Maintenance Platform Research Articles

Maintenance factory platform for in-space manufacturing: Conceptualizing design architecture

Commerce, long-term habitation, and far-location explorations are the objectives of the next space era, also called Space 2.0. These objectives are the focus of in-space servicing, assembly, and manufacturing (ISAM) research. Current research in this area is focused on demonstrating various manufacturing processes under microgravity using the International Space Station (ISS) as a platform and relies on an Earth-based supply chain. Building operational infrastructure and supply chains enabling in-space manufacturing is critical for the sustained growth of ISAM to meet the goals of Space 2.0. This paper is specifically focused on discussing the need and potential architecture for a maintenance factory in space to enable servicing, maintenance, and repairs in space environments. Space presents a hostile environment and a new set of challenges to establish a robust infrastructure necessary for supporting these objectives. The realization of such an infrastructure, including physical and digital footprints, is demanding new manufacturing science and engineering tools and platforms for maintenance. The authors present requirements of a robust maintenance platform, called a “factory in space” to deliver multi-functional maintenance service station(s) across various operational points in space such as low-earth orbit, lunar surface, and Martian surface. Such factories are projected to be an essential part of the in-space infrastructure for short as well as long-term commerce, habitation, and exploration. A critical analysis of this concept is proposed through an analysis of the state of the art, boundary conditions in space and requirements of maintenance, the role of manufacturing, and the design as well as sustainability considerations for a maintenance factory in space.

Elevating Smart Manufacturing with a Unified Predictive Maintenance Platform: The Synergy between Data Warehousing, Apache Spark, and Machine Learning.

The transition to smart manufacturing introduces heightened complexity in regard to the machinery and equipment used within modern collaborative manufacturing landscapes, presenting significant risks associated with equipment failures. The core ambition of smart manufacturing is to elevate automation through the integration of state-of-the-art technologies, including artificial intelligence (AI), the Internet of Things (IoT), machine-to-machine (M2M) communication, cloud technology, and expansive big data analytics. This technological evolution underscores the necessity for advanced predictive maintenance strategies that proactively detect equipment anomalies before they escalate into costly downtime. Addressing this need, our research presents an end-to-end platform that merges the organizational capabilities of data warehousing with the computational efficiency of Apache Spark. This system adeptly manages voluminous time-series sensor data, leverages big data analytics for the seamless creation of machine learning models, and utilizes an Apache Spark-powered engine for the instantaneous processing of streaming data for fault detection. This comprehensive platform exemplifies a significant leap forward in smart manufacturing, offering a proactive maintenance model that enhances operational reliability and sustainability in the digital manufacturing era.

Digital twin-driven intelligent operation and maintenance platform for large-scale hydro-steel structures

Large-scale hydro-steel structures (LS-HSS) are pivotal in hydraulic engineering, boasting high lift, expansive apertures, and discharge capacities. Conventional methods of operation and maintenance for LS-HSS require a digital revolution to ensure safety, reliability, and durability. This study introduces a novel approach, harnessing the power of a digital twin (DT) to integrate physical and virtual elements in LS-HSS operation and maintenance seamlessly. A digital twin modeling framework of LS-HSS operation and maintenance is first presented, incorporating five levels of a physical entity, a virtual entity, DT data, services, and connections. Subsequently, a DT-driven intelligent operation and maintenance (DT-IOM) platform is constructed and applied, which features real-time mapping, closed-loop control, dynamic health assessment, and intelligent decision-making. This platform provides a pathway for digitally transforming conventional LS-HSS operation and maintenance and paves the way for a new era of intelligent infrastructure management. The proposed approach has been successfully implemented and validated with multiple engineering projects, including radial gates of reservoir spillways, plain gates of flood discharge orifices, and emergency gates of pumped storage power stations. The impressive results demonstrate a significant anomaly response and processing efficiency improvement of over 60%. Moreover, the platform has substantially reduced field inspection time and equipment failure rate by approximately 50% and 20%, respectively. These tangible benefits further highlight the platform’s functional advantages, including its capability for IoT connection, modeling and integration, virtual-real interaction, 2D/3D visualization, and service expansion. The results demonstrate the unique features in the closed loop of perception, analysis, decision-making, and optimization for DT-IOM, especially for integrating data, models, knowledge, and services in LS-HSS intelligent operation and maintenance.

