基于数字孪生软件的设备预测性维护技术研究

本文研究分析了钢铁行业设备智能运维的现状,将数字孪生技术应用在设备预测性维护领域,通过研究棒线材设备故障诊断分析技术、轧机故障信号降噪重构特征识别方法、基于设备的振动信号报警识别方法及计算设备、具有故障点定位的棒线材设备故障诊断方法和设备数字孪生模型研究等,建立基于AIoT的棒线材设备数字孪生运维系统,以数字孪生3D可视化的仿真方式展现“产线级”和“设备级”对运行数据的实时监测,辅助修护决策取得较好成效。丰富多元的人机交互方式推动全新生产管理和设备维护协作模式的发展,成为企业数字化转型升级的重要引擎,为数字孪生技术的研究与落地应用提供参考。     

基于DT的售后设备预测性维护协同模式研究

设备制造商为了满足设备用户对快速故障诊断和维护的需求,需要对设备进行实时监测、预测和远程指导。以数字孪生五维模型为基础,提出基于数字孪生的协同维护七维模型和维护服务协同模式。该模式下设备制造商和设备用户可实时获得对设备的监测信息,使得制造商能向用户提供更有效、及时的服务和支持。最后以某瓶装水封盖过程为例,构建设备隐式半马尔可夫模型,对设备当前运行状态的持续时间进行测算,得到与下一次劣化或故障状态的时间间隔,起到预测效果。构建了数字孪生服务平台框架和维护知识数据库,验证了所提出的协同维护模式的可行性。     

控制阀的防患于未然——浅谈控制阀的预测性维护

以数字式阀门定位器为核心的控制阀数字解决方案旨在控制阀的预测性维护,并实现执行机构自校准、自适应、状态监测、在线动态特性分析、故障识别与诊断,使之成为流程工业现场的智能设备。本文对部分厂家控制阀预测性维护技术进行了探讨和细节分析。     

工业设备预测性维护系统

为解决工业企业中,工业机器人、大型盾构机、道岔等大型工业设备,施工环境恶劣,维护成本昂贵,乃至产品质量和有序生产.开发工业设备预测性维护系统.系统基于SpringBoot后端框架、VUE前端框架、TensorFlow大数据分析框架对系统进行开发;基于物联网设备系统在针对非计划停机维护的相关工业指标进行实时数据采集;基于...     

基于数字孪生的故障注入技术研究

针对机械设备的故障检测和诊断问题,提出了基于数字孪生的故障注入技术的基本架构和实现流程。以常见齿轮箱中啮合齿轮为例,在分析其振动机制后构建了对应的机制模型。根据物理实体构建了正常状态模型和故障状态模型,并根据故障条件下的信号特征建立了故障空间,为设备状态预测和故障检测创造了条件。     

矿用通风机的维护与故障处理

本文主要阐述了矿用主要通风机的定期检修和轴流式风机常见故障及处理等技术问题。     

基于数字孪生与深度学习技术的制造加工设备智能化方法研究

针对传统制造加工设备在生产加工过程中存在设备与数据信息联系不紧密,设备使用维护多依赖于人工经验等问题,提出了一种新的设备智能化方法。首先,在信息层建立能反映制造加工设备真实状态的数字孪生体;其次,基于历史加工大数据,通过数字孪生体对加工过程的行为进行建模及深度学习和训练,并利用训练好的人工神经网络根据采集到的实时数据来预测制造加工设备下一时刻的状态,使制造加工设备实现物理层与信息层数据的深度融合,拥有自我感知、自我预测的能力,最终实现智能化;最后,以浆料微流挤出成型设备挤出结构系统的智能化实施过程为例,验证了所提出方法的可行性。实例结果表明该设备智能化方法可有效地对挤出结构系统的运行状态进行监测及预测,为后续提高挤出成型精度提供了有效的数据信息。研究表明数字孪生和深度学习技术能够提升制造加工设备的智能化程度,可为未来智能制造的发展提供理论支撑。     

浅析数字光纤通信设备的维护

随着我国数字光纤通倩设备的广泛使用,搞好光纤通信设备的维护是当前通信工作中很重要的一项工作。由于数字光纤通信技术的迅速发展以及各种不同制式的出现,对于数字光纤通信设备的维护目前尚未形成统一的规格与标准。因此,本文仅就数字光纤通信设备维护的特点、内容、方法等几个基本问题作一简要分析与介绍。     

