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.

Dynamic Vision-Based Machinery Fault Diagnosis With Cross-Modality Feature Alignment

Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades,and the vibration acceleration data collected by contact accelerometers have been widely investigated.In many industrial scenarios,contactless sensors are more preferred.The event camera is an emerging bio-inspired technology for vision sensing,which asynchronously records per-pixel brightness change polarity with high temporal resolution and low latency.It offers a promising tool for contactless machine vibration sensing and fault diagnosis.However,the dynamic vision-based methods suffer from variations of practical factors such as camera position,machine operating condition,etc.Furthermore,as a new sensing technology,the labeled dynamic vision data are limited,which generally cannot cover a wide range of machine fault modes.Aiming at these challenges,a novel dynamic vision-based machinery fault diagnosis method is proposed in this paper.It is motivated to explore the abundant vibration acceleration data for enhancing the dynamic vision-based model performance.A crossmodality feature alignment method is thus proposed with deep adversarial neural networks to achieve fault diagnosis knowledge transfer.An event erasing method is further proposed for improving model robustness against variations.The proposed method can effectively identify unseen fault mode with dynamic vision data.Experiments on two rotating machine monitoring datasets are carried out for validations,and the results suggest the proposed method is promising for generalized contactless machinery fault diagnosis.

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.

Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

Product Maintenance Oriented Remote Monitoring and Diagnosis System

A research on maintenance oriented remote monitoring and diagnosis modular as well as the data transportation technique is carried out. An opened and modularized data share framework integrated with virtual graphic transportation is presented to realize the data exchange. As a result, it implements a real-time monitoring, diagnosis and maintenance system based on WWW. An effective support technique for the real-time remote fault diagnosis, maintenance and entire life cycle design of products is supplied.

Design and implementation of an expert system for remote fault diagnosis in ship lift

In this paper an expert system for remote fault diagnosis in the ship lift was developed by analysis of the fault tree and combination with VPN. The fault tree was constructed based on the operation condition of the ship lift. The diagnosis model was constructed by hierarchical classification of the fault tree structure, and the inference mechanism was given. Logical structure of the fault diagnosis in the ship lift was proposed. The implementation of the expert system for remote fault diagnosis in the ship lift was discussed, and the expert system developed was realized on the VPN virtual network. The system was applied to the Gaobaozhou ship lift project, and it ran successfully.

A Study on a Remote Monitoring and Diagnosis System and Its Application

Remote monitoring and diagnosis （RMD） is a new kind of monitoring and diagnosis technology that combines computer science, communication technology and fault diagnosis technology. Via the Internet a remote monitoring and diagnosis system can be established. In this paper, the model of an Internet based remote monitoring and diagnosis system is presented; the function of every part of the RMD system is discussed. Then, we introduce a practical example of a remote monitoring and diagnosis system that we established in a factory; its traits and functions are described.

Remote Monitoring and Diagnosis System for High-speed Wire Rolling Mills

Traditional fault diagnosis systems of rolling mills mostly use single machine monitoring net,which leads the re- al-time data running only in the enterprise locally and can not monitor and manage the high-speed wire rolling mills between units, workshops and factories concentratedly.A new-type structure of remote diagnosis system for high-speed wire rolling mills is pre- sented in this paper.The signal processing,computer network and remote diagnosis etc techniques are used to predictive maintenance manage the rolling mills units in this system.The new structure reinforced the remote feedback function,made up the existing fault diagnosis systems’ insufficiency in the extension and the function,promoted resource sharing and avoided the repeat develop- ment.The remote diagnosis example shows that the system can monitor and diagnose the fault information of remote machine timely and effectively.

A Novel Method Based on UNET for Bearing Fault Diagnosis

Reliability of rotating machines is highly dependent on the smooth rolling of bearings.Thus,it is very essential for reliable operation of rotating machines to monitor the working condition of bearings using suitable fault diagnosis and condition monitoring approach.In the recent past,Deep Learning(DL)has become applicable in condition monitoring of rotating machines owing to its performance.This paper proposes a novel bearing fault diagnosis method based on the processing and analysis of the vibration images.The proposed method is the UNET model that is a recent development in DL models.The model is applied to the 2D vibration images obtained by transforming normalized amplitudes of the time-series vibration data samples into the corresponding vibration images.The UNET model performs pixel-level feature learning using the vibration images owing to its unique architecture.The results demonstrate that the model can perform dense predictions without any loss of label information,generally caused by the sliding window labelling method.The comparative analysis with other DL models confirmed the superiority of the UNET model which has achieved maximum accuracy of 98.91%and F1-Score of 99%.

