Scheduling Of Maintenance Activities Research Articles

Scheduling maintenance activities subject to stochastic job-dependent machine deterioration

This paper considers a maintenance scheduling problem on a single machine with a fixed job sequence involving job-dependent, stochastic deterioration. If the machine breaks down, then a time-consuming emergency maintenance activity needs to be conducted. Instead, planned maintenance activities can be conducted between jobs in order to improve the machine’s state and, thus, prevent the machine from breaking down. We distinguish two types of machines differing in the flexibility regarding the point of time when the maintenance activities need to be scheduled. The goal is to minimize the total expected completion time of jobs. We formalize two optimization problems, analyze their computational complexities, develop exact solution approaches, and conduct a computational study. Here, we show that our approaches can solve large instances to optimality within short run times and we analyze the advantage gained by planning maintenance activities depending on the actual state of the machine.

Technical Language Processing of Nuclear Power Plants Equipment Reliability Data

Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.

An intelligent schedule maintenance method for hydrogen fuel cell vehicles based on deep reinforcement learning considering dynamic reliability

Schedule maintenance activities for hydrogen fuel cell vehicles are aimed to keep them in better condition in long-term operation. In this paper, we develop a reinforcement learning based schedule maintenance strategy, which is used to comprehensively consider the balance between safety and maintenance cost, so as to find the optimal maintenance strategy. A multi-level framework is established with remaining useful life of key components, fault tree analysis, and logistics cost model to generate an exploration environment that can express the operational stability, the failure rate of components, and the cost of repair and storage. The trained agent combines a deep neural network to explore the optimal strategy under the dynamic reliability of key components. Finite steps are the constraints. Safety rates, maintenance cost, and episode of operation are incorporated into a multi-objective reward function. The hydrogen supply circuit of fuel cell vehicle is simulated via Python. The trained agent is compared with traditional time-based schedule maintenance strategy and corrective maintenance strategy. The results show that the reinforcement learning based schedule maintenance agent has optimized the total reward, cost control and accident rate by 77%, 59% and 5%, respectively, compared with the traditional time-based schedule maintenance strategy. In the safe operation management of fuel cell vehicles, efficient and stable decision-making ability has been achieved.

Resilience-oriented approach of dynamic production and maintenance scheduling optimisation considering operational uncertainty

ABSTRACT One of the main challenges in operating multistate manufacturing systems (MMSs) is maintaining stable and robust production against various disruptions. Therefore, an urgent need exists for an operation and maintenance (O&M) method that optimises MMS resilience, i.e. the capability of withstanding or recover from disruptions of various sources. Consequently, this study proposes an integrated resilience-oriented production and maintenance scheduling approach for MMSs. This approach enhances MMS resilience by reinforcing its adaptivity to the variation in production requirements. Based on a conceptual investigation of operational uncertainty and its mechanism of disruption, this study devotes to (i) formulating a performance loss-based resilience measurement with consideration of operational uncertainties and (ii) proposing a reinforcement learning-based approach to schedule MMS operation and maintenance activities for MMS components of different performance states. An industrial case study of a ferrite phase shifter manufacturing system is subsequently conducted to validate the proposed approach. Results demonstrate the effectiveness of the proposed approach in the resilience optimisation of MMSs.

