Perform Maintenance Activities Research Articles

Determining a Reliability Centered Maintenance (RCM) analysis model for large diameter prestressed concrete water pipelines

Which maintenance task to perform on an asset is generally known but the schedule of when to perform the task may not be well defined. If the task is performed early, while it may benefit the asset, it may also be expending resources that could be better used elsewhere. If the task is not performed soon enough, the asset may fail, or the asset may require more extensive maintenance be performed. Reliability Centered Maintenance (RCM) is an analysis process used to determine the most effective maintenance strategies. RCM analysis can help an agency establish the most cost effective maintenance strategies for an asset and help establish the best schedule for implementing those strategies. The ultimate goal of an RCM is to determine the required function of an asset with the required reliability at the lowest operation and maintenance costs by identifying revealing indicators of potential failures to establish a condition based strategy; essentially, predicting failures and identifying procedures to minimize the risk of the failure. This paper reviews potential models for predicting the progression of distress of prestressed concrete pipe segments for use in an RCM analysis and proposes a regression model operators can use to forecast when to perform maintenance activities. The paper further considers the condition of the drinking water infrastructure in the United States, developing the case for evaluating predictive models to forecast when a pipeline may reach a specified limit state. The results of this study suggest that RCM analyses for large diameter water pipelines can improve reliability, reduce maintenance costs, and extend the useful life of a pipeline regardless of age or material. Further, using a regression style model with gathered data can be used to forecast when specific maintenance thresholds may be reached, prompting predictive maintenance actions.

A Robot-Operation-System-Based Smart Machine Box and Its Application on Predictive Maintenance.

Predictive maintenance is a proactive approach to maintenance in which equipment and machinery are monitored and analyzed to predict when maintenance is needed. Instead of relying on fixed schedules or reacting to breakdowns, predictive maintenance uses data and analytics to determine the appropriate time to perform maintenance activities. In industrial applications, machine boxes can be used to collect and transmit the feature information of manufacturing machines. The collected data are essential to identify the status of working machines. This paper investigates the design and implementation of a machine box based on the ROS framework. Several types of communication interfaces are included that can be adopted to different sensor modules for data sensing. The collected data are used for the application on predictive maintenance. The key concepts of predictive maintenance include data collection, a feature analysis, and predictive models. A correlation analysis is crucial in a feature analysis, where the dominant features can be determined. In this work, linear regression, a neural network, and a decision tree are adopted for model learning. Experimental results illustrate the feasibility of the proposed smart machine box. Also, the remaining useful life can be effectively predicted according to the trained models.

Training on LSA lifeboat operation using Mixed Reality

BackgroundThis work aims to provide an overview of the Mixed Reality (MR) technology's use in maritime industry for training purposes. Current training procedures cover a broad range of procedural operations for Life-Saving Appliances (LSA) lifeboats; however, several gaps and limitations have been identified related to the practical training that can be addressed through the use of MR. Augmented, Virtual and Mixed Reality applications are already used in various fields in maritime industry, but their full potential have not been yet exploited. SafePASS project aims to exploit MR advantages in the maritime training by introducing a relevant application focusing on use and maintenance of LSA lifeboats. MethodsAn MR Training application is proposed supporting the training of crew members in equipment usage and operation, as well as in maintenance activities and procedures. The application consists of the training tool that trains crew members on handling lifeboats, the training evaluation tool that allows trainers to assess the performance of trainees, and the maintenance tool that supports crew members to perform maintenance activities and procedures on lifeboats. For each tool, an indicative session and scenario workflow are implemented, along with the main supported interactions of the trainee with the equipment. ResultsThe application has been tested and validated both in lab environment and using a real LSA lifeboat, resulting to improved experience for the users that provided feedback and recommendations for further development. The application has also been demonstrated onboard a cruise ship, showcasing the supported functionalities to relevant stakeholders that recognized the added value of the application and suggested potential future exploitation areas. ConclusionsThe MR Training application has been evaluated as very promising in providing a user-friendly training environment that can support crew members in LSA lifeboat operation and maintenance, while it is still subject to improvement and further expansion.

