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.

Scheduling optimization problem considering time-of-use tariffs and piece-rate machine maintenance in EAF steelmaking

An operating schedule of the parallel electric arc furnaces(EAFs)considering both productivity and energy related criteria is investigated.A mathematical model is established to minimize the total completion time and the total electricity cost.This problem is proved to be an NP-hard problem,and an effective solution algorithm,longest processing time-genetic(LPT-gene)algorithm,is proposed.The impacts of varied processing energy consumption and electricity price on the optimal schedules are analyzed.The integrated influence of the different weight values and the variation between the peak price and the trough price on the optimal solution is studied.Computational experiments illustrate that considering the energy consumption costs in production has little influence on makespan;the computational performance of the proposed longest processing time-genetic algorithm is better than the genetic algorithm(GA)in the issue to be studied;considerable reductions in the energy consumption costs can be achieved by avoiding producing during high-energy price periods and reducing the machining energy consumption difference.The results can be a guidance for managers to improve productivity and to save energy costs under the time-of-use tariffs.

Comparing Machine Learning Algorithms for Improving the Maintenance of LTE Networks Based on Alarms Analysis

Mobile network operators are facing many challenges to satisfy their subscribers in terms of quality of service and quality of experience provided. To achieve this goal, technological progress and scientific advances offer good opportunities for efficiency in the management of faults occurring in a mobile network. Machine learning techniques allow systems to learn from past experiences and can predict, solutions to be applied to correct the root cause of a failure. This paper evaluates machine learning techniques and identifies the decision tree as a learning model that provides the most optimal error rate in predicting outages that may occur in a mobile network. Three machine learning techniques are presented in this study and compared with regard to accuracy. This study demonstrates that the appropriate machine learning technique improves the accuracy of the model. By using the decision tree as a machine learning model, it was possible to predict solutions to network failures, with an error rate less than 2%. In addition, the use of Machine Learning makes it possible to eliminate steps in the network failure processing chain;resulting in reduced service disruption time and improved the network availability which is a key network performance index.

Dynamic Evaluation Model and Application Methods for Engineering Machine Maintenance Quality

It is an important content of equipment management to keep the engineering machine well. Based on the theory of component technology and grey related algorithm arithmetic, the requirements and procedures of engineering machine maintenance predicting process are analyzed, and a support object evaluation system is provided. The qualitative and quantitative indexes of evaluating process are fully taken into consideration to provide scientific methods and ways for proper evaluation and decision.

Research on Approximate Calculation of Preventive Maintenance Period in Machinery Systems under Random Distribution

Approximate calculation methods of prevention maintenance period under the random distribution are given,and three kinds of approximate calculation models of prevention maintenance period based on different security demands are come up with according to maintenance problems of machinery systems in modern enterprise and starting with different demands of systems. And then,how to make certain the best maintenance period by using the approximate calculation methods is illustrated by an exam- ple.

A Multilevel Design Method of Large-scale Machine System Oriented Network Environment

The design of large-scale machine system is a very complex problem.These design problems usually have a lot of design variables and constraints so that they are difficult to be solved rapidly and efficiently by using conventional methods.In this paper,a new multilevel design method oriented network environment is proposed,which maps the design problem of large-scale machine system into a hypergraph with degree of linking strength (DLS) between vertices.By decomposition of hypergraph,this method can divide the complex design problem into some small and simple subproblems that can be solved concurrently in a network.

Joint Optimization of Imperfect Preventive Maintenance and Production Scheduling for Single Machine Based on Game Theory Method

In this study,an optimization model of a single machine system integrating imperfect preventive maintenance planning and production scheduling based on game theory is proposed.The costs of the production department and the maintenance department are minimized,respectively.Two kinds of three-stage dynamic game models and a backward induction method are proposed to determine the preventive maintenance(PM)threshold.A lemma is presented to obtain the exact solution.A comprehensive numerical study is provided to illustrate the proposed maintenance model.The effectiveness is also validated by comparison with other two existed optimization models.

Series Design of Large-Scale NC Machine Tool

Product system design is a mature concept in western developed countries. It has been applied in war industry during the last century. However,up until now,functional combination is still the main method for product system de-sign in China. Therefore,in terms of a concept of product generation and product interaction we are in a weak position compared with the requirements of global markets. Today,the idea of serial product design has attracted much attention in the design field and the definition of product generation as well as its parameters has already become the standard in serial product designs. Although the design of a large-scale NC machine tool is complicated,it can be further optimized by the precise exercise of object design by placing the concept of platform establishment firmly into serial product de-sign. The essence of a serial product design has been demonstrated by the design process of a large-scale NC machine tool.

