Data-driven decision-making for equipment maintenance

Ma, Zhiliang; Ren, Yuan; Xiang, Xinglei; Turk, Žiga

doi:10.1016/j.autcon.2020.103103

Cited by 70 publications

(34 citation statements)

References 28 publications

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“…A knowledge gap related to time-dependent sensor data in building information models was addressed through studies utilizing such techniques as Domain-Specific Language (DSL) to reduce human effort in this process or coupling BIM model with a smart contract [ 46 , 47 ]. The combination of BIM and Internet of Things technology provided dynamic data transmission and appropriate data format to show the feasibility of this method in enhancing safety and quality in structures or equipment maintenance [ 48 , 49 , 50 , 51 , 52 , 53 ].…”

Section: Content Analysismentioning

confidence: 99%

Application of Building Information Modelling (BIM) in the Health Monitoring and Maintenance Process: A Systematic Review

Panah

Kioumarsi

2021

Sensors

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Improvements in the science of health monitoring and maintenance have facilitated the observation of damage and defects in existing structures and infrastructures, such as bridges and railways. The need to extend sensing technology through the use of wireless sensors as well as the lack of description tools for understanding, visualizing, and documenting sensor outputs has encouraged researchers to use powerful tools such as Building Information Modelling (BIM) systems. BIM has become important because of conducting tools widely used in the Architecture, Engineering, and Construction (AEC) industry to present and manage information on structural systems and situations. Since combining health monitoring and maintenance results with BIM models is a new field of study, and most projects utilize various aspects of it, we have conducted a review of important work related to this subject published from 2010 to November of 2020. After reviewing 278 journal articles, research trends, approaches, methods, gaps, and future agenda related to BIM in monitoring and maintenance were highlighted. This paper, through a bibliometric and content analysis, concludes that besides main improvements, some limitations now exist which affect the modeling and maintenance process. These limitations are related to extending the IFC schema, optimizing sensor data, interoperability among various BIM platforms, optimization of various sensing technologies for fault detection and management of huge amounts of data, besides consideration of environmental effects on monitoring hazards and underground objects. Finally, this paper aims to help to solve the mentioned limitation through a comprehensive review of existing research.

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Section: Content Analysismentioning

confidence: 99%

Application of Building Information Modelling (BIM) in the Health Monitoring and Maintenance Process: A Systematic Review

Panah

Kioumarsi

2021

Sensors

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“…In equipment maintenance, for example, a data-driven system is developed for decision-making on equipment maintenance. The system implements quantitative decision-making models and integrates Building Information Modeling (BIM) and Geographic Information Systems (GIS) to support the data acquisition and update [ 8 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Chou

Liu

2021

Sensors

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Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel’s work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

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“…Reference [10] proposes a method to construct the component-failure mode matrix automatically by mining unstructured and short quality problem texts and mapping as well as representing them as component-failure knowledge. Reference [11] proposed a data-driven approach and a system design based on the approach for decision-making on equipment maintenance. The RCM process was improved to become data-driven to support the data requirements for the data-driven RCM process; a system framework for the proposed approach was established, and a prototype system was built and verified with end users.…”

Section: Literature Reviewmentioning

confidence: 99%

Study on Preventive Maintenance Strategies of Filling Equipment Based on Reliability-Cantered Maintenance

2021

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In order to ensure normal operation of enterprise production activities and enhance the competitiveness of enterprise, equipment management and maintenance strategy formulation has always been one of important contents of daily management of enterprise. According to the actual requirement of a Chinese beer production enterprise, preventive maintenance strategy of filling equipment is put forward based on reliability-centred maintenance (RCM). Firstly, on the basis of analyzing RCM theory and equipment maintenance, the general process of failure analysis of beer production equipment is presented. Secondly, the general production process of bottled beer is analyzed, and the composition of major filling equipment is also introduced in the beer production line. With the help of key indicators of equipment reliability, such as mean time between failure (MTBF), mean time to repair restoration (MTTR) and availability Ai, the fault analysis of filling production line is carried out, and the relevant results are calculated. Then, process failure mode and effect analysis (PFMEA) of filling machine is implemented, and fault tree analysis (FTA) of potential failure modes with high risk priority numbers is also completed. Finally, preventive and maintenance strategies of filling equipment are established on the basis of RCM. Through the research in this paper, maintenance costs and unplanned breakdown hours can be significantly reduced.

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Data-driven decision-making for equipment maintenance

Cited by 70 publications

References 28 publications

Application of Building Information Modelling (BIM) in the Health Monitoring and Maintenance Process: A Systematic Review

Application of Building Information Modelling (BIM) in the Health Monitoring and Maintenance Process: A Systematic Review

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Study on Preventive Maintenance Strategies of Filling Equipment Based on Reliability-Cantered Maintenance

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