Reliability based maintenance programming by a new index for electrical distribution system components ranking

Reliability and accessibility of distribution systems are important goals that have significant impacts on the costs. The proper strategy of maintenance based on components arrangement and assets is the best way to reach these goals. This strategy is a kind of uses reliability-centered maintenance (RCM). Due to the limited maintenance budget, performing maintenance activities for all components of the system is neither possible nor logical. So most of the resources should be allocated to the most critical and important components. This paper presents a novel analytical method of prioritization of distribution systems’ components by introducing a new weighted cumulative Reliability-based diagnostic importance factor. This new factor includes different reliability indexes in form of diagnostic factors and will show that the order of components obtained by this method is better than another method in saving the budget and providing reliability of the system. The process of decision-making for prioritization of distribution systems’ components based on their criticality degree will both improve the reliability level of the total system and decrease the cost of load interruption and finally maintenance costs. The proposed model is implemented on a radial distribution network. Numerical results show the effectiveness of the proposed RCM model for micro-grids.

Multi-Objective Model and Variable Neighborhood Search Algorithms for the Joint Maintenance Scheduling and Workforce Routing Problem

This paper addresses a problem faced by maintenance service providers: performing maintenance activities at the right time on geographically distributed machines subjected to random failures. This problem requires determining for each technician the sequence of maintenance operations to perform to minimize the total expected costs while ensuring a high level of machine availability. To date, research in this area has dealt with routing and maintenance schedules separately. This study aims to determine the optimal maintenance and routing plan simultaneously. A new bi-objective mathematical model that integrates both routing and maintenance considerations is proposed for time-based preventive maintenance. The first objective is to minimize the travel cost related to technicians’ routing. The second objective can either minimize the total preventive and corrective maintenance cost or the failure cost. New general variable neighborhood search (GVNS) and variable neighborhood descent (VND) algorithms based on the Pareto dominance concept are proposed and performed over newly generated instances. The efficiency of our approach is demonstrated through several experiments. Compared to the commercial solver and existing multi-objective VND and GVNS, these new algorithms obtain highly competitive results on both mono-objective and bi-objective variants.

Autonomous condition monitoring-based pavement management system

Due to high operation cost of dedicated inspection vehicles, conventional pavement management systems (PMS) suffer from limited data quantity collected from periodic inspections. However, increasing market penetration of connected autonomous vehicles (CAVs) offers opportunities to monitor pavement conditions more frequently through sensors, including vision cameras and accelerometers, originally installed for autonomous driving. In this paper, we proposed an autonomous condition monitoring-based pavement management system (ACM-PMS) with real-time data collection using CAVs traveling voluntarily. We presented a novel mathematical framework to evaluate potential benefits of ACM-PMS in reducing social costs for both users and agency, systematically accounting for its unique three advantages: (i) large amount of condition data increases prediction model accuracy; (ii) aggregated measurement of current facility condition improves inspection accuracy; (iii) agency can perform maintenance activities at optimal timings, achieving continuous-time and condition-based policies. Results of numerical examples confirm that ACM-PMS significantly reduces the social cost of conventional PMS.

Joint optimization of condition-based maintenance and spares inventory for a series–parallel system with two failure modes

In the field of industrial engineering, the joint optimization of maintenance and spares inventory has attracted more and more attention because it can better balance system availability and cost. However existing studies are usually restricted to a single failure mode and a simple maintenance strategy of the single component systems. To bridge these gaps, this paper investigates the joint optimization of condition-based maintenance and spares inventory for a general series–parallel system with two failure modes. Hard failures are self-announcing, and soft failures are generally caused by the degradation of components and only be discovered through inspection. At the time of each periodic inspection, the corresponding corrective maintenance, preventive maintenance and spare parts ordering policy are determined. Upon a hard failure occurs, an opportunistic inspection will be performed, and it will be determined whether to perform maintenance actions based on the degradation level of the components and spares inventory level. Furthermore, the state transition probability and the expected sojourn time can be derived by the formulated semi-Markov decision process. To minimize the expected average cost per unit time, the optimal preventive maintenance and the spares inventory control policies are jointly determined by applying a simulation method. Finally, a numerical experiment is presented to demonstrate the effectiveness and superiority of the proposed joint optimization model.