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 break-downs.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.

Predictive maintenance and its applications in civil engineering structures:A review

Structural health monitoring and performance prediction are crucial for smart disaster mitigation and intelligent management of structures throughout their lifespan.Recent advancements in predictive maintenance strategies within the industrial manufacturing industry have inspired similar innovations in civil engineering,aiming to improve structural performance evaluation,damage diagnosis,and capacity prediction.This review delves into the framework of predictive maintenance and examines various existing solutions,focusing on critical areas such as data acquisition,condition monitoring,damage prognosis,and maintenance planning.Results from real-world applications of predictive maintenance in civil engineering,covering high-rise structures,deep foundation pits,and other infrastructure,are presented.The challenges of implementing predictive maintenance in civil engineering structures under current technology,such as model interpretability of data-driven methods and standards for predictive maintenance,are explored.Future research prospects within this area are also discussed.

Study of Remote Monitoring and Maintenance Guiding Technique Based on LabVIEW for Machining Centers

The virtual instruments （VIs）, as a new type of instrument based on computer, has many advanced attractive characteristics. This research is based on Vls, and brings condition monitoring and knowledge-based maintenance support together through an integrated （including hate.met, ASP. NET, XML tochnique, Vls） network environme~. Within the enviromnent, machining centers operators, engineers or managers can share real-time data through the browser-based interface and minimize machining centers downtime by providing status monitoring and remote maintenance guiding from service centers.

Relevance of sleep for wellness:New trends in using artificial intelligence and machine learning

Sleep and well-being have been intricately linked,and sleep hygiene is paramount for developing mental well-being and resilience.Although widespread,sleep disorders require elaborate polysomnography laboratory and patient-stay with sleep in unfamiliar environments.Current technologies have allowed various devices to diagnose sleep disorders at home.However,these devices are in various validation stages,with many already receiving approvals from competent authorities.This has captured vast patient-related physiologic data for advanced analytics using artificial intelligence through machine and deep learning applications.This is expected to be integrated with patients’Electronic Health Records and provide individualized prescriptive therapy for sleep disorders in the future.

Application of Machine Learning in Electronic Device Fault Diagnosis

As electronic devices become increasingly complex, traditional fault diagnosis methods face significant challenges. Machine learning technologies offer new opportunities and solutions for electronic device fault diagnosis. This paper explores the application of machine learning in electronic device fault diagnosis, focusing on common machine learning algorithms, data preprocessing techniques, and diagnostic model construction methods. Case study analysis elucidates the advantages of machine learning in improving diagnostic accuracy, reducing diagnosis time, and implementing predictive maintenance. Research indicates that machine learning techniques can effectively enhance the efficiency and precision of electronic device fault diagnosis, providing robust support for device reliability and maintenance strategy optimization. In the future, as artificial intelligence technology further develops, machine learning will play an increasingly important role in the field of electronic device fault diagnosis.

Numerical simulation of freezing effect and tool change of shield machine with a frozen cutterhead

A shield machine with freezing function is proposed in order to realize tool change operation at atmospheric pressure. Furthermore, the transformation project of freezing cutterhead and tool change maintenance method are put forward. Taking the shield construction of Huanxi Power Tunnel as an example, a numerical analysis of the freezing cutter head of the project was carried out. The results show that when the brine temperature is-25 °C, after 30 d of freezing, the thickness of the frozen wall can reach 0.67 m and the average temperature drops to-9.9 °C. When the brine temperature is-30 °C, after 50 d of freezing, the thickness of the frozen wall can reach 1.01 m and the average temperature drops to-12.4 °C. If the thickness of the frozen wall is 0.5 m and the average temperature is-10 °C, as the design index of the frozen wall, the brine temperature should be lower than-28 °C to meet the excavation requirements in 30 d. Analyzing the frozen wall stress under 0.5 m thickness and-10 °C average temperature condition, the tensile safety factor and compressive safety factor are both greater than 2 at the most dangerous position, which can meet the tool change requirements for shield construction.

