Research on Machine Tool Fault Diagnosis and Maintenance Optimization in Intelligent Manufacturing Environments

In the context of intelligent manufacturing,machine tools,as core equipment,directly influence production efficiency and product quality through their operational reliability.Traditional maintenance methods for machine tools,often characterized by low efficiency and high costs,fail to meet the demands of modern manufacturing industries.Therefore,leveraging intelligent manufacturing technologies,this paper proposes a solution optimized for the diagnosis and maintenance of machine tool faults.Initially,the paper introduces sensor-based data acquisition technologies combined with big data analytics and machine learning algorithms to achieve intelligent fault diagnosis of machine tools.Subsequently,it discusses predictive maintenance strategies by establishing an optimized model for maintenance strategy and resource allocation,thereby enhancing maintenance efficiency and reducing costs.Lastly,the paper explores the architectural design,integration,and testing evaluation methods of intelligent manufacturing systems.The study indicates that optimization of machine tool fault diagnosis and maintenance in an intelligent manufacturing environment not only enhances equipment reliability but also significantly reduces maintenance costs,offering broad application prospects.

Research on Active Safety Methodologies for Intelligent Railway Systems

Safety is essential when building a strong transportation system.As a key development direction in the global railway system,the intelligent railway has safety at its core,making safety a top priority while pursuing the goals of efficiency,convenience,economy,and environmental friendliness.This paper describes the state of the art and proposes a system architecture for intelligent railway systems.It also focuses on the development of railway safety technology at home and abroad,and proposes the active safety method and technology system based on advanced theoretical methods such as the in-depth integration of cyber–physical systems(CPS),data-driven models,and intelligent computing.Finally,several typical applications are demonstrated to verify the advancement and feasibility of active safety technology in intelligent railway systems.

Failure Prediction and Intelligent Maintenance of a Transportation Company’s Urban Fleet

The present work deals with intelligent vehicle fleet maintenance and prediction. We propose an approach based primarily on the history of failures data and on the geographical data system. The objective here is to predict the date of failures for a fleet of vehicles in order to allow the maintenance department to efficiently deploy the proper resources;we further provide specific details regarding the origins of failures, and finally, give recommendations. This study used the Société de transport de Montréal (STM)’s historical bus failure data as well as weather data from Environment Canada. We thank Facebook’s Prophet, Simple Feed-forward, and Beats algorithms (Uber), we proposed a set of computer codes that allow us to identify the 20% of buses that are responsible for the 80% of failures by mean of the failure history. Then, we deepened our study on the unreliable equipments identified during the diffusion of our computer code This allowed us to propose probable predictions of the dates of future failures. To ensure the validity of the proposed algorithm, we carried out simulations with more than 250,000 data. The results obtained are similar to the predicted theoretical values.

Research and Development of a Network-Based Intelligent Maintenance Information System

A maintenance information system is an important part of equipmentmanagement. An intelligent maintenance information system (IMIS) is a synthesis of networktechnology, information technology and intelligent technology. The IMIS is used to finish flexiblemaintenance decision-making and fast maintenance planning, which helps enterprises to effectivelyreduce maintenance cost and increase working efficiency. Because the IMIS integrates advancedtechnologies, its performance is better than a traditional one. The difference between an IMIS and atraditional maintenance information system, and the functions, structure, important realizations,and application of an IMIS are discussed in this paper.

Artificial Intelligence-Driven Vehicle Fault Diagnosis to Revolutionize Automotive Maintenance:A Review

Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity critically.Hence,there is a growing demand for advanced fault diagnosis technologies to mitigate the impact of these limitations on unplanned vehicular downtime caused by unanticipated vehicle breakdowns.Due to vehicles’increasingly complex and autonomous nature,there is a growing urgency to investigate novel diagnosis methodologies for improving safety,reliability,and maintainability.While Artificial Intelligence(AI)has provided a great opportunity in this area,a systematic review of the feasibility and application of AI for Vehicle Fault Diagnosis(VFD)systems is unavailable.Therefore,this review brings new insights into the potential of AI in VFD methodologies and offers a broad analysis using multiple techniques.We focus on reviewing relevant literature in the field of machine learning as well as deep learning algorithms for fault diagnosis in engines,lifting systems(suspensions and tires),gearboxes,and brakes,among other vehicular subsystems.We then delve into some examples of the use of AI in fault diagnosis and maintenance for electric vehicles and autonomous cars.The review elucidates the transformation of VFD systems that consequently increase accuracy,economization,and prediction in most vehicular sub-systems due to AI applications.Indeed,the limited performance of systems based on only one of these AI techniques is likely to be addressed by combinations:The integration shows that a single technique or method fails its expectations,which can lead to more reliable and versatile diagnostic support.By synthesizing current information and distinguishing forthcoming patterns,this work aims to accelerate advancement in smart automotive innovations,conforming with the requests of Industry 4.0 and adding to the progression of more secure,more dependable vehicles.The findings underscored the necessity for cross-disciplinary cooperation and examined the total potential of AI in vehicle default analysis.

