Scheduling Of Maintenance Activities Research Articles

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.

Prediction of Railway Track Condition for Preventive Maintenance by Using a Data-Driven Approach

For scheduling track maintenance, infrastructure managers perform inspections to assess the track condition. When the irregularities are higher than the threshold limits, a track has a defect that should be corrected to avoid future failures or traffic disruption. Scheduling maintenance actions contribute to reliability and availability but demand the prediction of the evolution of track degradation. In recent years, several degradation models have been defined to forecast geometrical evolution over time and/or tonnage, mainly for heavy rail systems. Nevertheless, most of those models have limitations when dealing with measured data collected in different time intervals as happens in reality. To overcome this problem, a data-driven model based on the logistic binary function is presented and validated with real inspection measurements. The results prove that the model has a 91.1% success rate, an excellent discrimination ability, and a high sensitivity, classifying correctly 84.1% of inspections in need of maintenance. The model also has high specificity as it classifies 94.5% of inspections with no demand of maintenance action. The model is easy to implement, which is also an advantage for the track asset management with guaranty of excellent sensitivity and discrimination.

Text mining techniques for the management of predictive maintenance

The advent of Industry 4.0 provides new opportunities to improve the maintenance of production equipment from both the technical and managerial perspective. In this paper, we propose a contribution in the direction of predictive maintenance of machine tools based on the integration of a text mining algorithm with the cyber-physical system of a manufacturing industry. The system performs its analysis starting from data stored in log files maintained by a machine tool returning an alert about a future potential machine failure. Log files, produced by part programs running on the machine control system, record the status of execution parameters taken by key sensors or derived by the control system during the part program execution. Historical data are collected by means of Digital Twin technologies and then analyzed using computational linguistic techniques so that we can predict a machine failure in the imminent future starting from data collected in the past. The paper first describes a new scheme for the classification of maintenance approaches. Then, starting from the proposed cyber-physical system model, an algorithm for predictive maintenance based on text mining technology is integrated in it. The implemented tool supports the maintenance manager in making the most appropriate decisions about the scheduling of maintenance activities when there is an alert about a possible machine failure.

Schedule Planning and Maintenance Activities Auxiliary Power Unit (APU) Boeing 737-500 Aircraft With Reliability Method

Penelitian ini bertujuan untuk merencanakan jadwal dan aktifitas maintenance yang yangefektif pada sistem auxiliary power unit sehingga tidak terjadi lagi kegagalan ataupun kerusakan yang tidak di rencanakan atau terjadi secara tiba – tiba. Kegagalan pada peralatan auxiliary power unit ada sering terjadi pada beberapa sistem kerja yaitu electrical system, Lubrication System dan Ignition System, di mana hal ini menimbulkan kerugian yang cukup besar bagi perusahaan penerbangan. Metode penelitian ini menggunakan pendekatan kualitatif dan kuantitatif, analisis kualitatif menggunakan metode Failure Mode Effect and Critically Analysis (FMECA) dengan menganalisis faktor – faktor penyebab kegagalan dan efek terjadinya kegagalan, dengan hasil penyebab kegagalan pada beberapa sitem kerja auxiliary power unit (APU) adalah sebagai berikut electrical system adalah pada komponen start Relay, Lubrication System adalah pada komponen Oil Filter, Ignition System adalah pada igniter plug. Dari hasil analisis FMECA tersebut di lakukan analisis kuantitatif dengan analisis dilakukan menggunakan metode reliability, parameter kehandalan dihitung dengan probabilitas distribusi Weibull, untuk menentukan batas kritis waktu operasional komponen ataupun part sistem yang merupakan batas kehandalan suatu sistem auxiliary power unit. Batas kritis operasional electrical system adalah sebesar 434 jam terbang, lubrication system adalah 1186 jam terbang, dan Ignition system adalah sebesar 1610 jam terbang, selanjutnya hasil tersebut di gunakan untuk menentukan jadwal maintenance yang efektif di dukung dengan perencanaan aktifitas maintenance yang tepat untuk menghilangkan penyebab – penyebab kegagalan pada peralalatan auxiliary power unit.

