Effects Of Maintenance Activities Research Articles

The use of decision trees to identify the causes of failures in a medical enterprise - a case study

The large amount of historical data contains information about events that occur along an industrial production line. Having a set of historical data about emergency events and their causes makes it possible to automate decision-making processes based on a data-driven approach. Data-driven approaches, particularly machine learning (ML), are attracting attention. Due to its visualization and interpretability characteristics, the decision tree (DT) model is an important ML tool for decision analysis. This paper aims to present the possibility of using DT to increase the efficiency and effectiveness of maintenance activities by identifying the probable cause of failure based on historical data. Based on the research conducted, we have shown that the use of machine learning techniques can improve the accuracy of decisions regarding the type of maintenance work that should be carried out to efficiently and effectively remove failures and reduce losses caused by machine downtime.

The Aramis Data Challenge to prognostics and health management methods for application in evolving environments

A recurrent difficulty for the effective application of Prognostics and Health Management (PHM) methods is related to the “evolving environments” in which industrial components typically operate. Several factors render the operational environments evolving, including deterioration of components, effects of maintenance activities and changes in working conditions. The issue of evolving environments is even more complicated for multi-component systems, where the degradation of one component can affect the degradation processes of other components, thus modifying their lifetime distributions and the statistical properties of the monitored signals. In an effort to convey research toward practical PHM solutions capable of dealing with evolving environments, the “Aramis challenge on degradation state assessment in evolving environments,” has been launched for the ESREL2020-PSAM15 conference. This work describes the Aramis Data Challenge and associated public dataset, illustrates the methods proposed for its solution and the related results obtained. For the evaluation of the goodness of the fault detection methods, an original metric is introduced, which is a variant of the timeliness metric that has been used in the PHM08 data challenge.

Smart Glasses and Augmented Reality: Empowering Maintenance Personnel in Industrial Settings

The efficient maintenance and servicing of industrial equipment are crucial for the smooth operation of manufacturing facilities. However, maintenance personnel often encounter challenges in locating and addressing specific issues, such as equipment jams, within complex systems. Despite the presence of SCADA or HMI systems that can indicate the existence of a fault, identifying the exact location of the issue within the system remains a significant hurdle. This research aims to explore the development and implementation of smart glasses integrated with augmented reality (AR) technology to provide maintenance personnel with real-time, location-based navigation assistance within industrial environments. By leveraging AR, the proposed smart glass software will function as a navigational tool, guiding users directly to the precise location of equipment faults, resembling "Google Maps" for industrial maintenance. This research seeks to investigate the potential impact of AR-assisted navigation on the efficiency and effectiveness of maintenance activities, particularly in complex industrial settings. Furthermore, the study will address the usability, user acceptance, and technical feasibility of integrating AR technology into maintenance processes, ultimately contributing to the advancement of smart maintenance solutions in industrial settings.

Prediction of restoration factor for various maintenance types of railway systems using analytical hierarchy process

Purpose For repairable systems (RSs), reliability estimation is generally performed using virtual age models. Virtual age models consider the effect of maintenance actions by reducing system age using restoration factor (RF). RF is generally estimated from system failure data using various statistical methods. However, RSs such as railway systems experience various types of maintenance actions at different times during their life cycle. To consider all these different types of actions, we need multiple RFs in the virtual age model. As failure data are limited, the estimation of so many parameters becomes a complex problem and it can lead to erroneous inferences. These RFs are representative of effects of maintenance activities on the system. Therefore, these can be predicted from the information about the maintenance actions performed on the system. The paper aims to discuss these issues. Design/methodology/approach The paper considers different types of maintenance actions to predict RF of the system. These maintenance actions involve the replacement of components at some level of assembly. Each component in an assembly has its own impact on assembly restoration. RF for assembly/systems can be obtained by aggregating effects of multiple component replacement using analytical hierarchy process . The RF values obtained for different types of maintenance actions are then used to calculate the virtual age of the system at different failure points. Using these virtual age failure points, suitable distribution is fitted and parameters are estimated. The distribution and parameters provide information about reliability of the system at any point of time. Findings This paper provides an easier approach that gives different RFs for different types of PM and CM. To calculate RFs, it considers the impact of maintenance actions performed as well as the impact of the component on which they are performed. It is simpler and gives more consistent results than other approaches, which estimate RF using different statistical methods. Originality/value This paper provides an alternative approach to predict RF parameters instead of estimating these parameters using statistical methods. Estimation of parameters using different statistical methods is complex in nature and gives erroneous and inconsistent results. The approach given in this paper is simpler and gives more reliable results. This approach can be useful in estimating parameters for RSs when failure data are limited.