Research and design of an expert diagnosis system for rail vehicle driven by data mechanism models

Purpose-The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models.Design/methodology/approach-The expert diagnosis system utilizes statistical and deep learning methods to model the real-time status and historical data features of rail vehicle.Based on data mechanism models,it predicts the lifespan of key components,evaluates the health status of the vehicle and achieves intelligent monitoring and diagnosis of rail vehicle.Findings-The actual operation effect of this system shows that it has improved the intelligent level of the rail vehicle monitoring system,which helps operators to monitor the operation of vehicle online,predict potential risks and faults of vehicle and ensure the smooth and safe operation of vehicle.Originality/value-This system improves the efficiency of rail vehicle operation,scheduling and maintenance through intelligent monitoring and diagnosis of rail vehicle.

Research on Maintenance Information Management System for Distributed Manufacture System

An architecture and design of a maintenance information management system for distributed manufacture system is presented in this paper, and its related key technologies are studied and implemented also. A frame of the maintenance information management system oriented human-machine monitoring is designed, and using object-oriented method, a general maintenance information management system based on SQL server engineering database and adopted client/server/database three-layer mode can be established. Then, discussions on control technologies of maintenance information management system and remote distributed diagnostics and maintenance system are emphasized. The system is not only able to identify and diagnose faults of distributed manufacture system quickly, improve system stability, but also has intelligent maintenance functions.

NONMONOTONIC TRUTH MAINTENANCE SYSTEMS

Truth maintenance systems become the very useful tools in artificial intelligence. Existing truth maintenance systems can’t deal with nonmonotonic reasoning effectively. They have limitations in the representation of nonmonotonic justifications. We present stratified truth maintenance systems which introduce priorities among justifications. The stratified truth maintenance systems can deal with nonmonotonic reasoning more effectively and can be applied in many useful areas.

人工智能在土木工程领域的应用研究现状及展望

作为新一代产业变革的核心驱动力,人工智能是全面提高土木工程领域数字化、自动化、信息化和智能化的重要方法。为全面了解人工智能在土木工程中的发展及应用,定性分析了人工智能的基本研究领域,定量分析了人工智能在土木工程设计、制造、养维护阶段的研究现状,利用CiteSpace可视化工具深入挖掘人工智能在土木工程中存在的问题、发展瓶颈和研究趋势,并给出相应的解决办法及研究思路。通过文献综述发现,土木工程领域已展开了大量人工智能研究,但各阶段智能化发展不均衡,实际应用也存在一定局限性,需深入探索神经网络、大数据、深度学习等智能技术在土木工程全生命周期的交叉融合,促进土木工程领域人工智能研究的协同发展。

基于数字孪生的智能运维理论体系与实现方法

在建筑行业转型升级的背景下,针对大型建筑运维过程管理效率低、各类运维事件决策分析精度不足、智能化程度有待提高等问题,提出基于数字孪生的智能运维理论体系与实现方法。总结归纳大型建筑智能运维需要采集的信息,分析智能运维亟待解决的问题,融合智能感知与数字孪生技术,给出智能运维运行机理,建立针对大型建筑的智能运维架构体系与多维多尺度的孪生模型。研究建筑智能运维的实现方法,其中包含智能运维全要素信息的采集与传输、智能运维孪生体的构建与运行、智能运维孪生数据的管理机制及智能运维平台的架构。在智能运维理论体系和实现方法的指导下,搭建了智能运维平台,并应用于某大型建筑工程的运维管理。考虑运维管理过程中的虚实交互与时空演化,初步验证了数字孪生在提高运维管理智能化程度方面的有效性。

基于“一中心四平台”的公立医院后勤运营管理实践研究

目的探讨基于“一中心四平台”的医院后勤运营管理模式成效。方法以医院智慧管理分级评估标准为依据,依托大数据、物联网、互联网+、人工智能等技术运用,打造后勤智慧运维管理中心以及设备管控信息化平台、安全防范信息化平台、能耗监测信息化平台、运营管理信息化平台的“一中心四平台”运营管理模式。结果经过样本医院的管理实践证明:基于“智慧联动”的设备故障处置能力得到显著提升,2022年故障报警次数同比2021年下降28.63%,其中高级、普通报警次数同比2021年分别下降69.53%、33.15%;基于“一站式”的后勤服务保障能力得到显著提升,2022年同比2021年维修次数下降2.92%,维修质量有所提高,反复维修频次降低;基于“数据挖掘”的能耗管控能力得到显著提升,2022年万元收入能耗支出同比2021年下降8.71%。结论“一中心四平台”可以显著提高后勤运维管理水平。

农用拖拉机动力系统智能化维修平台应用

通过对农用拖拉机动力系统故障识别和检修过程进行描述,基于CAN总线通讯的方式,进行拖拉机动力系统智能维修平台硬件结构搭建,并采用高性能中央处理器,实现智能维修平台的功能原理和硬件结构。应用结果表明:系统运行过程可靠,动力系统故障定位准确,能够有效提升维修人员对动力系统故障的定位和故障维修的能力。