Intelligent Detection and Analysis of Software Vulnerabilities based on Encryption Algorithms and Feature Extraction

Implement status detection of ship software, identify the source of faults in problematic software, and release new software versions. Based on the above requirements, the author regards the detection and control of ship software status as the core research content. Based on the actual operating environment of ship software, the functional requirements of software status detection were studied and analyzed, and a set of ship software status detection was designed and implemented, a software inspection and maintenance platform that integrates ship software operation and maintenance, as well as ship software version release and update. The author conducted practical verification of the SM3 and SM2 hybrid encryption algorithm and selected software on the ship for detection. After analyzing the experimental results, it has been proven that using a hybrid algorithm for encryption and decryption, the server can accurately obtain software information on the ship's platform, detect the software status on the ship, and locate specific problem files. For software that does not meet the standard status, the server can accurately transmit software information to the ``component integration framework'' and put the component in a ``prohibited'' scheduling state. After the server repairs the problematic software, the detection results of the software change and display as legal, while the software is in the ``allowed'' scheduling state in the ``component integration framework''.

Research on the method of digital twin operation and maintenance platform for intelligent early warning of wind turbine tower

Wind power generators have a complex structure and operate in harsh environments, where working conditions are highly variable. As a result, the operation and maintenance of wind turbines face numerous challenges. In response to the need for the development of wind power operation and maintenance informatization, it is necessary to satisfy the requirements for multi-party collaborative monitoring to ensure the long-term safe and reliable operation of wind turbines. In this paper,we proposed a method for building an intelligent early-warning digital twin platform focused on the simulation of wind turbines and tower components. The platform construction method proposed in this article is based on the Web and from the perspective of intelligent operation and maintenance of wind turbines. It establishes a warning model for tower agent simulation and vibration signal time series prediction. The tower mechanism model is established based on the operating data set of a 4MW wind turbine at Shanghai Electric. Different physical responses of the tower under different wind speeds are simulated, and an agent model using LSTM and decision tree models is established for predictive analysis. To account for uncertainty, a Bayesian-LSTM model is established to warn against predictive errors. Finally, a data-driven digital twin wind turbine platform is achieved on the Web.

Application of big data technology in electromechanical operation and maintenance intelligent platform

Abstract Aiming at the data preprocessing requirements and label data cost issues arising from the intelligent operation and maintenance of electromechanical equipment, this article mainly studies structured data cleaning methods and fault prediction algorithms for a small number of label samples. First, this article introduces the overall architecture of the intelligent operation and maintenance system for electromechanical equipment. Second, based on the electromechanical equipment operation and maintenance data access service, data cleaning, and fault prediction, this article constructs an electromechanical equipment intelligent operation and maintenance platform based on Kafka message queue, Spark cluster, and other components, and introduces the functional composition of the system in detail. Finally, the article describes the functions of each component of data access service, data cleaning, and fault prediction in detail. To address the cost issue associated with sufficient labeled sample data for data analysis, we propose a semi-supervised learning algorithm, IF-GBDT, based on improved independent forests and Gradient Boosting Decision Tree. The independent forest algorithm supplements labels for unlabeled data based on the learning results of a small number of labeled samples. We also use the gradient lifting tree algorithm to train the model based on the new tag data set for fault prediction, thereby reducing the impact of lack of tags on the accuracy of the prediction model. Experiments show that this method improves classification accuracy and has good adaptability and concurrency performance for a small number of tags.

Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0

Possessing an efficient production line relies heavily on the availability of the production equipment. Thus, to ensure that the required function for critical equipment is in compliance, and unplanned downtime is minimized, succeeding with the field of maintenance is essential for industrialists. With the emergence of advanced manufacturing processes, incorporating predictive maintenance capabilities is seen as a necessity. Another field of interest is how modern value chains can support the maintenance function in a company. Accessibility to data from processes, equipment and products have increased significantly with the introduction of sensors and Industry 4.0 technologies. However, how to gather and utilize these data for enabling improved decision making within maintenance and value chain is still a challenge. Thus, the aim of this paper is to investigate on how maintenance and value chain data can collectively be used to improve value chain performance through prediction. The research approach includes both theoretical testing and industrial testing. The paper presents a novel concept for a predictive maintenance platform, and an artificial neural network (ANN) model with sensor data input. Further, a case of a company that has chosen to apply the platform, with the implications and determinants of this decision, is also provided. Results show that the platform can be used as an entry-level solution to enable Industry 4.0 and sensor data based predictive maintenance.

Construction of medical equipment maintenance network management platform based on big data

Introduction: Medical equipment is an indispensable part of hospitals. It is the basic condition and guarantee for the hospital to carry out medical services, scientific research, teaching, and other activities, and it plays an irreplaceable role in the entire medical process of the hospital. Therefore, the maintenance management of equipment is also the focus of our attention. In the past, traditional management methods such as paper were mainly used for equipment maintenance management, and it was difficult to share data.Methods: In today’s era of rapid development of information technology, we will use information technology to maintain and manage medical equipment. Through big data analysis and other technologies, the drawbacks of the existing traditional management methods are improved, so that medical equipment can be managed scientifically. By maximizing its functions, it can ensure the normal operation of medical facilities, improve the utilization rate and integrity rate of equipment, and reduce maintenance costs and unnecessary losses.Results: According to the research findings, based on the background of big data, a de-Bayesian network is used for data mining to build a medical equipment maintenance platform. Through the data in the platform, we can better discover the distribution and reasons of equipment maintenance, and at the same time conduct an analysis to provide reference for the formulation of preventive maintenance plans, reduce equipment failure rate and maintenance costs and improve equipment utilization. Through the survey of medical staff, we can also find that at least 40% of the people feel that the work distribution is more reasonable, and 45% of the people feel that the equipment failure rate and the time required for maintenance have been greatly reduced.Discussion: We can see that the network platform for medical equipment maintenance management built through big data is very feasible, which can help us work more effectively and improve work efficiency.

Service fault diagnosis method of information operation and maintenance platform based on Gaussian kernel function

Service fault diagnosis method of information operation and maintenance platform based on Gaussian kernel function

Research and application of equipment intelligent operation and maintenance platform

The intellectualization of equipment operation and maintenance is one of the main development directions under the background of intelligent manufacturing. Based on lean theory and an intelligent algorithm, this paper establishes the equipment hierarchical management model and equipment intelligent operation and maintenance framework, introduces the lean management strategy and equipment operation state prediction algorithm in the operation and maintenance framework in detail, and finally realizes the design and development of equipment intelligent operation and maintenance platform. The platform has been proved in a steel structure manufacturing enterprise that it can effectively realize equipment condition monitoring, fault diagnosis, and performance prediction, and has a significant effect in reducing accidental shutdown loss and improving machine utilization.

Study on the Hydraulic and Energy Loss Characteristics of the Agricultural Pumping Station Caused by Hydraulic Structures

The pumping station is an important part of the agricultural irrigation and drainage system. The sump is one of the common water inlet types of agricultural pumping stations. In the sump, to facilitate the installation and maintenance of equipment, some hydraulic structures, such as pump beams, maintenance platforms and chest walls, are added to the sump. At present, the impact of hydraulic structures in the sump on the hydraulic performance of the pump device is not clear, so this paper focused on the impact of hydraulic structures on the hydraulic characteristics and entropy generation characteristics of the pump device by using numerical simulation methods. The results showed that the installation of hydraulic structures in the sump has the greatest impact on the efficiency of the pump device. The efficiency coefficient increased after adding a pump beam in the sump and decreased by about 2% after adding a maintenance platform and a water retaining chest wall. Results also showed that the installation of hydraulic structures in the sump will lead to uneven distribution of entropy generation in the sump, especially in the vicinity of the hydraulic structures. The installation of the maintenance platform and chest wall will lead to the increase of the total entropy generation in the sump, which also means that the hydraulic loss in the sump will increase accordingly. Hence, in addition to the pump beam, other structures should be avoided in the sump.