Prioritizing Roadway Pavement Marking Maintenance Using Lane Keep Assist Sensor Data.

There are over four million miles of roads in the United States, and the prioritization of locations to perform maintenance activities typically relies on human inspection or semi-automated dedicated vehicles. Pavement markings are used to delineate the boundaries of the lane the vehicle is driving within. These markings are also used by original equipment manufacturers (OEM) for implementing advanced safety features such as lane keep assist (LKA) and eventually autonomous operation. However, pavement markings deteriorate over time due to the fact of weather and wear from tires and snowplow operations. Furthermore, their performance varies depending upon lighting (day/night) as well as surface conditions (wet/dry). This paper presents a case study in Indiana where over 5000 miles of interstate were driven and LKA was used to classify pavement markings. Longitudinal comparisons between 2020 and 2021 showed that the percentage of lanes with both lines detected increased from 80.2% to 92.3%. This information can be used for various applications such as developing or updating standards for pavement marking materials (infrastructure), quantifying performance measures that can be used by automotive OEMs to warn drivers of potential problems with identifying pavement markings, and prioritizing agency pavement marking maintenance activities.

Comparative Analysis an Early Fault Diagnosis Approaches in Rotating Machinery by Convolution Neural Network

In several industrial applications, rotating machinery is widely utilized in various forms. A growing amount of study, in the academic and industrial fields, as a potential sector for the confidentiality of modern industrial labor systems, has been drawing early fault diagnosis (EFD) techniques. However, EFD plays an essential role in providing sufficient information for performing maintenance activities, preventing and reducing financial loss and disastrous defaults. Many of the existing techniques for identifying rotations were ineffective. For the identification of spinning machine faults, many in-depth learning methods have recently been developed. This research report has included and analysed a number of research publications that have higher precision than standard algorithms for detecting early failures in rotating machinery. In addition to the artificial intelligence monitoring (AIM) model, detecting the defects in rotating machine was also realized through the simulation output. AIM framework model is also testing the rotating machinery in three different stages, which is based on the vibration signal obtained from the bearing system and further it has been trained with the neural network preceding. Compared to other traditional algorithms, the AIM model has achieved greater precision and also the other performance measures are tabulated in the result and discussion section.

Comparative Analysis an Early Fault Diagnosis Approaches in Rotating Machinery by Convolution Neural Network

In several industrial applications, rotating machinery is widely utilized in various forms. A growing amount of study, in the academic and industrial fields, as a potential sector for the confidentiality of modern industrial labor systems, has been drawing early fault diagnosis (EFD) techniques. However, EFD plays an essential role in providing sufficient information for performing maintenance activities, preventing and reducing financial loss and disastrous defaults. Many of the existing techniques for identifying rotations were ineffective. For the identification of spinning machine faults, many in-depth learning methods have recently been developed. This research report has included and analysed a number of research publications that have higher precision than standard algorithms for detecting early failures in rotating machinery. In addition to the artificial intelligence monitoring (AIM) model, detecting the defects in rotating machine was also realized through the simulation output. AIM framework model is also testing the rotating machinery in three different stages, which is based on the vibration signal obtained from the bearing system and further it has been trained with the neural network preceding. Compared to other traditional algorithms, the AIM model has achieved greater precision and also the other performance measures are tabulated in the result and discussion section.

