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16 pages, 7388 KiB

Open AccessArticle

by Jiancheng Yin, Yuqing Li, Rixin Wang and Minqiang Xu

Appl. Sci. 2021, 11(22), 10968; https://doi.org/10.3390/app112210968 - 19 Nov 2021

Viewed by 1145

With the complexity of the task requirement, multiple operating conditions have gradually become the common scenario for equipment. However, the degradation trend of monitoring data cannot be accurately extracted in life prediction under multiple operating conditions, which is because some monitoring data is [...] Read more.

With the complexity of the task requirement, multiple operating conditions have gradually become the common scenario for equipment. However, the degradation trend of monitoring data cannot be accurately extracted in life prediction under multiple operating conditions, which is because some monitoring data is affected by the operating conditions. Aiming at this problem, this paper proposes an improved similarity trajectory method that can directly use the monitoring data under multiple operating conditions for life prediction. The morphological pattern and symbolic aggregate approximation-based similarity measurement method (MP-SAX) is first used to measure the similarity between the monitoring data under multiple operating conditions. Then, the similar life candidate set, and corresponding weight are obtained according to the MP-SAX. Finally, the life prediction results of equipment under multiple operating conditions can be calculated by aggregating the similar life candidate set. The proposed method is validated by the public datasets from NASA Ames Prognostics Data Repository. The results show that the proposed method can directly and effectively use the original monitoring data for life prediction without extracting the degradation trend of the monitoring data. Full article

(This article belongs to the Special Issue Maintenance 4.0 Technologies for Sustainable Manufacturing)

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20 pages, 3393 KiB

Open AccessArticle

A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

by Corbinian Nentwich and Gunther Reinhart

Appl. Sci. 2021, 11(21), 10403; https://0-doi-org.brum.beds.ac.uk/10.3390/app112110403 - 5 Nov 2021

Cited by 5 | Viewed by 2355

Abstract

Conditions monitoring of industrial robot gears has the potential to increase the productivity of highly automated production systems. The huge amount of health indicators needed to monitor multiple gears of multiple robots requires an automated system for anomaly and trend detection. In this [...] Read more.

Conditions monitoring of industrial robot gears has the potential to increase the productivity of highly automated production systems. The huge amount of health indicators needed to monitor multiple gears of multiple robots requires an automated system for anomaly and trend detection. In this publication, such a system is presented and suitable anomaly detection and trend detection methods for the system are selected based on synthetic and real world industrial application data. A statistical test, namely the Cox-Stuart test, appears to be the most suitable approach for trend detection and the local outlier factor algorithm or the long short-term neural network performs best for anomaly detection in the application of industrial robot gear condition monitoring in the presented experiments. Full article

(This article belongs to the Special Issue Maintenance 4.0 Technologies for Sustainable Manufacturing)

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22 pages, 7260 KiB

Open AccessArticle

Bi-Objective Optimization for Industrial Robotics Workflow Resource Allocation in an Edge–Cloud Environment

by Xingju Xie, Xiaojun Wu and Qiao Hu

Appl. Sci. 2021, 11(21), 10066; https://0-doi-org.brum.beds.ac.uk/10.3390/app112110066 - 27 Oct 2021

Cited by 1 | Viewed by 1694

Abstract

The application scenarios and market shares of industrial robots have been increasing in recent years, and with them comes a huge market and technical demand for industrial robot-monitoring system (IRMS). With the development of IoT and cloud computing technologies, industrial robot monitoring has [...] Read more.

The application scenarios and market shares of industrial robots have been increasing in recent years, and with them comes a huge market and technical demand for industrial robot-monitoring system (IRMS). With the development of IoT and cloud computing technologies, industrial robot monitoring has entered the cloud computing era. However, the data of industrial robot-monitoring tasks have characteristics of large data volume and high information redundancy, and need to occupy a large amount of communication bandwidth in cloud computing architecture, so cloud-based IRMS has gradually become unable to meet its performance and cost requirements. Therefore, this work constructs edge–cloud architecture for the IRMS. The industrial robot-monitoring task will be executed in the form of workflow and the local monitor will allocate computing resources for the subtasks of the workflow by analyzing the current situation of the edge–cloud network. In this work, the allocation problem of industrial robot-monitoring workflow is modeled as a latency and cost bi-objective optimization problem, and its solution is based on the evolutionary algorithm of the heuristic improvement NSGA-II. The experimental results demonstrate that the proposed algorithm can find non-dominated solutions faster and be closer to the Pareto frontier of the problem. The monitor can select an effective solution in the Pareto frontier to meet the needs of the monitoring task. Full article

