Research on Remote Monitoring and Fault Diagnosis Technology of Numerical Control Machine

Based on the internet technology,it has become possible to complete remote monitoring and fault diagnosis for the numerical control machine.In order to capture the micro-shock signal induced by the incipient fault on the rotating parts,the reso- nance demodulation technology is utilized in the system.As a subsystem of the remote monitoring system,the embedded data acquisi- tion instrument not only integrates the demodulation board but also complete the collection and preprocess of monitoring data from different machines.Furthermore,through connecting to the internet,the data can be transferred to the remote diagnosis center and data reading and writing function can be finished in the database.At the same time,the problem of the IP address floating in the dial-up of web server is solved by the dynamic DNS technology.Finally,the remote diagnosis software developed on the Lab VIEW platform can analyze the monitoring data from manufacturing field.The research results have indicated that the equipment status can be monitored by the system effectively.

Real-Time Data and Visualization Monitoring of Computer Numerical Control Machine Tools Based on Hyper Text Markup Language 5

In order to ensure the safety,quality and efficiency of computer numerical control(CNC)machine tool processing,a real-time monitoring and visible solution for CNC machine tools based on hyper text markup language(HTML)5 is proposed.The characteristics of the real-time monitoring technology of CNC machine tools under the traditional Client/Server(C/S)structure are compared and analyzed,and the technical drawbacks are proposed.Web real-time communication technology and browser drawing technology are deeply studied.A real-time monitoring and visible system for CNC machine tool data is developed based on Metro platform,combining WebSocket real-time communication technology and Canvas drawing technology.The system architecture is given,and the functions and implementation methods of the system are described in detail.The practical application results show that the WebSocket real-time communication technology can effectively reduce the bandwidth and network delay and save server resources.The numerical control machine data monitoring system can intuitively reflect the machine data,and the visible effect is good.It realizes timely monitoring of equipment alarms and prompts maintenance and management personnel.

Global Control Simulation of Electric Vehicle Based on Finite State Machine Theory

Finite state machine theory (FSM) is introduced and applied to global control of electric vehicle. Theoretical adaptation for application of FSM in control of electric vehicle is analyzed. Global control logic for parts of electric vehicle is analyzed and built based on FSM. Using Matlab/Simulink, BJD6100-HEV global control algorithm is modeled and prove validity by simulation.

Dynamic Control System of Automatic Monitoring Equipment for Waste Gas Pollution Source

In order to improve the quality of automatic monitoring data of pollution sources and apply the automatic monitoring data to verify the environmental tax,Shandong Province took the lead in adopting the Internet of Things technology and drawing on the successful experience of air automatic monitoring stations and surface water automatic monitoring stations in management,and developed a dynamic management and control system for automatic monitoring equipment of pollution sources to improve and strengthen the quality audit of automatic monitoring data,further improve the quality of automatic monitoring data and better provide a basis for environmental management and decision making.The system realizes the simultaneous monitoring of monitoring data,running state and parameters of the automatic monitoring equipment,eliminates the phenomenon of falsification by modifying equipment parameters,and judges the validity of the collected data by acquiring the working state of the equipment remotely and randomly.After the actual operation test of the Department of Ecological Environment of Shandong Province,the system is proved to have the characteristics of practicality,real time and high efficiency,and be able to make up for low frequency and narrow coverage of manual inspection,with good application prospect in the field of environment and pollution source monitoring.

Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing assets.This article builds upon the Industry 4.0 concept to improve the efficiency of manufacturing systems.The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding(FSW)process.It consists of a CNC manufacturing machine,sensors,edge,cloud systems,and deep neural networks,all working cohesively in real time.The edge device,located near the FSW machine,consists of a neural network that receives sensory information and predicts weld quality in real time.It addresses time-critical manufacturing decisions.Cloud receives the sensory data if weld quality is poor,and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine.Several experiments are conducted for training the neural networks.The framework successfully tracks process quality and improves the welding by controlling it in real time.The system enables faster monitoring and control achieved in less than 1 s.The framework is validated through several experiments.

Formal Reduction of Interfaces to Large-scale Process Control Systems

A formal methodology is proposed to reduce the amount of information displayed to remote human operators at interfaces to large-scale process control plants of a certain type. The reduction proceeds in two stages. In the first stage, minimal reduced subsets of components, which give full information about the state of the whole system, are generated by determining functional dependencies between components. This is achieved by using a temporal logic proof obligation to check whether the state of all components can be inferred from the state of components in a subset in specified situations that the human operator needs to detect, with respect to a finite state machine model of the system and other human operator behavior. Generation of reduced subsets is automated with the help of a temporal logic model checker. The second stage determines the interconnections between components to be displayed in the reduced system so that the natural overall graphical structure of the system is maintained. A formal definition of an aesthetic for the required subgraph of a graph representation of the full system, containing the reduced subset of components, is given for this purpose. The methodology is demonstrated by a case study.

