A Stator Fault Diagnosis Method Based on the Offline Motor Parameter Measurement for PMSM

The permanent magnet synchronous motor (PMSM) is used widely in electric vehicle application due to its high-power density and efficiency. Stator fault is a frequently fault in the motor as it usually works in a harsh environment. Therefore, a stator fault diagnosis method based on the offline motor parameter measurement is proposed to detect and evaluate the stator fault in this paper. Firstly, the line-to-line resistance and inductance of a healthy motor are analyzed when a DC voltage and a high-frequency voltage are excited to the motor respectively, where the DC and AC equivalent circuits at a standstill are introduced. Then, to analyze the resistance and inductance of the stator fault, an extra branch is added to the fault part to obtain the fault equivalent circuits. Accordingly, the stator fault resistance and inductance are derived, and then the resistance and inductance differences between healthy and fault motors are analyzed to provide the basis for the stator fault detection. Furthermore, the fault indicators are defined based on the resistance and inductance differences when a motor has a stator fault. Hence the stator fault severity and location can be evaluated by using these fault indicators. Finally, the experimental results from a 400 W permanent magnet synchronous motor are demonstrated to validate the proposed method.

Improving postoperative radiographs for the parameter measurement of hexapod external fixator using an additional foot ring

Background It is challenging to determine the orthogonality of radiographs in daily clinical practice. The purpose of this study was to show the usefulness of an additional foot ring which might determine the orthogonality of postoperative radiographs for the parameter measurement of hexapod external fixator. Methods We retrospectively analyzed 81 consecutive trauma patients with tibial shaft fractures treated by the hexapod external fixator at our institution from September 2014 to July 2019. Starting in March 2016, the postoperative radiographs for parameter measurement were obtained under the control of an additional foot ring. The final data consisted of 47 patients in traditional radiographs (Group I) and 34 patients under the control of foot ring during the radiographic process (Group II). The demographic data, original postoperative deformities, residual deformities after final correction, number of repeated radiographs after the first postoperative radiographs, time to the satisfactory reduction achieved, and external fixation time in all patients were documented and analyzed. The Johner–Wruhs criteria were used for the final clinical outcomes evaluation at the last clinical visit. Results Satisfactory reduction and bone union were achieved in all patients. There were no statistical significances between the two groups in the demographic data, original postoperative deformities, residual deformities after final correction, external fixation time, and the final clinical outcomes (P > 0.05). The mean number of repeated radiographs after the first radiographs (1.4 times) and mean time to the satisfactory reduction achieved (3.3 days) in patients with an additional foot ring used were all less than those without foot ring (2.4 times, 5.3 days) (P < 0.05). Conclusions The additional foot ring is a practical device to ensure the orthogonality of postoperative radiographs for the hexapod external fixator parameter measurement. Radiation exposure, duration of deformity correction, and cost for patients might be reduced due to the less repeated radiographs with the wrong position.

A Construction Method of Intelligent Manufacturing System under Industry 4.0 Model

Starting from the current problems facing Industry 4.0, this article analyzes the changes in the macro and industrial environment that Industry 4.0 faces and explains the problems, opportunities, and strategies for the manufacturing industry in the external environment. First, the reference system of the intelligent manufacturing system, the current status, and the existing problems of industrial production management are analyzed through the investigation of the status quo of industrial production and management. This puts forward the detailed requirements of the industrial intelligent manufacturing system in the data acquisition layer, data storage layer, and analysis and decision support layer and then designs the hierarchical structure of the industrial intelligent manufacturing system. Subsequently, it adopts design methods and lists product manufacturing costs, pointing out that Industry 4.0 requires industrial transformation, and finally proposes the strategic direction of smart manufacturing in combination with the Industry 4.0 network strategy. At the same time, in view of the problems of long parameter measurement time and untimely system feedback in the existing koji-making process, an online parameter measurement method based on network optimization is proposed. On the basis of the neural network, an industrial neural network with double hidden layers and self-feedback of the output layer is proposed. Through algorithm comparison experiments, the proposed parameter prediction model based on industrial neural network has better prediction results and higher accuracy. Finally, a comparison of cost, quality, delivery time, etc., before and after the implementation of Industry 4.0 intelligent manufacturing is carried out. An intelligent solution is proposed, the implementation goal is formulated, and the implementation is gradually implemented in stages, and finally an intelligent upgrade and transformation are realized. It is shown in many aspects that intelligent manufacturing provides a powerful means for enterprises to achieve agility, virtualization, lean, integration, and collaboration, and it can bring efficiency, reliability, and safety to the manufacturing process of enterprises.

Neutrino mass ordering determination through combined analysis with JUNO and KM3NeT/ORCA

The neutrino mass ordering (NMO) is one of the fundamental questions in neutrino physics. KM3NeT/ORCA and JUNO are two neutrino oscillation experiments both aiming at measuring the NMO with different approaches: ORCA with atmospheric neutrinos traversing the Earth and JUNO with reactor neutrinos. This contribution presents the potential of determining the NMO through a combined analysis of JUNO and ORCA data. In a joint fit, the NMO sensitivity is enhanced beyond the simple sum of the sensitivities of each experiment due to the tension between the respective Δm 31 2 best fit values obtained when the wrong ordering is assumed, together with good constraints on this parameter measurement by both experiments. From this analysis, we expect the true NMO to be determined with 5σ significance after 1–2 years of data taking by both experiments for the current global best-fit values of the oscillation parameters, while maximally 6 years will be needed for any other parameter set.

Parameter Measurement of Live Animals Based on the Mirror of Multiview Point Cloud

Scale and standardization are essential to the prosperity of the breeding industry. During large-scale, standardized breeding, the selective breeding of good livestock breeds hinges on the accurate measurement of body parameters for live animals. However, the complex shooting environment brings several urgent problems, such as the missing of many local data in the point cloud and the difficulty in the automatic acquisition of body data. To solve these problems, this paper proposes a method for parameter measurement of live animals based on the mirror of multiview point cloud. Firstly, the acquisition and stitching principles were given for the multiview point cloud data on body parameters of live animals. Next, the authors presented a way to make up for the data missing areas in the point cloud. Finally, this paper acquires the body mirror data of live animals and scientifically calculates the body parameters. The proposed measurement method was proved effective through experiments.

Kinematic Analysis and Parameter Measurement for Multi-Axis Laser Engraving Machine Tools

Multi-axis Laser Engraving Machine Tools (LEMT) are widely used in precision processing of parts with complex surface. The accuracy of kinematic model and parameter measurement are the key factors determining the processing quality of LEMT. In this paper, a kinematic model of multi-axis LEMT was established based on Homogeneous Transformation Matrix (HTM). Two types of unknown parameters, linkage parameters and positioning parameters, were measured in the presented model. Taking advantage of the characteristics of laser processing, this paper proposed a rapid measurement method of linkage parameters by combining the machine tool motion with the laser marking action. For positioning parameters, this study proposed a non-contact measurement method based on structured light scanner, which can obtain the translation values and the rotation values from the Workpiece Coordinate System (WCS) to the Basic Coordinate System (BCS) simultaneously. After the measurement of two kinds of parameters of a multi-axis LEMT was completed, the processing of a spatial curve was performed and the average contour error was controlled at 15.1 µm, which is sufficient to meet the project requirements.