Harvester Maintenance Resource Scheduling Optimization, Based on the Combine Harvester Operation and Maintenance Platform

The combine harvester is the main machine for fieldwork during the harvest season. When the harvester fails and cannot continue to work, this indirectly affects the harvest time and the yield in the field. The emergency maintenance service of agricultural machinery can be optimized through the dynamic planning of harvester maintenance tasks, using the operation and maintenance platform. According to the scene, a priority scheme for the operation and maintenance tasks, based on the improved Q-learning algorithm, was proposed. The continuous approximation capability of the model was improved by using the BP neural network algorithm and the Q function value, in iterations, was updated continuously. At the same time, the improved TOPSIS method, based on Mahalanobis distance, was used to calculate the closeness of each harvester maintenance task, so as to determine the priority of the equipment maintenance tasks. An operation and maintenance service platform for combine harvesters was developed based on the B/S architecture, with the goal of minimizing the operation and maintenance costs and improving the tasks’ complete efficiency. In this research process, dynamic scheduling rules were formulated. Operation and maintenance resources were optimized and rationally allocated through dynamic optimization scheduling methods, and feasible solution information was generated from the operation and maintenance service platform. Finally, the actual data from the enterprise were used for verification and analysis. The verification showed the following: through a comparison of algorithm performance, it was seen that the improved BP-Q-Learning algorithm can quickly find the operation and maintenance scheduling scheme in the maintenance scheduling; the priority rules can improve the efficiency of task execution, to a certain extent; the cost of the tasks’ execution can be significantly reduced; and the maintenance distance can be shortened. This research has reference significance for the formulation and optimization of agricultural machinery maintenance for cross-regional operations.

On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges

In the era of the fourth industrial revolution, several concepts have arisen in parallel with this new revolution, such as predictive maintenance, which today plays a key role in sustainable manufacturing and production systems by introducing a digital version of machine maintenance. The data extracted from production processes have increased exponentially due to the proliferation of sensing technologies. Even if Maintenance 4.0 faces organizational, financial, or even data source and machine repair challenges, it remains a strong point for the companies that use it. Indeed, it allows for minimizing machine downtime and associated costs, maximizing the life cycle of the machine, and improving the quality and cadence of production. This approach is generally characterized by a very precise workflow, starting with project understanding and data collection and ending with the decision-making phase. This paper presents an exhaustive literature review of methods and applied tools for intelligent predictive maintenance models in Industry 4.0 by identifying and categorizing the life cycle of maintenance projects and the challenges encountered, and presents the models associated with this type of maintenance: condition-based maintenance (CBM), prognostics and health management (PHM), and remaining useful life (RUL). Finally, a novel applied industrial workflow of predictive maintenance is presented including the decision support phase wherein a recommendation for a predictive maintenance platform is presented. This platform ensures the management and fluid data communication between equipment throughout their life cycle in the context of smart maintenance.

Design and Implementation of the Remote Operation and Maintenance Platform for the Combine Harvester

To meet the needs of remotely operating and maintaining combine harvesters in view of the problems of extensive operation, inefficient operation and maintenance, and backward management of combine harvesters, a remote operation and maintenance platform for the combine harvester has been designed. The platform mainly includes modules, such as data monitoring, fault prediction, fault diagnosis, and comprehensive operation maintenance. Among them, the data-monitoring module obtains the monitoring data of the vehicle terminal. It actively transmits to the remote operation and maintenance platform to remotely monitor the operation data or status of the combine harvester system. The fault prediction module builds a fault prediction model based on the fault features and data provided by all previous stages. It predicts and evaluates the actual operation of each component of the combine harvester in the foreseeable future, Based on the fault diagnosis model, the fault diagnosis module evaluates and classifies the current abnormal state of the combine harvester. The comprehensive operation and maintenance module formulates the corresponding maintenance and repair strategies according to the output results of the fault diagnosis and prediction. Through functional field testing of the platform, the results showed that the platform could perform real-time monitoring, fault prediction, fault diagnosis, and comprehensive operation maintenance for the operation information of the combine harvester. It helped to improve the intelligent operation level, management quality and operation, and maintenance efficiency of the combine harvester.