DMB Machine Reliability Analysis Through Failure Mode Effect Analysis and Reliability Block

The production process is carried out continuously, it will result in decreased machine capability. When a component on a machine is damaged resulting in the production process being interrupted, the machine's performance depends on the Reliability of the machine or the component. The DMB machine is a sorting machine at PT Etex Building Performance Indonesia, because it is the main machine in the production process and DMB has a high downtime in 2019. Failure Mode effect And Analysis, aims to determine the machine failure factor. Reliability Block Diagram, to determine machine reliability. The results of research with FMEA are Pneumatic with RPN value 60, Decelerator value RPN 64, Accelerator value RPN 56, Hydraulic Table Safety Lock value RPN 48, Roller value RPN 48 Bearing Lifting value RPN 36, Sensor Transfer Dolly value RPN 48, Motor Transfer Template RPN value 36, the Suction Cup is the component that has the lowest RPN value with a value of 30, from the Reliability Block Diagram (RBD) calculation, the DMB machine reliability opportunity value is obtained including 88.42% of the production process for 23 hours, 42.25% of the process for 161 hours of operation. , and decreased by 2.70% when used for 674 hours of operation. Recommended action is to perform maintenance actions with the type of daily care and weekly maintenance.

An energy-oriented maintenance policy under energy and quality constraints for a multielement-dependent degradation batch production system

In recent years, to attain a competitive edge, merely optimizing the production efficiency is no longer the whole story that the manufacturing enterprises concentrate on. Instead, the increasing energy costs are driving manufacturing enterprises to seek an energy-oriented approach further to improve the overall performance of the production systems. Therefore, an opportunistic maintenance policy under energy and quality constraints is developed for a degrading batch production system in this paper. First and foremost, the degradation process of the production system is modeled using the Lévy-type processes, meanwhile, the concept of the multielement-dependent degradation model was first put forward to describe the energy and quality degradation in production systems during the work period. Secondly, the degradation constraint threshold, energy constraint threshold, and quality constraint threshold are developed to determine the maintenance groups which include the machines that require two types of maintenance, preventive and corrective. Subsequently, in order not to interrupt the batch production activities, the changeover time between every two batches is regarded as an energy saving window to perform maintenance actions for the integrated maintenance groups. Thirdly, an energy-oriented integrated model that optimizes the total cost rate in consideration of energy and quality is developed, and the optimal maintenance combination scheme can be obtained by the Monte Carlo method. Finally, a numerical example is used to prove the effectiveness of the proposed policy.

A maintenance scheduling optimization model for a multi-component machine in a digitalized manufacturing context

Abstract Manufacturing systems are facing a digital transformation that supports data-driven decision making and the improvement of the efficiency of condition-based or predictive maintenance systems. In this context, international standards and previous works related to the architecture of condition-based maintenance (CBM) systems are available, and the interest in maintenance scheduling optimization, triggered by failure predictions, are increasing. This work is framed within the scope of the PICOePRO project and considers a critical multi-component welding machine. This paper first provides a predictive maintenance architecture, coherent with the OS A-CBM standard and proposed within the project, and then develops one of the modules of this system, in which the main component is a maintenance scheduling optimization model. The defined architecture allows real-time decision-making, and the proposed model minimizes the machine maintenance cost, exploring the potential for cost-saving by grouping maintenance and using machine downtime to perform maintenance activities. The functioning of the module and the potential gains from the adoption of the developed model are illustrated using a numerical example. The architecture and the model provide a viable approach to guide maintenance decision-making in the era of digitalized manufacturing.

A Short-Term Preventive Maintenance Scheduling Method for Distribution Networks With Distributed Generators and Batteries

Preventive maintenance is applied in distribution networks to prevent failures by performing maintenance actions on components that are at risk. Distributed generators (DGs) and batteries can be used to support power to nearby loads when they are isolated due to maintenance. In this paper, a novel short-term preventive maintenance method is proposed that explicitly considers the support potential of DGs and batteries as well as uncertainties in the power generated by the DGs. Two major issues are addressed. To deal with the large-scale complexity of the network, a depth-first-search clustering method is used to divide the network into zones. Moreover, a method is proposed to capture the influence of maintenance decisions in the model of the served load from DGs and batteries via generation of topological constraints. Then a stochastic scenario-based mixed-integer non-linear programming problem is formulated to determine the short-term maintenance schedule. We show the effectiveness and efficiency of the proposed approach via a case study based on a modified IEEE-34 bus distribution network, where we also compare a branch-and-bound and a particle swarm optimization solver. The results also show that the supporting potential of DGs and batteries in preventive maintenance scheduling allows a significant reduction of load losses.