Fuzzy reasoning model using fuzzy Petri Nets for the monitoring of robotic assembly

This paper presented a fuzzy Petri net model to deal with the monitoring of robotic assembly. Based on the fuzzy Petri net model, an efficient composite reasoning mode was proposed to perform fuzzy reasoning automatiealy. It can determine whether there exists an antecedent-consequence relationship between two contact states. Furthermore, various types of sensor signals can be converted to the same form of real values between zero and one, and the contradiction among large number, high degree of truth and importance of input conditions can be resolved very well by introducing the weight factors and priorities for sensor signals. Finally, a peg- in-the-hole example was given to illustrate the reasonability and feasibility of the proposed model.

A Rapid and Simple Quantitative Method for the Active Ingredients of Aescin in the Extraction Process Using Near Infrared Spectroscopy

To achieve a rapid and simple detection for the active ingredients of Aescin in the extraction process using near-infrared spectroscopy (NIR) and to realize the state monitoring and quality control of the extraction process. Partial least square regression (PLS) was applied to build the near-infrared calibration models, and the applicability of the model was investigated by predicting the unknown samples in the extraction process. The correlation coefficients of the established Aescin models (A, B, C, D) were 0.9836, 0.9831, 0.9833, 0.9824, and the prediction standard deviations (SEP) were 0.05636, 0.05043, 0.02412, 0.05636, respectively. This study suggests that the proposed model has superior stability and accuracy. NIR spectroscopy technique provides a novel efficient and environmentally friendly approach to the rapid determination of four Aescin key quality indicators (A, B, C, D) in the extraction, which was solved the problem that the lack of state monitoring during the extraction of Aescin, thereby improved the quality of Aescin.

Development of a Solar Controller with MLI Control

This work presents the development of a solar regulator which manages the charge and discharge of a (lead) battery installed in a photovoltaic system in order to extend its lifetime. The regulator is controlled by a microcontroller (PIC16F877A) and protects the battery against overcharging, deep discharge, but also against temperature drifts. The operating principle is based on the control of a DC-DC converter by a rectangular signal MLI generated by the microcontroller. In addition to the protection function of the regulator, there is included a control and monitoring panel consisting of a visualization interface on which the system quantities can be observed. Thus, it will be given to the user to be able to act on the system. This display interface uses as a display an LCD screen and LEDs. Simulation results are presented to illustrate the operation of the proposed solar controller.

Active Fault Tolerant Nonsingular Terminal Sliding Mode Control for Electromechanical System Based on Support Vector Machine

Effective fault diagnosis and fault-tolerant control method for aeronautics electromechanical actuator is concerned in this paper.By borrowing the advantages of model-driven and data-driven methods,a fault tolerant nonsingular terminal sliding mode control method based on support vector machine(SVM)is proposed.A SVM is designed to estimate the fault by off-line learning from small sample data with solving convex quadratic programming method and is introduced into a high-gain observer,so as to improve the state estimation and fault detection accuracy when the fault occurs.The state estimation value of the observer is used for state reconfiguration.A novel nonsingular terminal sliding mode surface is designed,and Lyapunov theorem is used to derive a parameter adaptation law and a control law.It is guaranteed that the proposed controller can achieve asymptotical stability which is superior to many advanced fault-tolerant controllers.In addition,the parameter estimation also can help to diagnose the system faults because the faults can be reflected by the parameters variation.Extensive comparative simulation and experimental results illustrate the effectiveness and advancement of the proposed controller compared with several other main-stream controllers.

动态控制元结构的建模及其在StateFlow环境下的仿真

针对我们提出的动态控制元结构新理念,进行层次组合和并列结构扩展,并论证扩展后的系统的控制特性,从而使得所构建的整个系统能同时满足功能需求和控制需求。在上述设计思想基础上,以瓷砖分布式制造作为系统原型,选取设备单元为动态控制元结构,构建了整个系统,并用Matlab/stateflow对整个系统构建和运行全过程进行仿真,仿真结果验证了该理念的正确性和可行性。

基于FPGA的NAND Flash控制器设计

NAND Flash速度快、密度大、成本低,在各种数码产品中广泛使用。在详细介绍NAND Flash基础上,提出了基于AHB总线的NAND Flash控制器设计方案,并且已通过了功能验证和FPGA验证,各条指令功能均达到了预期的设计要求。

基于FPGA的NAND控制器设计与实现

在基于NAND型固态存储系统的开发中,接口芯片直接影响储存系统的性能,为了最大限度地提高读写性能,需对NAND控制器进行自主设计。通过分析NAND Flash的接口特性和NAND控制器的组成结构,采用了状态机对NAND控制器的主逻辑以及常用编程命令进行了描述,同时运用FPGA对该状态机逻辑进行实现,并对NAND主要操作命令进行了仿真试验。试验结果表明,该设计符合NAND Flash的操作时序要求。

基于NiosⅡ的NAND Flash控制器IP设计

文章介绍一种基于NiosⅡ处理器的NAND Flash控制器的设计思路和设计方法。着重介绍NAND Flash控制器的状态机和驱动程序的设计。该NAND Flash控制器IP已在集成测试仪器的SOPC系统中稳定应用。

基于Android平台的折弯机远程监控系统

为了促进数控系统发展,方便系统维护与监测,开发了一种基于Android平台的折弯机远程监控系统,实现了对折弯机系统运行数据的远程采集和数据存储功能。该系统不受地点时间限制,用户可在任何具备网络覆盖的地方从手机上浏览并获取数据,并根据数据自动生成系统运行报告、故障诊断报告及图形分析报告,系统运行可靠,且具有较高的性价比。

Multimodal process monitoring based on transition-constrained Gaussian mixture model

Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is challenging.Most multimodal monitoring methods rely on the assumption that the modes are independent of each other,which may not be appropriate for practical application.This study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process monitoring.This technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online data.This process enables the identified modes to reflect the stability of actual working conditions,improve mode identification accuracy,and enhance monitoring reliability in cases of mode overlap.Case studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach inmultimodal process monitoring with mode overlap.