INTELLIGENT MONITORING SYSTEMS IN MANUFACTURING: CURRENT STATE AND FUTURE PERSPECTIVES

Intelligent Monitoring Systems (IMS) have emerged as indispensable tools in modern manufacturing, offering real-time insights into production processes, equipment performance, and quality control. This review provides an overview of the current state and future prospects of IMS in manufacturing environments. The current state of IMS in manufacturing involves the integration of advanced technologies such as Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Big Data analytics. These systems collect vast amounts of data from sensors, machines, and production lines, enabling real-time monitoring and analysis of various parameters. By employing AI and ML algorithms, IMS can identify patterns, predict anomalies, and optimize production processes, leading to increased efficiency, reduced downtime, and improved product quality. IMS play a crucial role in predictive maintenance, where they can anticipate equipment failures before they occur, thereby minimizing unplanned downtime and maintenance costs. Moreover, IMS facilitate condition-based monitoring, allowing manufacturers to monitor the health and performance of machinery in real-time and schedule maintenance activities accordingly, optimizing resource allocation and prolonging equipment lifespan. Furthermore, IMS contribute to quality control by continuously monitoring production processes and detecting deviations from desired specifications. By leveraging AI-driven algorithms, IMS can automatically adjust process parameters to maintain product quality standards and minimize defects, thereby enhancing overall product reliability and customer satisfaction. Looking ahead, the future perspectives of IMS in manufacturing are promising, with advancements in areas such as edge computing, robotics, and augmented reality poised to revolutionize manufacturing operations further. Edge computing enables data processing and analysis to occur closer to the data source, reducing latency and enhancing real-time decision-making capabilities. Robotics integration with IMS facilitates autonomous manufacturing processes, while augmented reality technologies provide intuitive interfaces for operators to interact with IMS data in real-time. IMS represent a transformative technology in manufacturing, offering unprecedented levels of visibility, control, and optimization. As technology continues to evolve, IMS are poised to play an increasingly vital role in shaping the future of manufacturing, driving efficiency, productivity, and innovation..
 Keywords: Monitoring, System, Intelligent, Manufacturing, Review, Perspectives.

Optimizing energy efficiency in data center cooling towers through predictive maintenance and project management

Optimizing energy efficiency in data center cooling towers is crucial for reducing operational costs and environmental impact. This review explores the integration of predictive maintenance and project management to achieve this goal. By leveraging predictive maintenance techniques, data center operators can anticipate and address potential issues before they lead to costly downtime or inefficiencies. Project management plays a key role in coordinating these efforts, ensuring that maintenance activities are carried out efficiently and effectively. Predictive maintenance relies on data analytics and machine learning algorithms to monitor the condition of cooling towers in real-time. By analyzing data such as temperature, pressure, and flow rates, these algorithms can detect anomalies and predict potential failures. This proactive approach allows data center operators to schedule maintenance activities during planned downtime, minimizing disruptions to operations. Project management practices, such as Agile or Waterfall methodologies, are essential for coordinating predictive maintenance efforts. Project managers oversee the planning, execution, and monitoring of maintenance activities, ensuring that they are completed on time and within budget. They also facilitate communication between stakeholders, including maintenance teams, data analysts, and management, to ensure that everyone is aligned and working towards the same goals. By integrating predictive maintenance with project management, data center operators can achieve significant improvements in energy efficiency. By addressing maintenance issues proactively, operators can reduce energy consumption, extend the lifespan of cooling equipment, and minimize downtime. Additionally, project management practices ensure that these efforts are coordinated and effective, maximizing the benefits of predictive maintenance. In conclusion, optimizing energy efficiency in data center cooling towers requires a holistic approach that combines predictive maintenance and project management. By leveraging predictive maintenance techniques and project management practices, data center operators can achieve significant improvements in energy efficiency, reduce operational costs, and enhance environmental sustainability.

Failure Prevention in Large-Diameter Water Pipelines Using Reliability-Centered Maintenance

The consequences of failures from large-diameter water pipelines can be severe. Results can include significant property damage, adjacent damage to infrastructure such as roads and bridges resulting in transportation delays or shutdowns, adjacent structural damage to buildings resulting in loss of business, service disruption to a significant number of customers, loss of water, costly emergency repairs, and even loss of life. The Washington Suburban Sanitary Commission (WSSC) in the United States found that flooding was the greatest concern due to its potential duration, the potential for broad geographic impact, and its role in crater creation. Public safety, property damage, social and economic consequences, and loss of water service and for how long is also of paramount concern. The American Water Works Association’s (AWWA) 2020 “State of the Water Industry” report states that the top issue facing the water industry since 2016 is aging infrastructure, with the second being financing for improvements. The industry needs to find novel ways of extending asset life and reducing maintenance expenditures. While there are many different assets that comprise the water/wastewater industry, pipelines are a major component and are often neglected because they are typically buried. Reliability-Centered Maintenance (RCM) is a process used to determine the most effective maintenance strategy for an asset, with the ultimate goal being to establish the required function of the asset considering the required reliability and availability at the lowest cost. The RCM philosophy considers Preventive Maintenance, Predictive Maintenance, Condition Based Monitoring, Reactive Maintenance, and Proactive Maintenance techniques in an integrated manner to increase the probability an asset will perform its designed function throughout its design life with minimal maintenance. RCM requires maintenance decisions be based on maintenance requirements supported by sound technical and economic justification. However, one industry where principles of RCM are in its infancy is the water/wastewater industry. This paper provides a case example and numeric modeling for use in RCM analyses for developing maintenance strategies for large-diameter water pipelines, particularly prestressed concrete pipelines, and proposes an approach for determining the most effective and efficient maintenance activities for large-diameter prestressed concrete water pipelines. The case study discussed in this paper analyzed wire breaks over time to predict when certain thresholds would be reached. The intent of this study is to predict when a specified threshold will be reached. From the RCM, a threshold was set to begin planning, budgeting, and scheduling maintenance activities when 55% of the wires in a frame or two adjoining frames are distressed or when 65% of the wires in non-adjacent frames are distressed. The results from the numeric model predict the 55% threshold may be reached in August 2025 for the most distressed pipe segment.