Stochastic modeling and analysis of vapor cloud explosions domino effects in chemical plants

Because of the substances they process and the conditions of operation, chemical plants are systems prone to the occurrence of undesirable and potentially dangerous events. Major accidents may occur when a triggering event produces a cascading accident that propagates to other units, a scenario known as domino effect. Assessing the probability of experiencing a domino effect and estimating the magnitude of its consequences is a complex task, as it depends on the nature of the substances being processed, the operating conditions, the failure proneness of equipment units, the execution of preventive maintenance activities, and of course the plant layout. In this work, we propose a stochastic modeling methodology to perform a probabilistic analysis of the likelihood of domino effects caused by propagating vapor cloud explosions. Our methodology combines mathematical models of the physical characteristics of the explosion, with stochastic state-based models representing the actual propagation among equipment units and the effect of maintenance activities. Altogether, the models allow predicting the likelihood of major events occurrence and the associated costs. A case study is analyzed, where various layouts of atmospheric gasoline tanks are assessed in terms of the predicted consequences of domino effects occurrence. The results of the analyses show that our approach can provide precious insights to support decision-making for safety and cost management.

A modelling approach to optimal imperfect maintenance of repairable equipment with multiple failure modes

Most of the existing works on optimal imperfect maintenance activities of a repairable equipment with independent components consider a single model for equipment behaviour. In addition, it is assumed that all the components of the equipment share the same model and the same maintenance intervals and that effectiveness of maintenance is known. In this paper we take a different approach. In order to formalize the uncertainty on the occurrence of failures and on the effect of maintenance activities, we consider, for each component, a class of candidate models. These models are obtained by combining failure rate models with imperfect maintenance models. The best model, that might be different for the different components, is then selected. All the parameters are assumed to be unknown and are jointly estimated via maximum likelihood. Model selection is performed, separately for each component, using standard selection criteria that take the problem of over-parametrization into account. The selected models are used to derive the cost per time unit and the average reliability of the equipment, the objective functions of a Multi-Objective Optimization Problem with maintenance intervals of each single component as decision variables. The proposed procedure is illustrated using a real data example.

Auxiliary power unit failure prediction using quantified generalized renewal process

Auxiliary Power Unit (APU) is an essential component utilized in modern civil aircraft. In order to meet APU availability requirements, failure prediction should be performed in an effective way. To this end, APU performance deterioration can be modeled by a Generalized Renewal Process (GRP). In this paper, aircraft APU failure prediction is implemented using a Weibull-based GRP (WGRP). However, the effect of maintenance activities on aircraft APU required in WGRP is difficult to be quantified. To solve this problem, a new model named Quantified Generalized Renewal Process (QGRP) is developed in this paper. In this model, APU performance-related test parameters after repairs are utilized to quantify the maintenance effect. Based on the proposed QGRP model, the conditional failure rate and hazard rate of each aircraft APU at a future point in time can be calculated based on the APU's virtual age and be combined to predict the number of failures of a fleet of APUs. The performance of the proposed QGRP model is validated using a three-year data set provided by China Southern Airlines. The results show that the QGRP model is effective in aircraft APU failure prediction.

Measuring of Software Maintainability Using Adaptive Fuzzy Neural Network

Software maintenance mainly refers to the process of correcting software after delivery. Maintenance process is usually a high percentage of Organizational effort to the whole process of software programs. As a result, the effectiveness of the entire production process and customer satisfaction is dependent on the effectiveness of maintenance activities. Because many factors including type of service, type of product and human factors is dependent on the maintenance process, And the imprecise nature of qualitative factors and sub-criteria leading software maintenance, accurate assessment can be maintained in order to measure the effectiveness of programs seem highly desirable. In this paper, using adaptive fuzzy neural network to provide a method for evaluating the capability of software maintenance conducted after the tests, the root mean square error of the proposed method was equal to 0.34331. The results show that the method is based on adaptive fuzzy neural, maintainability software performance evaluation is appropriate.

Application of network traffic flow model to road maintenance

The study shows how the evolution of two-way traffic flows on a local highway network can be predicted over time using a network-level traffic flow model (NTFM) to model both urban and motorway road networks. After a brief review of the main principles of the NTFM and its associated sub-models, the paper describes how a maintenance worksite can be modelled using a roadwork-node sub-model and a network solution routine in the NTFM. In order to model the two-way traffic flow in the road network, an iterative simulation method is used to generate the evolution of dependent traffic flows and queues. The NTFM has been applied to model the traffic characteristics and the effects of maintenance activities on the local Loughborough–Nottingham highway network. The study has demonstrated that the methodology is useful in selecting various worksite arrangements in order to reduce the effects of maintenance on road users.