(This article belongs to the Special Issue Maintenance 4.0 Technologies for Sustainable Manufacturing)

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18 pages, 10959 KiB

Open AccessArticle

Method of Selecting the Means of Transport of the Winning, Taking into Account Environmental Aspects

by Arkadiusz Kowalski and Robert Waszkowski

Appl. Sci. 2021, 11(12), 5512; https://0-doi-org.brum.beds.ac.uk/10.3390/app11125512 - 14 Jun 2021

Viewed by 1579

Abstract

The transport of the winning in deep mines, using the room and pillar mining system, is most often performed with bucket loaders and haul trucks. In the era of attempts to stop rapid climate change, it is crucial to choose the transport means [...] Read more.

The transport of the winning in deep mines, using the room and pillar mining system, is most often performed with bucket loaders and haul trucks. In the era of attempts to stop rapid climate change, it is crucial to choose the transport means for the winning both in terms of efficiency and cost-effectiveness and to consider its environmental aspect. Permissible levels of pollutant emissions in exhaust gases are defined for this type of means of transport by the EU Stage Standards. There is a discernible need to develop a multi-criteria method supporting the decision-making process, which should reward loaders and haul trucks that meet more stringent emission standards. The article proposes an innovative idea of taking environmental aspects into account when selecting loaders and haul trucks for excavated material transport tasks, so that the amount of pollutants emitted by them in exhaust gases, e.g., the sum of hydrocarbons and nitrogen oxides (HC+NO_x), is also taken into consideration when assigning means of transport to particular tasks. Based on simulation studies for a specific case, it was found that a 20% reduction of HC+NO_x emission is possible with only a 2% increase in the transport costs of the winning. For this purpose, an objective function was used formulated on the basis of two criteria: minimization of the transport cost of the winning and the level of pollutant emissions in the exhaust gases. Since dozens of mining machines are operated continuously in deep mines of non-ferrous metal ores, the application of the proposed method would significantly reduce the emission of pollutants in the used air coming out of ventilation shafts. Full article

(This article belongs to the Special Issue Maintenance 4.0 Technologies for Sustainable Manufacturing)

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29 pages, 13574 KiB

Open AccessArticle

Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study

by Helge Nordal and Idriss El-Thalji

Appl. Sci. 2021, 11(8), 3487; https://0-doi-org.brum.beds.ac.uk/10.3390/app11083487 - 13 Apr 2021

Cited by 6 | Viewed by 2352

Abstract

The introduction of Industry 4.0 is expected to revolutionize current maintenance practices by reaching new levels of predictive (detection, diagnosis, and prognosis processes) and prescriptive maintenance analytics. In general, the new maintenance paradigms (predictive and prescriptive) are often difficult to justify because of [...] Read more.

The introduction of Industry 4.0 is expected to revolutionize current maintenance practices by reaching new levels of predictive (detection, diagnosis, and prognosis processes) and prescriptive maintenance analytics. In general, the new maintenance paradigms (predictive and prescriptive) are often difficult to justify because of their multiple inherent trade-offs and hidden systems causalities. The prediction models, in the literature, can be considered as a “black box” that is missing the links between input data, analysis, and final predictions, which makes the industrial adaptability to such models almost impossible. It is also missing enable modeling deterioration based on loading, or considering technical specifications related to detection, diagnosis, and prognosis, which are all decisive for intelligent maintenance purposes. The purpose and scientific contribution of this paper is to present a novel simulation model that enables estimating the lifetime benefits of an industrial asset when an intelligent maintenance management system is utilized as mixed maintenance strategies and the predictive maintenance (PdM) is leveraged into opportunistic intervals. The multi-method simulation modeling approach combining agent-based modeling with system dynamics is applied with a purposefully selected case study to conceptualize and validate the simulation model. Three maintenance strategies (preventive, corrective, and intelligent) and five different scenarios (case study data, manipulated case study data, offshore and onshore reliability data handbook (OREDA) database, physics-based data, and hybrid) are modeled and simulated for a time period of 20 years (175,200 h). Intelligent maintenance is defined as PdM leveraged in opportunistic maintenance intervals. The results clearly demonstrate the possible lifetime benefits of implementing an intelligent maintenance system into the case study as it enhanced the operational availability by 0.268% and reduced corrective maintenance workload by 459 h or 11%. The multi-method simulation model leverages and shows the effect of the physics-based data (deterioration curves), loading profiles, and detection and prediction levels. It is concluded that implementing intelligent maintenance without an effective predictive horizon of the associated PdM and effective frequency of opportunistic maintenance intervals, does not guarantee the gain of its lifetime benefits. Moreover, the case study maintenance data shall be collected in a complete (no missing data) and more accurate manner (use hours instead of date only) and used to continuously upgrade the failure rates and maintenance times. Full article