A Comparative Study of Bayes Classifiers for Blade Fault Diagnosis in Wind Turbines through Vibration Signals

Renewable energy sources are considered much in energy fields because of thecontemporary energy calamities. Among the important alternatives being considered, windenergy is a durable competitor because of its dependability due to the development of theinnovations, comparative cost effectiveness and great framework. To yield wind energymore proficiently, the structure of wind turbines has turned out to be substantially bigger,creating conservation and renovation works troublesome. Due to various ecologicalconditions, wind turbine blades are subjected to vibration and it leads to failure. If thefailure is not diagnosed early, it will lead to catastrophic damage to the framework. In orderto increase safety observations, to reduce down time, to bring down the recurrence ofunexpected breakdowns and related enormous maintenance, logistic expenditures and tocontribute steady power generation, the wind turbine blade must be monitored now andthen to assure that they are in good condition. In this paper, a three bladed wind turbinewas preferred and using vibration source, the condition of a wind turbine blade is examined.The faults like blade crack, erosion, hub-blade loose connection, pitch angle twist and bladebend faults were considered and these faults are classified using Bayes Net (BN),Discriminative Multinomial Naïve Bayes (DMNB), Naïve Bayes (NB), Simple NaïveBayes (SNB), and Updateable Naïve Bayes (UNB) classifiers. These classifiers arecompared and better classifier is suggested for condition monitoring of wind turbine blades.

Data-driven technology of fault diagnosis in railway point machines:review and challenges

Safety and reliability are absolutely vital for sophisticated Railway Point Machines(RPMs).Hence,various kinds of sensors and transducers are deployed on RPMs as much as possible to monitor their behaviour for detection of incipient faults and anticipation using data-driven technology.This paper firstly analyses and summarizes six RPMs’characteristics and then reviews the data-driven algorithms applied to fault diagnosis in RPMs during the past decade.It provides not only the process and evaluation metrics but also the pros and cons of these different methods.Ultimately,regarding the characteristics of RPMs and the existing studies,eight challenging problems and promising research directions are pointed out.

Research on Key Techniques of Condition Monitoring and Fault Diagnosing Systems of Machine Groups

This paper describes the development of the condition monitoring and faultdiagnosing system of a group of rotating machinery. The data management is performed by means ofdouble redundant data bases stored simultaneously in both the analyzing server and monitoringclient. In this way, high reliability of the storage of data is guaranteed. Condensation of trenddata releases much space resource of the hard disk. Diagnosing strategies orientated to differenttypical faults of rotating machinery are developed and incorporated into the system. Experimentalverification shows that the system is suitable and effective for condition monitoring and faultdiagnosing for a rotating machine group.

Monitoring and diagnosis of complex production process based on free energy of Gaussian–Bernoulli restricted Boltzmann machine

Online monitoring and diagnosis of production processes face great challenges due to the nonlinearity and multivariate of complex industrial processes.Traditional process monitoring methods employ kernel function or multilayer neural networks to solve the nonlinear mapping problem of data.However,the above methods increase the model complexity and are not interpretable,leading to difficulties in subsequent fault recognition/diagnosis/location.A process monitoring and diagnosis method based on the free energy of Gaussian-Bernoulli restricted Boltzmann machine(GBRBM-FE)was proposed.Firstly,a GBRBM network was established to make the probability distribution of the reconstructed data as close as possible to the probability distribution of the raw data.On this basis,the weights and biases in GBRBM network were used to construct F statistics,which represents the free energy of the sample.The smaller the energy of the sample is,the more normal the sample is.Therefore,F statistics can be used to monitor the production process.To diagnose fault variables,the F statistic for each sample was decomposed to obtain the Fv statistic for each variable.By analyzing the deviation degree between the corresponding variables of abnormal samples and normal samples,the cause of process abnormalities can be accurately located.The application of converter steelmaking process demonstrates that the proposed method outperforms the traditional methods,in terms of fault monitoring and diagnosis performance.