基于故障树的IMS网络运维场景异常检测算法

由于导致网络运维场景异常的因素较为复杂,直接利用表观数据对异常状态进行检测,误差相对较大,为此,提出基于故障树的IMS网络运维场景异常检测算法。利用Fusell-Vesely算法,以运维场景的比特率为参数,按照逐级向下分解的方式计算故障树的最小割集,通过顶事件下属事件之间的逻辑关系实现对网络运维场景异常因素的全覆盖,在获取待检测场景的比特率数据后,借助故障树逐级判断数据对应的事件,并得到最终的状态判定结果。测试结果中,设计算法对不同程度的IMS网络运维场景异常检测结果误差稳定在1.0%以内。

自注意力机制模型在烟草业务系统运维中的应用

本文针对烟草业务系统日常运维中,对生产异常,特别是物料损耗异常发现难、追溯排查难的问题,设计并实现了一种基于双向长短期记忆模型(bi-directional long short-term memory,Bi-LSTM)和自注意力机制的损耗异常分析模型。以烟丝损耗异常检查为例介绍该模型,以卷包系统的时序剔除数据为输入,判断原材料损耗是否存在异常。该分析模型可用于烟草业务系统日常监控运维,自动识别各生产阶段物料损耗异常,并通过注意力权重从空间和时间维度解释分析结果,为人工排查提供先验,辅助生产管理,提升运维系统的智能化。

基于数字孪生的清扫装置智能运维系统设计

为解决带式输送机清扫装置智能运维准确率低、可视化不足等问题,设计了一种基于数字孪生的清扫装置智能运维系统。结合实际工况,建立了基于数字孪生的智能运维系统框架,并构建融合物理模型、逻辑模式、功能模型的孪生模型,提出一种融合Dropout的LSTM-ELM轴承故障诊断模型,进一步结合WebGL等技术进行可视化展示。结果表明:该系统平均诊断精度达95.34%,可视化展示充分,达到实际运维需求。可为推动数字孪生技术落地提供参考。

ITMA ASIA+CITME2022上新一代信息技术应用浅析

探讨ITMA ASIA+CITME2022上众多新一代信息技术的应用情况。从车间物流数字孪生、原棉精细化管理、细纱机单锭监测、设备远程运维等不同角度,梳理了展会上新一代信息技术应用场景及解决方案。指出:纺机企业研发方向主要面向高效节能、高附加值纺织品的数字化、自动化和智能化生产与质量管控;新一代信息技术使得纺织产业数字化升级更加快速和高效,纺织生产整体智能化水平明显提升;基于工业互联网的纺织产业新生态正在加快形成。认为:新一代信息技术与纺织产业的融合应用,推动了产品研发、制造、服务等环节的协同创新,助力企业实现提质增效与高质量发展的战略目标。

烟草制造业务系统智能运维平台的设计与应用

随着国家“两化融合”战略的提出和实施,烟草企业积极响应并进行全面的信息化建设,信息技术在烟草企业的深入发展,信息化业务系统以及数据量呈指数态势上升,同时烟草企业内部各个业务系统相对独立,在系统间缺乏有效的数据共享和协同,存在系统竖井的问题,业务端与设备端运维缺乏有效的协同支持,为运维人员的日常工作带来了很大的挑战。本文研究智能运维在烟草企业的落地场景,通过应用性能指标异常智能化分析、系统故障的智能化判断与辅助、日志集中管理与分析以及系统性能容量预测四大场景,设计以烟草制造业务与IT资源双稳定为运维视角的智能运维平台。

基于ITSS的智慧教学空间运维体系设计研究

当前数字技术与教学环境深度融合的智慧教学空间极大支持了教学新模式的开展,但存在教学空间内设备和系统复杂且运维工作量逐年增加,以及运维服务工作面临流程难追溯、资源易流失、技术不匹配等诸多问题挑战。借鉴ITSS标准,深入分析智慧教学空间运维工作相关要素,针对院校现状及存在问题,以提升管理效率和服务质量为目标,提出构建适合院校智慧教学空间运维实际的高效运维体系。

大连湾海底隧道结构健康监测系统研究与应用

结构健康监测是通过无损、实时的方式识别外部环境变化及结构自身变化,实现损伤识别、评估分析、安全预警等功能,从而快速辨别结构的健康状态的监测手段。结构健康监测系统可为项目的智慧管养、运维提供决策和支持,达到降低运营风险、延长结构使用寿命、节约运营成本的目的。文中从监测系统中监测内容的设计、监测平台的软硬件构建等方面对大连湾海底隧道结构健康监测系统进行介绍,应用反馈的监测数据验证了监测系统的准确有效性。

不确定环境下考虑多技能人力资源的复杂产品MRO服务资源调度

针对复杂产品维护、维修、大修(MRO)服务资源调度中存在人力资源多技能异质而导致任务实际执行工期随着指派方案的不同而变化的问题,将多技能工调度与人员学习效应相结合,建立“任务技能人员”的匹配关系。以最小化服务完成时间、最小化人员冗余和最大化资源性能成本比为目标,构建资源调度时间参数不确定条件下的随机机会约束规划数学模型。并提出一种基于随机模拟、BP神经网络和改进NSGA-II的混合智能算法进行求解。通过具有不同参数特征的对比实验案例和敏感性分析,验证了所提模型和算法的有效性和适用性。