Optimal Proof-Testing Strategies for Safety-Related Systems Based on Continuous Time Markov Chain

Maintenance activities for safety-related systems are generally required to ensure that the systems are working as intended. Regarding the maintenance activities, proof-testing is known as scheduled inspections and maintenance activities for detecting dangerous undetected faults which cannot be detected by diagnostic testing systems installed in the safety-related systems. However, the proof-testing needs a lot of cost and provokes decreasing of the availability for the whole system because the whole system is needed to shut down for proofing that the whole system is working as intended. We discuss analytical methodologies for obtaining optimal proof-testing interval with harmful risk and proof-testing cost by describing the behavior of the safety-related system based on a continuous-time Markov chain. Further, an analytical optimal policy for obtaining economic proof-testing interval is proposed in this paper.

Matheuristics for scheduling of maintenance service with linear operation cost and step function maintenance cost

We study a problem of scheduling machine maintenance activities in discretized periods, under the constraint that a maximum of one activity can be scheduled for each period. The scheduled maintenance activities will be repeated in a cyclic policy with a given cycle length.The problem is to find a scheduling policy specifying which activity is executed in each period in order to minimize the total cost.Two kinds of costs are assumed: operation and maintenance costs. Both of them depend on the number of periods since the last machine maintenance service. The operation cost occurs during the periods in which a machine is not maintained and increases linearly. The maintenance cost takes place in the periods in which a machine is maintained and is defined as a step function. The steps in the maintenance cost function could be justified by the fact of considering the machine element's substitution when this machine is run for too long without being maintained. This cost structure reduces the gap between the academic problem and the industrial reality.In this paper, the scheduling problem with linear operation cost and step function maintenance cost is defined and formalized. In addition, different matheuristic algorithms are proposed for its resolution. Lower bounds for the objective function are proposed and the computational results show an average deviation from the optimal solution lower than or equal to 3.19%.

AI-Integrated Mechanical Engineering Solutions for Next-Gen Rocket Propulsion Systems

The integration of Artificial Intelligence (AI) into the field of mechanical engineering has heralded a new era of innovation and efficiency, particularly in the realm of next-generation rocket propulsion systems. This abstract explores the transformative impact of AI in the development and optimization of rocket propulsion technologies, highlighting its potential to revolutionize the aerospace industry. AI-powered mechanical engineering solutions have emerged as a game-changer in the design and manufacturing of rocket propulsion systems. Through advanced machine learning algorithms and predictive analytics, AI can significantly enhance the efficiency of the development process. By analyzing vast datasets of historical performance data, AI can identify patterns and correlations that human engineers might overlook. This allows for the creation of propulsion systems that are not only more powerful but also safer and more reliable. AI plays a pivotal role in the optimization of rocket engines. Traditional optimization methods often require extensive computational resources and time-consuming simulations. AI, on the other hand, leverages neural networks and genetic algorithms to rapidly iterate through design possibilities, resulting in propulsion systems that are finely tuned for maximum performance and fuel efficiency. This not only reduces development costs but also accelerates the time-to-market for next-gen rocket propulsion systems. Safety is paramount in rocket propulsion systems, and AI offers innovative solutions in this regard as well. AI algorithms can continuously monitor and analyze sensor data during rocket launches, quickly identifying anomalies and potential issues.[1] This real-time monitoring allows for immediate corrective actions, reducing the risk of catastrophic failures and ensuring the safety of crewed and uncrewed missions. AI-integrated mechanical engineering solutions enable autonomous maintenance and diagnostics of propulsion systems. Through predictive maintenance models, AI can predict when components are likely to fail and schedule maintenance activities accordingly. This proactive approach not only extends the lifespan of propulsion systems but also minimizes downtime and operational disruptions.