An Assessment of Aircraft Maintenance Technician Competency

Aircraft maintenance activities are one of the most important criteria for the safe and effective execution of aviation operations., In aircraft accidents and incidents, maintenance factor is vital for the development of safety for organizations, authorities, and countries in the aviation field. Effective maintenance activities will also contribute to the costs of organizations by ensuring the safe operations of aircraft with people. Maintenance activities are carried out by maintenance technicians in areas such as hangars or aprons. Aircraft maintenance technicians' performance in performing maintenance activities directly impacts flight safety and technician safety, which in turn has a positive or negative impact on organizations. Improving technician competency assessment processes can reduce maintenance errors, improve technician performance, create positive impacts on safe and efficient flight operations, reduce maintenance costs and benefit of the entire aviation industry. Technician competency should be considered in performance evaluations and assignments by assessing at all levels with the compatibility of widely used human resources management methods. In this study, technician competency assessment processes are mentioned, the effects of these processes on aviation safety are explained and solutions are proposed to develop and apply the assessment processes.

Model development of risk assessment of risk-taking relationship with integrated approach

Each organization or company must always be dealing with risk. The decision-makers strive to be able to avoid the impact of such risks. Those generated risks may affect the achievement of the objectives, therefore risk management is needed. Risk management is a solution to avoid or to minimize the occurrence of the risk. Risk management consists of several steps, one of them is risk assessment. An appropriate risk assessment would support the achievement of the objectives. In this study, a risk assessment model is developed with PLTSa Kota Surabaya as a (research) object, which was selected because there were still constraints that affect the energy produced and waste volume used as fuel in this PLTSa. This study aims to develop risk assessment models integrated with the Delphi method, Fuzzy Cognitive Maps, and Fuzzy Synthetic Evaluation, then implementing these models in real cases and proposing risk mitigation steps by considering the relationship of the risks. The Delphi method was used to identify risks, FCM was used to identify the relationship among risks and the relationship between risks with the objectives of PLTSa, and FSE was used to assess the level of risks to find risks with the critical category. Delphi method identified 15 risks as a result and reached the consensus based on statistical analysis, FCM generated positive value of the relationship among all of the identified risks and negative value of the relationship between identified risks with the objectives of PLTSa. Meanwhile, FSE method obtained six types of critical risk, which were the machine brake all of a sudden (RC = 3,082), the blower did not work (RC = 3,041), congestion in fuel flow (RC = 2,954), clogged filter (RC = 2.915), lack of machine maintenance knowledge by the operator (RC = 2,872), and the energy produced had not maximum. (RC = 2.828). Those six types of risk used a scenario to run the FCM simulation and meet the dynamic behavior of the risk relationship value. The mitigation proposals are to perform maintenance activities programmatically, to improve the management system related to human resources, and to do the fuel examination according to the standard.

Analysis of The Relationship Between Personal Competencies and Individual Work Performance with Job Specialization, Formalization, and Centralization as Moderators

Abstract. This research examines the impact of personal competencies on individual work performance when performing maintenance activities and examines job specialization, formalization, and centralization as moderator variables. For the analysis, we use questionnaires data from 741 respondents of maintenance officers in the Signalling and Telecommunication Departement at PT KAI (Persero). The results showed that personal competencies significantly affecting individual work performance with a correlation value of 0.001 ( <0.05), and other results related to job Specialization and Centralization as moderator variables demonstrated that these two variables could not be treated as moderators in strengthening the relationship between personal competencies and performance. The results showed correlation value of 0.847 ( >0.05) and 0. 173 (>0.05), respectively. This research's formalization variable was not used in the hypothesis testing stage as a moderator because it lacked instrument validity. This study recommends that maintenance officers have better individual work performance when management focuses on increasing the personal competencies or focuses on increasing competencies in work fields that require special skills by the scope of work they face. Keywords: Personal Competencies. Individual Work Performance. Job Specialization. Formalization. Centralization