Digital Twin-driven approach to improving energy efficiency of indoor lighting based on computer vision and dynamic BIM

Intelligent lighting systems and surveillance systems have become an important part of intelligent buildings. However, the current intelligent lighting system generally adopts independent sensor control and does not perform multi-source heterogeneous data fusion with other digital systems. This paper fully considers the linkage between the lighting system and the surveillance system and proposes a digital twin lighting (DTL) system that mainly consists of three parts. Firstly, a visualized operation and maintenance (VO&M) platform for a DTL system was established based on dynamic BIM. Secondly, the environment perception, key-frame similarity judgment, and multi-channel key-frame cut and merge mechanism were utilized to preprocess the video stream of the surveillance system in real-time. Lastly, pedestrians detected using YOLOv4 and the ambient brightness perceived by the environment perception mechanism were transmitted to the cloud database and were continuously read by the VO&M platform. The intent here was to aid timely adaptive adjustment of the digital twin and realistic lighting through the internet. The effectiveness of the proposed method was verified by experimenting with a surveillance video stream for 14 days. The key results of the experiments are as follows: (1) the accuracy rate of intelligent decision control reached 95.15%; (2) energy consumption and electricity costs were reduced by approximately 79%; and (3) the hardware cost and energy consumption of detection equipment and the time and cost of operation and maintenance (O&M) were greatly reduced.

Research on Comprehensive Operation and Maintenance Based on the Fault Diagnosis System of Combine Harvester

In view of the difficulty in diagnosing and discriminating fault conditions during the operation of combine harvesters, difficulty in real-time processing of health status, and low timeliness of fault processing, a comprehensive operation and maintenance platform for combine harvesters was developed in this study which realized the functions of data monitoring and the full operation and maintenance of a combine harvester. At the same time, through the comprehensive operation and maintenance platform, the harvester information was obtained in real-time, the diagnosis results were obtained, and the maintenance service was effectively carried out through the platform. The IPSO-SVM fault diagnosis algorithm was proposed, and the performance of the fault diagnosis of the combine harvester was verified by the simulation test. The experimental verification showed that the system met the requirements of remote monitoring of combine harvesters, and the prediction accuracy of this method was 97.96%. Compared with SVM (87.51%), GA-SVM (89.44%), and PSO-SVM (92.56%), this system had better generalization ability and effectively improved the management level of the comprehensive operation and maintenance of the combine harvester. A theoretical basis and technical reference will be provided for the follow-up research for the comprehensive operation and maintenance platform of the combine harvester in this paper.

Construction and Implementation of Hospital Automation Operation and Maintenance Platform

Construction and Implementation of Hospital Automation Operation and Maintenance Platform

Research and Discussion on Big Data Operation and Maintenance Platform of Railway Passenger Security Inspection System

Research and Discussion on Big Data Operation and Maintenance Platform of Railway Passenger Security Inspection System

Service fault diagnosis method of information operation and maintenance platform based on Gaussian kernel function

Service fault diagnosis method of information operation and maintenance platform based on Gaussian kernel function

Development of a flexible predictive maintenance system in the context of Industry 4.0

Maintenance activities have changed over the recent years, through the digitalization of the field and the application of tools and concepts from Industry 4.0. By connecting and communicating with the production system, companies are now able to create knowledge about its current and future health state, allowing more and more efficient control over the equipment. This approach is called predictive maintenance, and its goal is to reduce unplanned downtimes and organize efficiently maintenance actions before failures and stoppages appear. However, to reach such performance, it is still quite challenging for the industrial actor to implement an Intelligent Maintenance System that will help with the data management. Therefore, this paper presents the approach that was used to develop and implement a predictive maintenance platform in the automotive industry context. This platform is built on open standards, and scalable regarding the different needs for data collect, storage, visualization, and analyses.