Multi-objective reinforcement learning-based framework for solving selective maintenance problems in reconfigurable cyber-physical manufacturing systems

ABSTRACT Unlike mass production manufacturing systems, where configurations are rarely changed after the initial design, reconfigurable cyber-physical systems (RCPMS) self-change their structures throughout missions and thus self-adjust production in response to demand requirements. Accordingly, such a paradigm requires enhancing selective maintenance strategy to optimise scheduling maintenance actions, selecting configuration layouts for capacity and product family changes, and achieving maintenance cost reduction and reliability maximisation. This paper is the first to propose a robust model for a selective maintenance problem with imperfect repairs in the RCPMS context. The model also integrates uncertainties originating from the imperfect observations of components' health status. The model's objectives are to maximise the expected reliability and minimise the variance and maintenance cost under maintenance resource constraints. Moreover, we propose a new deep reinforcement learning framework for solving the resulting multi-objective and combinatorial optimisation problem. In addition, we use decision values to enhance the scalarisation process by permitting the priorities of specific objectives to be adjusted after the learning process. Furthermore, we employ Analytical Hierarchy Process to adjust the static priorities with respect to the objective functions and the actual learning context. Finally, broad experiments are conducted to highlight the performance of the proposed model and resolution framework.

Optimization model for production scheduling taking into account preventive maintenance in an uncertainty-based production system

In the dynamic yet uncertain environment of Industry 4.0, industrial companies are utilizing the benefits of contemporary technologies in manufacturing by striving to implement optimization models in each stage of the decision-making process. Many organizations are focusing particularly on the optimization of two key aspects of the manufacturing process - production schedules and maintenance plans. This article presents a mathematical model with the main advantage of finding a valid production schedule (if such exists) for the distribution of individual production orders on the available production lines over a specified period. The model further considers the scheduled preventive maintenance activities on the production lines, as well as the preferences of the production planners regarding the start of the production orders and non-use of certain machines. When necessary, it also offers the possibility to make timely changes in the production schedule, and thus to handle the uncertainty as precisely as possible. For the verification of the model, two experiments were conducted (quasi-real and real-life), with data from a discrete automotive manufacturer of locking systems. The results from the sensitivity analysis demonstrated that the model further optimizes the execution times of all orders, and specifically the production lines usage - their optimal load and non-use of unnecessary machines (valid plan with 4 out of 12 lines not used). This allows for cost savings and raises the overall efficiency of the production process. Thus, the model adds value for the organization by presenting a production plan with optimal machine usage and product allocation. If incorporated into an ERP system, it could distinctly save time and streamline the production scheduling process.

Real-time power theft monitoring and detection system with double connected data capture system

Power utilities worldwide are facing enormous challenges when it comes to the distribution of electricity. With these challenges, electricity theft is regarded as the most common challenge in the electrical distribution system. Electricity theft can be meter tampering done in consumer houses and illegal connections done using hook-ups from the distribution pole grids. These electricity theft challenges have caused power utilities to reconsider customer engagements focusing on feedback, putting loss detection systems in their distribution system networks, using artificial intelligence to schedule maintenance and other asset management activities, etc. The main focus of this paper is to design a real-time power theft monitoring and detection system that is able to detect power theft in distribution systems. This proposed system utilizes smart meters consisting of an Arduino ATMega328P microcontrollers with GSM modules (Global System for Mobile Communication) used for system communication. Cloud storage is created to store the smart meter data. Simulations of the proposed system were done using Proteus Design Suite v.8.10 SP3 software. The proposed system is practically constructed for prototype measurement results. Should power imbalances be measured by the system, the authority office will receive an SMS notification as an alert for power theft detected by a specific smart metering system. The authority office will analyse the power measurements sent to the cloud storage (MATLAB Online, ThingSpeak IoT channels display), and further action will be taken.