(This article belongs to the Special Issue Maintenance 4.0 Technologies for Sustainable Manufacturing)

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16 pages, 1728 KiB

Open AccessArticle

The Need for Ecosystem 4.0 to Support Maintenance 4.0: An Aviation Assembly Line Case

by Alessandro Giacotto, Henrique Costa Marques, Eduardo Afonso Pereira Barreto and Alberto Martinetti

Appl. Sci. 2021, 11(8), 3333; https://0-doi-org.brum.beds.ac.uk/10.3390/app11083333 - 8 Apr 2021

Cited by 5 | Viewed by 2291

Abstract

Manufacturing and assembling aircraft require hundreds of different machines for various process applications. The machines have different complexity and often different ages; however, they have to ensure a higher precision than other industrial fields. Recent technology advancement in maintenance approaches offers a wide [...] Read more.

Manufacturing and assembling aircraft require hundreds of different machines for various process applications. The machines have different complexity and often different ages; however, they have to ensure a higher precision than other industrial fields. Recent technology advancement in maintenance approaches offers a wide range of opportunities to provide performance and availability. The paper discusses how the maintenance technologies applicable to the various machines need to be appropriately supported by a production environment, called “ecosystem”, that allows their integration within the process and their synergy with the operators. (1) A background analysis of the aircraft production environment is offered. (2) A possible framework for designing a proper ecosystem 4.0 for integrating maintenance activities with design solutions and data gathering is provided. (3) A case study based on the assembly line of specific aircraft is adopted for testing the validity of the framework. (4) Finally, a discussion highlights the critical points of the research, underlying future work. Full article

(This article belongs to the Special Issue Maintenance 4.0 Technologies for Sustainable Manufacturing)

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18 pages, 18917 KiB

Open AccessArticle

How to Make Augmented Reality a Tool for Railway Maintenance Operations: Operator 4.0 Perspective

by Sara Scheffer, Alberto Martinetti, Roy Damgrave, Sebastian Thiede and Leo van Dongen

Appl. Sci. 2021, 11(6), 2656; https://0-doi-org.brum.beds.ac.uk/10.3390/app11062656 - 16 Mar 2021

Cited by 12 | Viewed by 3726

Abstract

In the last few decades, several initiatives and approaches are set up to support maintenance procedures for the railway industry in adopting the principles of Industry 4.0. Contextualized maintenance technologies such as Augmented Reality (AR) overlay can integrate virtual information on physical objects [...] Read more.

In the last few decades, several initiatives and approaches are set up to support maintenance procedures for the railway industry in adopting the principles of Industry 4.0. Contextualized maintenance technologies such as Augmented Reality (AR) overlay can integrate virtual information on physical objects to improve decision-making and action-taking processes. Operators work in a dynamic working environment requiring both high adaptive capabilities and expert knowledge. There is a need to support the operators with tailor-based information that is customized and contextualized to their expertise and experience. It calls for AR tools and approaches that combine complex methodologies with high usability requirements. The development of these AR tools could benefit from a structured approach. Therefore, the objective of this paper is to propose an adaptive architectural framework aimed at shaping and structuring the process that provides operators with tailored support when using an AR tool. Case study research is applied within a revelatory railway industry setting. It was found that the framework ensures that self-explanatory AR systems can capture the knowledge of the operator, support the operator during maintenance activities, conduct failure analysis, provide problem-solving strategies, and improve learning capabilities. This study contributes to the necessity of having a human-centered approach for the successful adaption of AR technology tools for the railway industry. Full article

(This article belongs to the Special Issue Maintenance 4.0 Technologies for Sustainable Manufacturing)

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13 pages, 4827 KiB

Open AccessArticle

Improve the Energy Efficiency of the Cooling System by Slide Regulating the Capacity of Refrigerator Compressors

by Javier Cárcel-Carrasco, Manuel Pascual-Guillamón and Fidel Salas-Vicente

Appl. Sci. 2021, 11(5), 2019; https://0-doi-org.brum.beds.ac.uk/10.3390/app11052019 - 25 Feb 2021

Cited by 3 | Viewed by 1669

Abstract

A fundamental part of the electric consumption of the main industries of the food sector comes from the refrigeration production, needed in all production phases. Therefore, every measure that aims to optimize the electric consumption and increase the efficiency of centralized industrial refrigeration [...] Read more.