OPEN ARCHITECTURE CNC SYSTEM HITCNC AND KEY TECHNOLOGY

Aiming at the characteristics of modularity and reconfigurable in open architecture computer numerical control （CNC） system, the open architecture CNC system, Harbin Institute of Tech- nology computer numerical control （HITCNC）, is researched and manufactured based on the interface standards. The system＇s external interfaces are coincident with the corresponding international standards, and the internal interfaces follow the open modular architecture controller （OMAC） agreement. In the research and manufacturing process, object-oriented technology is used to ensure the openness of the HITCNC, and static programming is applied in the CNC system according to the idea of modularization disassembly. The HITCNC also actualizes real-time and unreal-time modules adopting real-time dynamical linked library （RTDLL） and component object model （COM）. Finite state ma- chine （FSM） is adopted to do dynamically modeling of HITCNC. The complete separation between the software and the hardware is achieved in the HITCNC by applying the SoftSERCANS technique. The application of the above key techniques decreases the programming workload greatly, and uses software programs replacing hardware functions, which offers plenty technique ensures for the openness of HITCNC. Finally, based on the HITCNC, a three-dimensional milling system is established. On the system, series experiments are done to validate the expandability and interchangeability of HITCNC. The results of the experiments show that the established open architecture CNC system HITCNC is correct and feasible, and has good openness.

基于Simulink/Stateflow的小卫星姿态控制模块设计与仿真研究

针对小卫星在轨自主运行的需要,对其姿态控制模块进行设计与仿真研究,其中使用修正罗德里格参数描述卫星姿态运动并进行姿态控制律设计,使用Stateflow设计了六种姿态控制模式及其转换逻辑并结合Simulink搭建了姿态控制仿真系统。根据对地定向模式、目标指向模式、闲置模式的时序指令进行了姿态控制仿真分析,三种模式下姿态均收敛,说明了姿态控制律设计和姿态控制模式之间转换逻辑设计的正确性。

一种改进的低功耗NAND Flash控制器设计

介绍了一种低功耗低成本Nand Flash控制器,控制器能够根据输入的命令码产生满足擦除、读写功能的时序完成相应操作。具有功耗低、面积小、电路实现简单的特点,采用中芯国际0.18μm工艺库进行DC综合验证,最高工作频率可达28.5MHz,面积仅需6805μm2。32位命令码仅用到低八位,能够有效实现抗干扰,在命令码判别方面,采用改进的判别电路,将命令码进行分块比较,将命令码分成高16位和低16位,然后再细分进行比较判断,减少了计数器和移位寄存器的使用,从而减小了面积。

Identification of navigation characteristics of single otter trawl vessel using four machine learning models

Fishing logbook records the fishing behaviors and other information of fishing vessels.However,the accuracy of the recorded information is often difficult to guarantee due to the misreport and concealment.The fishing vessel monitoring system(VMS)can monitor and record the navigation information of fishing vessels in real time,and it may be used to improve the accuracy of identifying the state of fishing vessels.If the VMS data and fishing logbook are combined to establish their relationships,then the navigation characteristics and fishing behavior of fishing vessels can be more accurately identified.Therefore,first,a method for determining the state of VMS data points using fishing log data was proposed.Secondly,the relationship between VMS data and the different states of fishing vessels was further explored.Thirdly,the state of the fishing vessel was predicted using VMS data by building machine learning models.The speed,heading,longitude,latitude,and time as features from the VMS data were extracted by matching the VMS and logbook data of three single otter trawl vessels from September 2012 to January 2013,and four machine learning models were established,i.e.,Random Forest(RF),Adaptive Boosting(AdaBoost),K-Nearest Neighbor(KNN),and Gradient Boosting Decision Tree(GBDT)to predict the behavior of fishing vessels.The prediction performances of the models were evaluated by using normalized confusion matrix and receiver operator characteristic curve.Results show that the importance rankings of spatial(longitude and latitude)and time features were higher than those of speed and heading.The prediction performances of the RF and AdaBoost models were higher than those of the KNN and GBDT models.RF model showed the highest prediction performance for fishing state.Meanwhile,AdaBoost model exhibited the highest prediction performance for non-fishing state.This study offered a technical basis for judging the navigation characteristics of fishing vessels,which improved the algorithm for judging the behavior of fishing vessels based on VMS data,enhanced the prediction accuracy,and upgraded the fishery management being more scientific and efficient.