Towards climate-neutral aviation: Assessment of maintenance requirements for airborne hydrogen storage and distribution systems

Airlines are faced with the challenge of reducing their environmental footprint in an effort to push for climate-neutral initiatives that comply with international regulations. In the past, the aviation industry has followed the approach of incremental improvement of fuel efficiency while simultaneously experiencing significant growth in annual air traffic. With the increase in air traffic negating any reduction in Greenhouse Gas (GHG) emissions, more disruptive technologies such as hydrogen-based onboard power generation are required to reduce the environmental impact of airline operations. However, despite initial euphoria and first conceptual studies for hydrogen-powered aircraft several decades ago, there still has been no mass adoption to this day. Besides the challenges of a suitable ground infrastructure, this can partly be attributed to uncertainties with the associated maintenance requirements and the expected operating costs to demonstrate the economic viability of this technology. With this study, we address this knowledge gap by estimating changes towards scheduled maintenance activities for an airborne hydrogen storage and distribution system. In particular, we develop a detailed system design for a hydrogen-powered, fuel-cell-based auxiliary power generation and perform a comparative analysis with an Airbus A320 legacy system. That analysis allows us to (a) identify changes for the expected maintenance effort to enhance subsequent techno-economic assessments, (b) identify implications of specific design assumptions with corresponding maintenance activities while ensuring regulatory compliance and (c) describe the impact on the resulting task execution. The thoroughly examined interactions between system design and subsequent maintenance requirements of this study can support practitioners in the development of prospective hydrogen-powered aircraft. In particular, it allows the inclusion of maintenance implications in early design stages of corresponding system architectures. Furthermore, since the presented methodology is transferable to different design solutions, it provides a blueprint for alternative operating concepts such as the complete substitution of kerosene by hydrogen to power the main engines.

On the prediction of intermediate-to-long term bus section travel time with the Burr mixture autoregressive model

Travel time is an essential indicator for trip planning, transportation service planning, and operation. This study aims to propose a novel Burr mixture autoregressive (BMAR) model for the intermediate-to-long term period of bus section travel time prediction, which is useful for bus service and schedule planning. The BMAR model exhibits greater flexibility, allowing it to effectively capture the multi-peak and non-peak, non-linear with heteroscedasticity characteristics of travel time. The model is trained and well-validated with 6-month bus section travel time data collected via the automatic vehicle location system. Results show that the BMAR model gives promising results in travel time point and interval prediction, especially for the higher degree of variability and irregular pattern of travel time observed on urban roads and highways. The reliability ratio index derived from the BMAR model could be used to measure the bus service reliability and aid in the bus scheduling of maintenance activities.

Automated machine learning recognition to diagnose flood resilience of railway switches and crossings

The increase in demand for railway transportation results in a significant need for higher train axle load and faster speed. Weak and sensitive trackforms such as railway switches and crossings (or called ‘turnout’) can suffer from such an increase in either axle loads or speeds. Moreover, railway turnout supports can deteriorate from other incidences due to extreme weather such as floods which undermine cohesion between ballast leading to ballast washaway or loss of support under turnout structures. In this study, new intelligent automation based on machine learning pattern recognition has been built to detect and predict the deterioration of railway turnouts exposed to flooding conditions which is the scope of this study. Since the turnout system is very complex by nature, different features and smart filtering are explored to find the potential features for deep learning. Nonlinear finite element models validated by actual field measurements are used to mimic the dynamic behaviors of turnout supports under flooding conditions. The study exhibits that the novel recognition model can achieve more than 98% accuracy, yielding the potential capability to recognize and classify turnout support deteriorations facing extreme weather conditions which will be beneficial for responsible parties to schedule and plan maintenance activities.

Economic Proof-Testing Intervals for E/E/Pe Safety-Related System with Harmful Risk

E/E/PE safety-related systems play an important role in ensuring the functional safety especially for the safety-critical systems. In fact, the E/E/PE safety-related systems are widely implemented in several types of systems, such as automotive and chemical plant control systems, for the safe operation of them. In the operation of the systems, proof-testing, which is known as scheduled periodic inspections or maintenance activities, is conducted for maintaining certain level of safety integrity required to the E/E/PE safety-related system. However, the proof-testing needs a lot of time and cost for checking and proofing whether the safety-related system satisfies the safety requirement in operation. We discuss economic strategies for supporting design of proof-testing interval by focusing on the trade-off relationship between the maintenance cost and the risk at harmful event occurrence.