A fundamental part of the electric consumption of the main industries of the food sector comes from the refrigeration production, needed in all production phases. Therefore, every measure that aims to optimize the electric consumption and increase the efficiency of centralized industrial refrigeration systems will help the energetic waste of the company, improving reliability and maintenance. Acting on the regulation of capacity of power compressors used can be a good way to save energy. This article shows a case studied by the authors in an industrial company in the meat industry in Spain, where the refrigeration systems have a great importance in the productive process. It displays the methodology used, the description of the taken actions and the results obtained. These combined measures brought about an improvement, with an energetic saving value reaching 400 MWh per year, leading to an equivalent in CO₂ emission reduction of 147.9 tons_. Full article

(This article belongs to the Special Issue Maintenance 4.0 Technologies for Sustainable Manufacturing)

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20 pages, 6340 KiB

Open AccessArticle

WARM: Wearable AR and Tablet-Based Assistant Systems for Bus Maintenance

by Diego Borro, Ángel Suescun, Alfonso Brazález, José Manuel González, Eloy Ortega and Eduardo González

Appl. Sci. 2021, 11(4), 1443; https://0-doi-org.brum.beds.ac.uk/10.3390/app11041443 - 5 Feb 2021

Cited by 11 | Viewed by 2526

Abstract

This paper shows two developed digital systems as an example of intelligent garage and maintenance that targets the applicability of augmented reality and wearable devices technologies to the maintenance of bus fleets. Both solutions are designed to improve the maintenance process based on [...] Read more.

This paper shows two developed digital systems as an example of intelligent garage and maintenance that targets the applicability of augmented reality and wearable devices technologies to the maintenance of bus fleets. Both solutions are designed to improve the maintenance process based on verification of tasks checklist. The main contribution of the paper focuses on the implementation of the prototypes in the company’s facilities in an operational environment with real users and address the difficulties inherent in the transfer of a technology to a real work environment, such as a mechanical workshop. The experiments have been conducted in real operation thanks to the involvement of the public transport operator DBUS, which operates public transport buses in the city of Donostia—San Sebastian (Spain). Two solutions have been developed and compared against the traditional process: one based on Tablet and another one based on Microsoft HoloLens. The results show objective metrics (Key Performance Indicators, KPI) as well as subjective metrics based on questionnaires comparing the two technological approaches against the traditional one based on manual work and paper. Full article

(This article belongs to the Special Issue Maintenance 4.0 Technologies for Sustainable Manufacturing)

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16 pages, 2377 KiB

Open AccessArticle

Analysis for the Knowledge Management Application in Maintenance Engineering: Perception from Maintenance Technicians

by Javier Cárcel-Carrasco and José-Antonio Cárcel-Carrasco

Appl. Sci. 2021, 11(2), 703; https://0-doi-org.brum.beds.ac.uk/10.3390/app11020703 - 13 Jan 2021

Cited by 9 | Viewed by 2622

Abstract

Knowledge based on personal experience (tacit knowledge) acquired in problem solving actions and in maintenance actions is the fundamental basis for maintenance technicians in companies with great physical assets. Generally, there is no proper policy for managing strategic knowledge and its capture. In [...] Read more.

Knowledge based on personal experience (tacit knowledge) acquired in problem solving actions and in maintenance actions is the fundamental basis for maintenance technicians in companies with great physical assets. Generally, there is no proper policy for managing strategic knowledge and its capture. In this article, through qualitative studies (grounded theory) and surveys conducted with technicians, the aim was to obtain the perception of the maintenance technicians’ part of the companies, in order to establish the characteristics of the relation between the strategic aspects and the engineering aspects of industrial maintenance, regarding knowledge management, as well as the enablers and barriers to its application. The results show how a high level of tacit knowledge is used in this activity, which requires more time for new staff. The values obtained from this survey show that the knowledge recorded by the companies (explicit) is 51.25%, compared to the personal knowledge (tacit) of maintenance technicians regarding reliability and breakdowns. In operational/exploitational actions it is 43.90%, for energy efficiency actions it is 49.61%, and in maintenance actions (preventive, predictive, and corrective) the value is 68.78%. This shows the significant gap between the perception of recorded knowledge (explicit), and the knowledge that maintenance technicians have (tacit knowledge). All this can affect the companies, as part of the strategic knowledge is lost when a maintenance technician leaves the company. Full article

(This article belongs to the Special Issue Maintenance 4.0 Technologies for Sustainable Manufacturing)

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19 pages, 2764 KiB

Open AccessArticle

Digital Twins in Product Lifecycle for Sustainability in Manufacturing and Maintenance

by Izabela Rojek, Dariusz Mikołajewski and Ewa Dostatni

Appl. Sci. 2021, 11(1), 31; https://0-doi-org.brum.beds.ac.uk/10.3390/app11010031 - 23 Dec 2020

Cited by 67 | Viewed by 6165

Abstract

A “digital twin” is a dynamic, digital replica of a technical object (e.g., a physical system, device, machine or production process) or a living organism. Using this type of solution has become an integral part of Industry 4.0, offering businesses tangible benefits, in [...] Read more.