基于Web的CNC机床远程故障诊断系统

对基于Web的Multi-agent的远程故障诊断技术进行了研究,提出基于知识的Multi-agent的数控机床(CNC)的远程故障诊断系统,该系统由诊断和学习agents(DLA)、远程设备agents(RMA)和中央管理agent(CMA)组成。通过这些Agents之间的协作和通信,实现CNC机床的远程监测和故障诊断。

增强的最小相关广义Lp/Lq范数反卷积在旋转机械故障诊断中的应用

用于提取滚动轴承故障周期冲击特征的最小相关广义Lp/Lq范数反卷积(MCG-Lp/Lq-D)存在对先验周期参数的精确度要求过高的问题。因此有必要提出一种新的周期估计方法-具有约束的自适应周期估计(APEC),该方法可以在强噪声条件下依然能够稳定的给出周期信息的真实估计或其近似值,随后将APEC引入到MCG-Lp/Lq-D构造了新的盲解卷积算法EMCG-Lp/Lq-D,该方法不仅能在强噪声条件下通过APEC自适应调整故障周期,同时还继承了MCG-Lp/Lq-D对噪声和异常值鲁棒的特点。由于APEC是一种需要先验周期(不要求精确周期)指导的周期估计方法,这使得其在低信噪比(SNR)条件下具有更好的鲁棒性来估计故障周期,从而使EMCG-Lp/Lq-D的运用范围相较于MCG-Lp/Lq-D的更广泛。最后,仿真和实验验证了EMCG-Lp/Lq-D的有效性和优越性。

基于嵌入式机器视觉识别的电力设备监测系统设计

为了提高对电力设备运行过程的监控能力,设计了嵌入式的机器视觉系统,该系统包括图形采集模块、图像处理模块、图形显示模块、结果输出模块等。通过嵌入式技术设计,实现了电力设备图像信息的采集、处理、显示和输出;通过OV7670图像传感器提高了图像采集能力。在进行故障信息诊断时,采用基于多主成分分析模型及支持向量机-DS融合决策的方法,实现正常数据信息与故障数据信息的分离。实验表明,该系统故障诊断正确率高达95%,大大提高了对电力设备监控能力和故障处理效率。

工程机械的状态监测与故障诊断技术

本文深入探讨了工程机械状态监测与故障诊断技术的最新发展。针对工程机械在运行过程中可能遇到的各种故障,提出一种基于数据驱动和机器学习的先进监测系统。该系统通过实时采集并分析工程机械传感器数据,利用深度学习模型进行故障诊断,提前发现并预防潜在故障。本文介绍了该系统的设计框架和关键技术,包括数据预处理、特征提取以及故障诊断模型的建立。

煤矿提升机智能监控及故障诊断技术研究

针对煤矿提升机自动化监测水平低下、监测量单一等问题,结合远程监控及传感器融合检测等技术,提出了一种以PLC为控制核心、多传感器采集的煤矿提升机智能监控系统设计方案。通过多种传感器对提升机核心部件的压力、温度、振动等重要工况参数进行实时采集,通过通信网络架构及上位机实现设备远程监控及故障预警诊断,有效保障了提升机安全稳定运行。

海上平台无人值守智能化井口装置及采油树的设计与应用

探讨海上平台无人值守智能化井口装置及采油树的应用情况和存在问题。在明确无人值守智能化井口的总体方案,确定智能化井口必备的实时监测、故障诊断、远程控制三大主要功能和改造、升级方向的基础上,设计平台的井口布局图,对智能化井口进行结构设计、仿真测试、尺寸优化,研发满足无人平台空间布局要求的井口装置及采油树。为确定主要模块的功能和改造要求,根据其特点和需求,对智能化井口进行详细设计和分析,并针对当前无人井口平台的技术特点,提出增强远程监测和控制技术的改进方向。

Monitoring and Detection of Wind Turbine Vibration with KNN-Algorithm

Maintenance for wind turbines has been transformed using supervised machine learning techniques. This method of automatic and autonomous learning can identify, monitor, and detect electrical and mechanical components of wind turbines and predict, detect, and anticipate their degeneration. Using a machine learning classifier and frequency analysis, we simulate two failure states caused by bearing vibrations. Implementing KNN facilitates efficient monitoring, monitoring, and fault-finding for wind turbines. It is possible to reduce downtime, anticipate breakdowns, and import offshore aspects through these technologies.