Combining chronicle mining and semantics for predictive maintenance in manufacturing processes

Within manufacturing processes, faults and failures may cause severe economic loss. With the vision of Industry 4.0, artificial intelligence techniques such as data mining play a crucial role in automatic fault and failure prediction. However, due to the heterogeneous nature of industrial data, data mining results normally lack both machine and human-understandable representation and interpretation of knowledge. This may cause the semantic gap issue, which stands for the incoherence between the knowledge extracted from industrial data and the interpretation of the knowledge from a user. To address this issue, ontology-based approaches have been used to bridge the semantic gap between data mining results and users. However, only a few existing ontology-based approaches provide satisfactory knowledge modeling and representation for all the essential concepts in predictive maintenance. Moreover, most of the existing research works merely focus on the classification of operating conditions of machines, while lacking the extraction of specific temporal information of failure occurrence. This brings obstacles for users to perform maintenance actions with the consideration of temporal constraints. To tackle these challenges, in this paper we introduce a novel hybrid approach to facilitate predictive maintenance tasks in manufacturing processes. The proposed approach is a combination of data mining and semantics, within which chronicle mining is used to predict the future failures of the monitored industrial machinery, and a Manufacturing Predictive Maintenance Ontology (MPMO) with its rule-based extension is used to predict temporal constraints of failures and to represent the predictive results formally. As a result, Semantic Web Rule Language (SWRL) rules are constructed for predicting the occurrence time of machinery failures in the future. The proposed rules provide explicit knowledge representation and semantic enrichment of failure prediction results, thus easing the understanding of the inferred knowledge. A case study on a semi-conductor manufacturing process is used to demonstrate our approach in detail. The evaluation of results shows that the MPMO ontology is free of bad practices in the structural, functional, and usability-profiling dimensions. The constructed SWRL rules posses more than 80% of True Positive Rate, Precision, and F-measure, which shows promising performance in failure prediction.

Selective Maintenance Policy for a Series-Parallel System Considering Maintenance Priority of Components

Many systems are required to execute a series of missions with a finite break between any two adjacent missions. In order to improve the reliability of the system completing the next mission successfully, it is necessary to perform maintenance actions on the components considering limited maintenance resources. A novel idea to solve the problem of selective maintenance for a series-parallel system is proposed in this paper. This problem consists in finding the best choice of maintenance actions on a multi-component system by maximizing the system reliability under limited resources. With the increased number of components in the system, the possible combinations of selective maintenance increase exponentially, which raises the difficulty of the problem. For better solution of the problem, from the perspective of maintenance benefit, we develop a selective maintenance policy under maintenance priority of components. Finally, numerical example of a series-parallel system is given to illustrate the proposed method and verify the correctness and accuracy of the method.

Application of Reliability-Centered Maintenance for Tugboat Kresna 315 Cooling Systems

Reliability-Centered Maintenance uses the risk management principle of a component’s failure so that the type of maintenance can be determined properly. By using an appropriate type of maintenance, the possibility of failures in a component can be detected and prevented. The proper usage of Reliability-Centered Maintenance can have a positive impact on cost savings, both maintenance costs and repair costs, as well as reducing the consequences of failures. The object of this research is the cooling system aboard Tugboat Kresna 315. The cooling system is an important system on a ship, which is designed to prevent the main engine from overheating. The aim of this research is to apply Reliability-Centered Maintenance methods on the cooling system of Tugboat Kresna 315 and determine the types of maintenance tasks required. The results of Reliability-Centered Maintenance identified 16% of the overall failures to be a type of hidden failure while 86% were identified as evident failures, thus allowing the maintenance division to perform maintenance activities in accordance with the planned maintenance system on each tugboat.