矿用机电设备故障监测及防治处理

煤矿机电设备在生产期间发生故障会严重影响正常的地下矿井开采活动。为此,企业在制定生产计划时必须要对矿用机电设备制定严格的故障监测模式,通过“以监代防”的方式避免机电设备故障造成的不利影响。本文主要探讨矿用机电设备故障的监测及其防治处理。     

基于数字孪生的单兵作战系统设计研究

目的 当前国际形势日益复杂严峻,大国之间军事竞争不断加剧,各国加速进行国防装备研发.信息技术飞速发展催生未来战场的信息化、智能化转变,亟需设计研发新型单兵作战系统以应对未来战场环境.为有效地提升单兵作战系统设计效率、解决传统单兵作战系统设计过程的问题,提出数字孪生驱动的单兵作战系统设计方法.方法 基于文献研究和案例研究,总结归纳单兵作战系统设计过程和存在问题,提出数字孪生单兵作战系统设计模型,分析数字孪生单兵作战系统设计模型的组成要素,研究并呈现了几种数字孪生单兵作战系统设计关键技术.结论 随着数字孪生技术日益成熟并得以广泛应用,将数字孪生技术应用于单兵作战系统设计过程具有可行性.数字孪生驱动的单兵作战系统设计过程丰富了国防装备设计研发的理论及方法,为新型单兵作战系统创新设计提供了新的路径.     

Predictive maintenance of complex system with multi-level reliability structure

Onboard sensors, which constantly monitor the states of a system and its components, have made the predictive maintenance (PdM) of a complex system possible. To date, system reliability has been extensively studied with the assumption that systems are either single-component systems or they have a deterministic reliability structure. However, in many realistic problems, there are complex multi-component systems with uncertainties in the system reliability structure. This paper presents a PdM scheme for complex systems by employing discrete time Markov chain models for modelling multiple degradation processes of components and a Bayesian network (BN) model for predicting system reliability. The proposed method can be considered as a special type of dynamic Bayesian network because the same BN is repeatedly used over time for evaluating system reliability and the inter-time–slice connection of the same node is monitored by a sensor. This PdM scheme is able to make probabilistic inference at any system level, so PdM can be scheduled accordingly.     

Predictive maintenance policy based on process data

For the ‘under maintained’ and ‘over maintained’ problems of traditional preventive maintenance, a new predictive maintenance policy is developed based on process data in this article to overcome these disadvantages. This predictive maintenance method utilizes results of probabilistic fault prediction, which reveals evolvement of the system's degradation for a gradually deteriorating system caused by incipient fault. Reliability is calculated through the fault probability deduced from the probabilistic fault prediction method, but not through prior failure rate function which is difficult to be obtained. Moreover, the deterioration mode of the system is determined by the change rate of the calculated reliability, and several predictive maintenance rules are introduced. The superiority of the proposed method is illustrated by applying it to the Tennessee Eastman process. Compared with traditional preventive maintenance strategies, the presented predictive maintenance strategy shows its adaptability and effectiveness to the gradually deteriorating system.     

Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system

The potential of digital twin technology is immense, specifically in the infrastructure, aerospace, and automotive sector. However, practical implementation of this technology is not at an expected speed, specifically because of lack of application-specific details. In this paper, we propose a novel digital twin framework for stochastic nonlinear multi-degree of freedom (MDOF) dynamical systems. The proposed digital twin has four modules — (a) a physics-based nominal model, (b) a data collection module, (c) algorithm for real-time update of the digital twin and (d) module for predicting future state. The modules for real-time update and prediction are based on the so-called gray-box modeling approach, and utilizes both physics based and data driven frameworks; this enables the proposed digital twin to generalize and predict future responses. The gray box modeling framework used within the digital twin is developed by coupling Bayesian filtering and machine learning algorithm. Although, the proposed digital twin can be used with any machine learning regression algorithm, we have used Gaussian process in this study. Performance of the proposed approach is illustrated using two examples. Results obtained indicate the applicability and excellent performance of the proposed digital twin framework.     

Real-time health prognosis and dynamic preventive maintenance policy for equipment under aging Markovian deterioration

An often seen practice of preventive maintenance (PM) is to construct a machine's reliability model based on its historical failure records. The reliability model is then used to determine the PM schedule by minimizing the machine's long-run operation cost or average machine downtime. Machines in many hi-tech manufacturing sectors are using sophisticated sensor technologies to provide sufficient immediate online data for real-time observation of equipment condition. Not only is the historical data but also the real time condition now available for scheduling a more effective PM policy. This research is to determine an effective PM policy based on real-time observations of equipment condition. We first use the multivariate process capability index to integrate the equipment's multiple parameters into an overall equipment health index. This health index serves as the basis for real-time health prognosis under an aging Markovian deterioration model. A dynamic PM schedule is then determined based on the health prognosis.     