A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

Accelerated solution of the transmission maintenance schedule problem:a Bayesian optimization approach

To maximize the maintenance willingness of the owner of transmission lines,this study presents a transmission maintenance scheduling model that considers the energy constraints of the power system and the security constraints of on-site maintenance operations.Considering the computational complexity of the mixed integer programming(MIP)problem,a machine learning(ML)approach is presented to solve the transmission maintenance scheduling model efficiently.The value of the branching score factor value is optimized by Bayesian optimization(BO)in the proposed algorithm,which plays an important role in the size of the branch-and-bound search tree in the solution process.The test case in a modified version of the IEEE 30-bus system shows that the proposed algorithm can not only reach the optimal solution but also improve the computational efficiency.

Detecting Design Patterns in Object-Oriented Program Source Code by Using Metrics and Machine Learning

Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of object-oriented programs can be improved. There are automated detection techniques;however, many existing techniques are based on static analysis and use strict conditions composed on class structure data. Hence, it is difficult for them to detect and distinguish design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. To solve these problems in existing techniques, we propose a design pattern detection technique using source code metrics and machine learning. Our technique judges candidates for the roles that compose design patterns by using machine learning and measurements of several metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. As a result of experimental evaluations with a set of programs, we confirmed that our technique is more accurate than two conventional techniques.

Large-scale functional connectivity predicts cognitive impairment related to type 2 diabetes mellitus

BACKGROUND Large-scale functional connectivity(LSFC)patterns in the brain have unique intrinsic characteristics.Abnormal LSFC patterns have been found in patients with dementia,as well as in those with mild cognitive impairment(MCI),and these patterns predicted their cognitive performance.It has been reported that patients with type 2 diabetes mellitus(T2DM)may develop MCI that could progress to dementia.We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM,using connectome-based predictive modeling(CPM)and a support vector machine.AIM To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.METHODS Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls.Cognitive function was assessed using the Montreal Cognitive Assessment(MoCA).Patients with T2DM were divided into two groups,according to the presence(T2DM-C;n=16)or absence(T2DM-NC;n=26)of MCI.Brain regions were marked using Harvard Oxford(HOA-112),automated anatomical labeling(AAL-116),and 264-region functional(Power-264)atlases.LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique.Subsequently,we used a support vector machine based on LSFC patterns for among-group differentiation.The area under the receiver operating characteristic curve determined the appearance of the classification.RESULTS CPM could predict the MoCA scores in patients with T2DM(Pearson’s correlation coefficient between predicted and actual MoCA scores,r=0.32,P=0.0066[HOA-112 atlas];r=0.32,P=0.0078[AAL-116 atlas];r=0.42,P=0.0038[Power-264 atlas]),indicating that LSFC patterns represent cognition-level measures in these patients.Positive(anti-correlated)LSFC networks based on the Power-264 atlas showed the best predictive performance;moreover,we observed new brain regions of interest associated with T2DM-related cognition.The area under the receiver operating characteristic curve values(T2DM-NC group vs.T2DM-C group)were 0.65-0.70,with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value(0.70).Most discriminative and attractive LSFCs were related to the default mode network,limbic system,and basal ganglia.CONCLUSION LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.

Single machine scheduling with semi-resumable machineavailability constraints

This paper considers the semi-resumable model of single machine scheduling with anon-availability period. The machine is not available for processing during a given time interval.A job cannot be completed before the non-availability period will have to partially restartafter the machine has become available again. For the problem with objective of minimizingmakespan, the tight worst-case ratio of algorithm LPT is given, and an FPTAS is also proposed.For the problem with objective of minimizing total weighted completion time, an approximationalgorithm with worst-case ratio smaller than 2 is presented. Two special cases of the latterproblem are also considered, and improved algorithms are given.

Learning to branch in the generation maintenance scheduling problem

To maximize the reliability index of a power system,this study modeled a generation maintenance scheduling problem that considers the network security constraints and rationality constraints of the generation maintenance practice in a power system.In view of the computational complexity of the generation maintenance scheduling model,a variable selection method based on a support vector machine(SVM)is proposed to solve the 0-1 mixed integer programming problem(MIP).The algorithm observes and collects data from the decisions made by strong branching(SB)and then learns a surrogate function that mimics the SB strategy using a support vector machine.The learned ranking function is then used for variable branching during the solution process of the model.The test case showed that the proposed variable selection algorithm-based on the features of the proposed generation maintenance scheduling problem during branch-and-bound-can increase the solution efficiency of the generation-scheduling model on the premise of guaranteed accuracy.