Qiang Liu

Bio: Qiang Liu is an academic researcher from China University of Mining and Technology. The author has contributed to research in topics: Fault (power engineering) & Roadheader. The author has an hindex of 2, co-authored 3 publications receiving 8 citations. Previous affiliations of Qiang Liu include Xuzhou Institute of Technology.

Magnetic properties of ferromagnetic steels under different stress by the alternating current magnetization

Abstract: The alternating current magnetic characteristics of the ferromagnetic materials act a significant factor to reflect their stress states. To reveal the relationship between the stress and the magnetic properties of the ferrimagnetic steels, we deduce the relation of alternating current permeability and the detected voltage of the nine-feet probe by using the basic laws in magnetism and law of electromagnetic reaction, and then we use the nine-feet probe to design and build the electromagnetic measurement system. Next some corresponding experiments were carried out to test the induction voltage of the two kinds Q-steel samples under the different stress. The results were shown that there was a similar variation tendency of the detected voltage in the two kinds the Q-steel samples, the max different values of the detected voltage can be reach 150 mV in the Q215 sample and 280 mV in the Q275 sample, respectively. Therefore, with the help of the difference detected voltage we can check the stress state of the ferrimagnetic steels conveniently in the engineering.

Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader

Abstract: Incipient fault detection and identification (IFDI) of cutting arms is a crucial guarantee for the smooth operation of a roadheader. However, the shortage of fault samples restricts the application of the fault diagnosis technique, and the data analysis tools should be optimized efficiently. In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. The commonly measured current and vibration data cutting arms are used in the IFDI. The experimental results show that the support vector machines based on dynamic cuckoo outperform the other methods. Besides, the performance of the four methods under different operating conditions is compared. The fault cause of cutting arms of the roadheader is analyzed and the design improvement scheme for cutting arms is provided. This study provides a reference for improving the fault diagnosis of the roadheader.

Method for reducing dimension by velocity inversion of two-dimensional mine earthquake wave in coal mine

Abstract: The invention discloses a method for reducing a dimension by velocity inversion of a two-dimensional mine earthquake wave in a coal mine. The method comprises the following steps of: dividing a targetarea equally into multiple square grids; obtaining a wave velocity upper limit vector and a wave velocity lower limit vector of each square grid; obtaining the retained variable to be inverted and avariable range; reducing a spatial dimension of an inversion model; and finding an optimal solution in a model space after reducing the dimension. The technical solution provided by the invention cansolve a dependence problem of an initial value in an inversion process, thereby reducing the possibility that an inversion result converges to a wrong position. Therefore, the technical solution provided by the invention finds the optimal solution in the model space after reducing the dimension, thereby reducing the size of the model space for inversion and optimization, and improving the reliability of an inversion result and the speed of the inversion calculation.

SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets

Abstract: Predictive maintenance has lately proved to be a useful tool for optimizing costs, performance and systems availability. Furthermore, the greater and more complex the system, the higher the benefit but also the less applied: Architectural, computational and complexity limitations have historically ballasted the adoption of predictive maintenance on the biggest systems. This has been especially true in military systems where the security and criticality of the operations do not accept uncertainty. This paper describes the work conducted in addressing these challenges, aiming to evaluate its applicability in a real scenario: It presents a specific design and development for an actual big and diverse ecosystem of equipment, proposing an semi-unsupervised predictive maintenance system. In addition, it depicts the solution deployment, test and technological adoption of real-world military operative environments and validates the applicability.

AC and DC magnetizing for magnetic flux leakage testing

Abstract: Rules for selecting the magnetizing frequency for magnetic flux leakage testing (MFL) are discussed from MFL testing theory using probability theory. The frequency of the carrier wave should be much higher than that of the modulated wave. Experimental data collected from a 0.2 mm wide by 0.2 mm deep default verified the analysis. The data also showed that the magnetizing frequency should be higher than 1 kHz for typical inspecting conditions and that the 50 Hz AC magnetizing method was unfit for MFL.

Review on the Steel Magnetic Permeability Detection Method

Abstract: Detecting the magnetic permeability can obtain test information of steel stress,the fatigue damage,physical and chemical damage,etc.This paper makes a summary for the existing steel magnetic permeability detection method,providing a reference for steel permeability test.

Influence of the Condition of Magnetism Stress Measurement on the Stress Sensitivity

Abstract: Magnetism stress measurement method has the merits of high measuring speed,deep detecting depth(up to several millimeters),no radiation and being easy to take,etc.It can also measure the component real-timely on the spot and safely,even when the component is at work.But this method can only be used to measure the ferromagnetic materials and is also sensitive to some factors like the ferromagnetic materials' structure.Therefore,there are many causes that influence the measuring precision in magnetism stress measurement, and the most important parameter in these causes is stress sensitivity.This paper mainly investigated the relationship between the stress sensitivity and the measuring condition of two materials,and the influence of measuring condition on the stress sensitivity,so that the bad conditions could be eliminated,the stress sensitivity could be identified correctly and the accuracy of magnetism stress measurement technology could be improved in the course of the magnetism measurement.