Multi-phase preventive maintenance policy for leased equipment

This paper proposes a multi-phase preventive maintenance (PM) policy for leased equipment by combining the advantages of both periodic PM and sequential PM. The lease period of the equipment is divided into multiple PM phases. The PM activities within each phase are performed periodically with the convenience of implementation, while the frequency of PM for each phase is different and it gives a gradual increase because of the imperfect effect of PM. A multi-phase PM model is built up based on the age reduction method for imperfect PM with the penalty for equipment failures and overtime of repair involved. The optimal PM intervals for every PM phases are achieved by minimising the cumulative maintenance cost throughout the lease period from the perspective of the lessor. Numerical example shows that the cumulative maintenance cost under the proposed multi-phase PM policy is lower than that under periodic PM policy.     

The equipment maintenance scheduling problem in a coal production system

This paper addresses an equipment maintenance scheduling problem in a coal production system which includes three consecutive stages: the coal mining stage, the coal washing stage and the coal loading stage. Each stage is composed of different equipment that needs maintenance each day. There exists intermediate storage with finite capacities and the finished products are transported by train. Moreover, some equipment has a different preference for (aversion to) the start time of maintenance (STOM). The objective is to minimise the weighted sum of aversion about STOM, changeover times and train waiting time. We first formulate this problem into a mixed integer linear programming (MILP) model, then a hybrid genetic algorithm (HGA) is proposed to solve it. The proposed method has been tested on a practical coal enterprise in China and some randomly generated instances. Computational results indicate that our algorithm can produce near-optimal solutions efficiently.     

Developing a factory-wide intelligent predictive maintenance system based on Industry 4.0

Abstract

The main purpose of predictive maintenance (PdM) is to reduce unscheduled downtime and consequently improve productivity and reduce production cost. PdM has been featured as a key theme of Industry 4.0. However, the traditional PdM system was only designed for a single tool; as such, the resources allocation will become extremely complicated when hundreds of tools are working together in a factory. A manageable hierarchy and various health indexes are required for factory-wide equipment maintenance. To solve the problem mentioned above, this paper proposes a factory-wide intelligent predictive maintenance system by applying the so-called cyber-physical agent and advanced manufacturing cloud of Things to fulfill the requirements of Industry 4.0, the baseline predictive maintenance scheme to accomplish the PdM functions, and the newly proposed health index hierarchy to supervise factory-wide equipment maintenance.     

A corrective maintenance scheme for engineering equipment

Corrective maintenance is a maintenance task performed to identify and rectify the cause failures for a failed system. The engineering equipment gets many components and failure modes, and its failure mechanism is very complicated. Failure of system-level might occur due to failure(s) of any subsystem/component. Thus, the symptom failure of equipment may be caused by multilevel causality of latent failures.This paper proposes a complete corrective maintenance scheme for engineering equipment. Firstly, the FMECA is extended to organize the numerous failure modes. Secondly, the failure propagation model (FPM) is presented to depict the cause-effect relationship between failures. Multiple FPMs will make up the failure propagation graph (FPG). For a specific symptom failure, the FPG is built by iteratively searching the cause failures with FPM. Moreover, when some failure in the FPG is newly ascertained to occur (or not), the FPG needs to be adjusted. The FPG updating process is proposed to accomplish the adjustment of FPG under newly ascertained failure. Then, the probability of the cause failures is calculated by the fault diagnosis process. Thirdly, the conventional corrective maintenance recommends that the failure with the largest probability should be ascertained firstly. However, the proposed approach considers not only the probability but also the failure detectability and severity. The term REN is introduced to measure the risk of the failure. Then, a binary decision tree is trained based on REN reduction to determine the failure ascertainment order. Finally, a case is presented to implement the proposed approach on the ram feed subsystem of a boring machine tool. The result proves the validity and practicability of the proposed method for corrective maintenance of engineering equipment.     

Opportunistic maintenance of multi‐equipment system: a case study

A case study on preventive maintenance (PM) of a multi‐equipment system is presented in this paper. Each equipment of the system consists of many components/subsystems connected in series. Because of the series structure, opportunistic maintenance (OM) policies are more effective for the components of the equipment. A new OM policy based on the classification of opportunities has been proposed. Various OM policies have been evaluated using simulation modeling, and the new policy has been found to be more effective than the existing OM policies. The impact of this policy on the overall system has also been simulated. Copyright © 2000 John Wiley & Sons, Ltd.