method can effectively perceive the trivial changes too during the operation of the motor and it shows in the results.

Health Monitoring of Induction Motor Using Electrical Signature Analysis

Induction motors have been widely used across industry,particularly with smaller loads and fixed speed services.Existing works focus on fault detection of induction motors without considering the shutdown time and production in industry.Therefore,this work aims to monitor the health conditions of the induction motor continuously through electrical signature analysis(ESA).The proposed technique is capable of predicting different kinds of faults,i.e.,rotor faults,stator phase imbalances,and supply cable faults at early stages.Moreover,ESA in real time is implemented.Thereafter,these current spectra were analyzed in frequency domain and compared with healthy current spectra.Performance evaluation is implemented by observing these spectra under different faulty conditions.A comparative study is made and analyzed through MATLAB simulations.

Seismic Signature Analysis for Clarification of Mud Volcanoes from the New Mud Diapirs Discovered at the NE-SW Moroccan Atlantic Margin

Our aim is to clarify mud volcanoes from the new mud diapirs resulting from the seismic attributes analysis applied to the low resolution Burmah oil “1973-1974” seismic data. The latter was carried out on the Larache and Tanger-Larache offshores from the NE-SW Atlantic margin. The high resolution seismic data was essential for this evaluation. In this case, we applied seismic signature analysis to four seismic profiles from the map of the seismic data set, which includes all new mud diapirs. This helped us to classify different types of mud diapirs within the seismic profiles. And as a result, six deep mud diapirs from the Prerifaine Nappe of Morocco, a shallow mud diapir, and four seafloor-piercing mud diapirs have been observed. Furthermore, the seafloor-piercing mud diapirs show a mushroom-shaped and conical-shaped cone. As they break through the seafloor, these kinds of cones characterize mud volcanoes. In this case, we may conclude that the resultant seafloor-piercing mud diapirs are likely to be mud volcanoes. However, more geological sampling and seafloor observation are still required.

Industrial Fault Signals Propagation and Current Signature Analysis

Motor current signature analysis provides good results in laboratory environment. In real life situation, electrical machines usually share voltage and current from common terminals and would easily influence each other. This will result in considerable amount of interferences among motors and doubt in identity of fault signals. Therefore, estimating the mutual influence of motors will help identifying the original signal from the environmental noise. This research aims at modelling the propagation of signals that are caused by faults of induction motors in power networks. Estimating the propagation pattern of fault signal leads to a method to discriminate and identify the original source of major events in industrial networks. Simulation results show that source of fault could be identified using this approach with a higher certainty than anticipated output coming of any individual diagnosis.

A Seismic Facies Analysis to Determine the Relative Age and History of the Al Idrissi Mud Volcano from Offshore Larache Located in the NW Moroccan Atlantic Margin

Formed on top of the Gulf of Cadiz, the Al Idrissi mud volcano is the shallowest and largest mud volcano in the El Arraiche mud volcano field of the northwestern Moroccan margin. The development and morphology of mud volcanoes from the El Arraiche mud volcanoes group have been studied at a large scale. However, the time interval related to their formation period still needs to be better understood. In this regard, we interpreted and analyzed the seismic facies from the 2D reflection data of the GEOMARGEN-1 campaign, which took place in 2011. The aim was to identify the seismic sequences and draw the Al Idrissi mud volcano system to determine the formation period of the Al Idriss mud volcano. And as a result, the Al Idrissi mud volcano system is made of both buried and superficial bicone and was identified along with the Upper Tortonian to Messinian-Upper Pliocene facies. As the initial mud volcano extrusive edifice, the buried bicone was formed in the Late-Messinian to Early-Pliocene period. However, the superficial bicone, as the final extrusive edifice, was included in the Late Pliocene. In this case, the timing interval between the buried and superficial bicone is equivalent to the Late-Messinian to Upper-Pliocene period. Therefore, the latter corresponds to the Al Idrissi mud volcano formation period.

Whole-genome resequencing reveals molecular imprints of anthropogenic and natural selection in wild and domesticated sheep

The abundance of domesticated sheep varieties and phenotypes is largely the result of long-term natural and artificial selection. However, there is limited information regarding the genetic mechanisms underlying phenotypic variation induced by the domestication and improvement of sheep. In this study, to explore genomic diversity and selective regions at the genome level, we sequenced the genomes of 100 sheep across 10 breeds and combined these results with publicly available genomic data from 225 individuals, including improved breeds, Chinese indigenous breeds,African indigenous breeds, and their Asian mouflon ancestor. Based on population structure, the domesticated sheep formed a monophyletic group,while the Chinese indigenous sheep showed a clear geographical distribution trend. Comparative genomic analysis of domestication identified several selective signatures, including IFI44 and IFI44L genes and PANK2 and RNF24 genes, associated with immune response and visual function.Population genomic analysis of improvement demonstrated that candidate genes of selected regions were mainly associated with pigmentation,energy metabolism, and growth development.Furthermore, the IFI44 and IFI44L genes showed a common selection signature in the genomes of 30domesticated sheep breeds. The IFI44 c. 54413058C>G mutation was selected for genotyping and population genetic validation. Results showed that the IFI44 polymorphism was significantly associated with partial immune traits. Our findings identified the population genetic basis of domesticated sheep at the whole-genome level, providing theoretical insights into the molecular mechanism underlying breed characteristics and phenotypic changes during sheep domestication and improvement.

Influence of Induction Motor Design Data on Effects of Cage Asymmetry

The purpose of this paper is to analyze influence of design data on a component （1 - 2s）~ in stator currents of induction motors, mainly used for cage fault diagnosis. This paper shows that such an approach does not always lead to a correct outcome. The considerations are based on a ＂classical＂ model of induction motors extended to cage asymmetry by introducing cage asymmetry factors ko~ and ka. It has been found that in order to estimate the level of the component （1 - 2s）7~, it is enough to know the pole-pair number ＂p＂ and the number of rotor slots ＂N＂. The main objective of the paper is to provide engineers with simple qualitative prediction of effects due to cage faults for various motors when information on design data is very limited.