A “digital twin” is a dynamic, digital replica of a technical object (e.g., a physical system, device, machine or production process) or a living organism. Using this type of solution has become an integral part of Industry 4.0, offering businesses tangible benefits, in addition to being particularly effective within the context of sustainable production and maintenance. The purpose of this paper is to present the results of research on the development of digital twins of technical objects, which involved data acquisition and their conversion into knowledge, the use of physical models to simulate tasks and processes, and the use of simulation models to improve the physical tasks and processes. In addition, monitoring processes and process parameters allow for the continued improvement of existing processes as regards intelligent eco-designing and planning and monitoring production processes while taking into account sustainable production and maintenance. Full article

(This article belongs to the Special Issue Maintenance 4.0 Technologies for Sustainable Manufacturing)

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20 pages, 4469 KiB

Open AccessArticle

Dynamic Planning of Mobile Service Teams’ Mission Subject to Orders Uncertainty Constraints

by Grzegorz Bocewicz, Peter Nielsen, Małgorzata Jasiulewicz-Kaczmarek and Zbigniew Banaszak

Appl. Sci. 2020, 10(24), 8872; https://0-doi-org.brum.beds.ac.uk/10.3390/app10248872 - 11 Dec 2020

Cited by 5 | Viewed by 1417

Abstract

This paper considers the dynamic vehicle routing problem where a fleet of vehicles deals with periodic deliveries of goods or services to spatially dispersed customers over a given time horizon. Individual customers may only be served by predefined (dedicated) suppliers. Each vehicle follows [...] Read more.

This paper considers the dynamic vehicle routing problem where a fleet of vehicles deals with periodic deliveries of goods or services to spatially dispersed customers over a given time horizon. Individual customers may only be served by predefined (dedicated) suppliers. Each vehicle follows a pre-planned separate route linking points defined by the customer location and service periods when ordered deliveries are carried out. Customer order specifications and their services time windows as well as vehicle travel times are dynamically recognized over time. The objective is to maximize a number of newly introduced or modified requests, being submitted dynamically throughout the assumed time horizon, but not compromising already considered orders. Therefore, the main question is whether a newly reported delivery request or currently modified/corrected one can be accepted or not. The considered problem arises, for example, in systems in which garbage collection or DHL parcel deliveries as well as preventive maintenance requests are scheduled and implemented according to a cyclically repeating sequence. It is formulated as a constraint satisfaction problem implementing the ordered fuzzy number formalism enabling to handle the fuzzy nature of variables through an algebraic approach. Computational results show that the proposed solution outperforms commonly used computer simulation methods. Full article

(This article belongs to the Special Issue Maintenance 4.0 Technologies for Sustainable Manufacturing)

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13 pages, 2368 KiB

Open AccessReview

Digital Twin Lean Intralogistics: Research Implications

by Pawel Pawlewski, Monika Kosacka-Olejnik and Karolina Werner-Lewandowska

Appl. Sci. 2021, 11(4), 1495; https://0-doi-org.brum.beds.ac.uk/10.3390/app11041495 - 7 Feb 2021

Cited by 12 | Viewed by 3106

Abstract

This article presents research implications related to the analysis of current trends occurring in the industry and resulting from the analysis of trends in literature. A new trend is noticeable in the range of computer simulations using digital twin technologies in the optimization [...] Read more.

This article presents research implications related to the analysis of current trends occurring in the industry and resulting from the analysis of trends in literature. A new trend is noticeable in the range of computer simulations using digital twin technologies in the optimization of intralogistics processes, the implementation of which is based on Lean philosophy. This article shows the connection of Industry 4.0 with Lean in the context of Digital Twin (simulation) in the area of intralogistics. A three-step methodology of literature research was developed and described. In accordance with the adopted research methodology, research questions were indicated and a detailed list of selection criteria was developed. The research methods included brainstorming and statistical analysis. The research results are presented in three sections: the results of the trend analysis, the results of the quantitative literature research, and the results of the complementary research. The research results confirm the existence of a new trend and form the basis for formulating objectives for further research. Full article

(This article belongs to the Special Issue Maintenance 4.0 Technologies for Sustainable Manufacturing)

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