Investigating Imperfect Inspection of Avionics System and its Relationship with Availability Percentage

System maintenance is necessary to prolong the operational life of a system. There are four types of maintenance policies which are corrective, preventive, scheduled and predictive (conditioning based maintenance). However, this paper focuses on preventive maintenance and corrective maintenance policies. In general, the goal of preventive maintenance is to retain the system at its good operating conditions before the occurrence of any failure while corrective maintenance is done as a result of the system’s operating failure. This paper investigates the effects of maintenance activities for Engine Indication and Flight Instruments subsystems of the avionics system of a general aircraft. The simulation of the maintenance process is done using Monte Carlo and Discrete Event simulation methods for different preventive maintenance interval (PMI).

Monte Carlo simulation of railway track geometry deterioration and restoration

Travelling safely and comfortably on high-speed railway lines requires excellent conditions of the whole railway infrastructure in general and of the railway track geometry in particular. The maintenance process required to achieve such excellent conditions is complex and expensive, demanding a large amount of both human and technical resources. In this framework, choosing the right maintenance strategy becomes a critical issue. A reliable simulation of the railway geometry ageing process would offer a great advantage for the optimization of planning and scheduling of maintenance activities. A fundamental requirement for such simulation is a statistical model describing the behaviour of the railway track geometry deterioration as well as the effects of maintenance activities. The French railway operator SNCF has been periodically measuring the geometrical characteristics of its high-speed network since its commissioning (i.e. for more than 20 years now). These records are an excellent data source to achieve a sound statistical description of the process. In this paper a new system identification method to obtain such simulations is presented. The proposed method uses a grey-box model: a model structure and its constraints are specified basing on previous knowledge of the process to be identified, and then the set of parameter values which best fits the signal measurements is searched. As previous knowledge indicates that the process is non-linear, parameter values are searched by means of the Levenberg–Marquardt algorithm, an iterative technique that finds a local minimum of a function that is expressed as the sum of squares of non-linear functions. Furthermore, the presented model is extended in order to analyse the effect of the variation of factors influencing the ageing process (e.g. operational speed). Finally, the method is applied and validated with real data of a French high-speed TGV line.

A perspective on job assignments under varying scheduled maintenance scenarios

The purpose of this paper is to present the effects of maintenance activities on assignment and sequencing decisions in a job shop environment. We analyze and compare three different maintenance policies: one with no scheduled maintenance, one with scheduled maintenance activities but the time for maintenance is not considered in the assignment decisions, and one where scheduled maintenance activities are performed and the time for maintenance is part of the assignment decisions. The results indicate that companies benefit more when assignment and sequencing decisions integrate the time for maintenance activities.

Customer-Oriented Maintenance Decision Support System: Developing a Prototype

Performance of highway maintenance units has been traditionally measured by output or production rates. Today, more and more highway agencies are incorporating customer-oriented performance measures to evaluate their maintenance management processes. The Minnesota Department of Transportation (MnDOT) has been adapting and applying private-sector business practices to the management of its maintenance functions. The department initiated a maintenance business planning (MBP) project several years ago to focus on rethinking highway maintenance from the customer’s perspective. A prototype decision support system (DSS) was developed that builds on the MBP by modeling performance measurement parameters and implementing analytical processes that can support MnDOT’s maintenance managers in responding to customer needs. The DSS uses input, output, outcome, environment, and customer data from existing databases to measure efficiency and effectiveness of maintenance activities, as defined by the customer. The pilot system focuses on two of seven groups of maintenance products and services—clear roads and attractive roadsides. The prototype DSS displays data in formats useful for evaluating maintenance work processes, benchmarking work units’ performance, evaluating efficiency and effectiveness in meeting customer needs, and deploying resources. Models that calculate the value added to customers in terms of road-user costs (i.e., travel time and accident costs) and customer willingness to pay for attractive roadside products were developed and incorporated in the DSS. If fully implemented, the DSS can be a powerful tool for maintenance decision making that can be applied to other state maintenance agencies.

The effect of roadway salting on safety and mobility

The research detailed in this report represents a preliminary effort to develop an understanding of the effects of roadway salting on road safety and mobility.Analyses included an assessment of available literature, the review of accident data from a number of sources, and the development of a study procedure for assessing the effects of winter conditions and maintenance activities on vehicle operations. The procedure developed was based on vehicle headway observations.Field trials of the study procedure in six different jurisdictions confirmed it to be sensitive to roadway conditions, and to be capable of reflecting the effects of maintenance activities. Results of the studies showed little variation for mean headways under dry, wet, and slushy road conditions. Mean headways under show or icy road conditions were much larger. Limited study of the effects of maintenance activities suggested that different roadway salting strategies had little differential effect on vehicle headways. Finally a variety of study refinements were identified.Analyses of accident data was not conclusive with data from the 11 jurisdictions reviewed showing remarkable consistency in the proportion of accidents experienced under winter versus nonwinter conditions. This consistency was unexpected in view of the variations in weather conditions, maintenance strategies, and metropolitan characteristics of the study group. Key words: salt, snow, ice, sodium chloride, level of service, winter maintenance, deicers, and snow and ice control.