Predictive Maintenance in Healthcare System: A Survey

Medical devices are a vital component of healthcare systems, the advantages they may give continue to grow as they are crucial for the safe and effective prevention, diagnosis, treatment, and rehabilitation of illnesses and diseases. Therefore, it is critical to maintain them in good operating order to ensure optimum availability, minimal failures, and guarantee patients’ and users’ safety. The stages involved in medical devices regulation and management are complex, but they are necessary to ensure their quality, safety, and compatibility with the settings in which they are used. Medical equipment complexity has increased due to technological advancement and the traditional maintenance strategies do not meet the needs of today’s healthcare organizations. Therefore, integrating information technology, social networking technologies, digitization and management of medical devices, and the use of big data technologies and Machine Learning (ML) techniques has the potential to significantly improve healthcare services. Integrating autonomous and intelligent systems where data and sophisticated data analytics may be employed led to enhanced equipment data collecting via the deployment of information and communication technologies, notably intelligent devices. With this advancement came an increase in Predictive Maintenance (PdM) solutions. PdM has become a commonly used approach, described as a set of procedures used to evaluate the condition of equipment and predict future failures. These estimations are then utilized to schedule maintenance activities through smart scheduling of maintenance procedures, which aids in preventing or at least minimizing the impacts of unanticipated failures. The purpose of this article is to present a Systematic Literature Review (SLR) exploring and reviewing prior research on the subject of PdM and the developments of this method, particularly in the medical field. In addition to supporting new research projects in the PdM sector, this paper offers a good foundation for understanding PdM approaches, their key findings, problems, and potential. This review focuses on two scientific databases from which a substantial number of articles dedicated solely to PdM in the medical field have been retrieved for analysis. Our research led us to conclude that, despite the many potential benefits of predictive maintenance in the medical field, the concept is still being under-exploited and faces many obstacles.

Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective

ABSTRACT Predictive Maintenance (PdM) solutions assist decision-makers by predicting equipment health and scheduling maintenance actions, but their implementation in industry remains problematic. Specifically, prior research repeatedly indicates that decision-makers often refuse to adopt the data-driven, system-generated advice in their working procedures. In this paper, we address these acceptance issues by studying how PdM implementation changes the nature of decision-makers’ work and how these changes affect their acceptance of PdM systems. We build on the human-centric Smith-Carayon Work System model to synthesise literature from research areas where system acceptance has been explored in more detail. Consequently, we expand the maintenance literature by investigating the human-, task-, and organisational characteristics of PdM implementation. Following the literature review, we distil ten propositions regarding decision-making behaviour in PdM settings. Next, we verify each proposition’s relevance through in-depth interviews with experts from both academia and industry. Based on the propositions and interviews, we identify four factors that facilitate PdM adoption: trust between decision-maker and model (maker), control in the decision-making process, availability of sufficient cognitive resources, and proper organisational allocation of decision-making. Our results contribute to a fundamental understanding of acceptance behaviour in a PdM context and provide recommendations to increase the effectiveness of PdM implementations.

Integrating machine learning techniques into optimal maintenance scheduling

Poor maintenance regimes often contribute to unplanned downtimes, quality defects and accidents; thus it is crucial to apply an effective maintenance strategy to achieve efficient and safe processes. Industry 4.0 has brought about a proliferation of digital data and with it new opportunities to advance and improve the way maintenance activities are planned. Here, we propose a novel methodology that utilises machine learning to predict both machine faults and repair time, and uses this data to underpin the scheduling of maintenance activities. This can be used to plan maintenance, and optimise the schedule with a cost objective within the constraints of labour availability and plant layout. When applied to a dataset obtained using a simulated Fischertechnik (FT) model, this methodology reduced the overall plant maintenance costs by decreasing unplanned downtimes and increasing maintenance efficiency. This work provides a promising first step towards improving the way maintenance tasks are approached in Industry 4.0.