Analisa Efektifitas Perbaikan Perangkat BTS Telkomsel Karawang dengan iManager u2000 software

Recently, the need for cellular telecommunication services in Indonesia being a very large number. The customer growth rate must be supported with the infrastructure development. PT. Telkomsel (one of the provider) has its own way of balancing customer needs with infrastructure development, namely building BTS or Base Transceiver Station. This solution has a negative impact, which affected the PT. Telkomsel loses revenue or loss profit if the BTS experiences damage or interference as a result of continuous use. To reduce this profit loss value, PT. Telkomsel carries out scheduled maintenance or maintenance activities on BTS devices. Based on these problems, the practical work carried out by the author aims to find out and understand the repair or repair procedures on computerized BTS devices at PT. Telkomsel, Karawang. The result of practical work activities is that it is known that the application of a computerized system at PT. Telekomunikasi Selular GraPaRi Karawang helps the process of repairing BTS devices in meeting information needs. To monitor damage to BTS equipment, it is done by means of an alarm monitoring system using the iManager u2000 software.

A simulation-driven online scheduling algorithm for the maintenance and operation of wind farm systems

Wind turbines experience stochastic loading due to seasonal variations in wind speed and direction. These harsh operational conditions lead to failures of wind turbines, which are difficult to predict. Consequently, it is challenging to schedule maintenance actions that will avoid failures. In this article, a simulation-driven online maintenance scheduling algorithm for wind farm operational planning is derived. Online scheduling is a suitable framework for this problem since it integrates data that evolve over time into the maintenance scheduling decisions. The computational study presented in this article compares the performance of the simulation-driven online scheduling algorithm against two benchmark algorithms commonly used in practice: scheduled maintenance and condition-based monitoring maintenance. An existing discrete event system specification simulation model was used to test and study the benefits of the proposed algorithm. The computational study demonstrates the importance of avoiding over-simplistic assumptions when making maintenance decisions for wind farms. For instance, most literature assumes maintenance lead times are constant. The computational results show that allowing lead times to be adjusted in an online fashion improves the performance of wind farm operations in terms of the number of turbine failures, availability capacity, and power generation.

Advantages and Losses Analysis on Burning Hours by The Production Planning Control in Determining Time Implementation of Maintenance Activities

Burning Hours is the one of policy in scheduling aircraft maintenance. With the burning hours the operator could performed aircraft maintenance before the due time. Burning Hours is indeed beneficial for flight operators to maintain the stability of the safety of the aircraft operation. Using the quantitative analysis method with the sample data’s from two aircraft, found that the burning hours affects to Burning Hours Cost loss for every aircraft meintenance activity. The value of Burning Hours Cost Loss and Potential Lost per Day needs to be considered by operators in scheduling maintenance activities, because it is possible that Burning Hours cannot be avoided in aircraft operation. Based on the data processing, the value of burning hours cost lost is not more than limit of potential lost per day. However it should also be considered if burning hours applied continuously will increase maintenance cost. Therefore, between maintenance planning department and the operation department should keep good coordination to minimize the burning hours cost loss. Keywords: Planning, Maintenance, Burning Hours

Analisa Efektifitas Perbaikan Perangkat BTS Telkomsel Karawang dengan iManager u2000 software

Recently, the need for cellular telecommunication services in Indonesia refers to a very large number. This customer growth rate must be balanced with infrastructure development. PT. Telkomsel carries out scheduled maintenance or maintenance activities on BTS devices. Based on these problems, the research carried out by the author aims to find out and understand the repair or repair procedures on computerized BTS devices at PT. Telkomsel, Karawang. The result of the research is that it is known that the application of a computerized system at PT. Telekomunikasi Selular GraPaRi Karawang helps the process of repairing BTS devices in meeting information needs. To monitor damage to BTS equipment, it is done by means of an alarm monitoring system using the iManager u2000 software.

Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model

In manufacturing, cutting tools gradually wear out during the cutting process and decrease in cutting precision. A cutting tool has to be replaced if its degradation exceeds a certain threshold, which is determined by the required cutting precision. To effectively schedule production and maintenance actions, it is vital to model the wear process of cutting tools and predict their remaining useful life (RUL). However, it is difficult to determine the RUL of cutting tools with cutting precision as a failure criterion, as cutting precision is not directly measurable. This paper proposed a RUL prediction method for a cutting tool, developed based on a degradation model, with the roughness of the cutting surface as a failure criterion. The surface roughness was linked to the wearing process of a cutting tool through a random threshold, and accounts for the impact of the dynamic working environment and variable materials of working pieces. The wear process is modeled using a random-effects inverse Gaussian (IG) process. The degradation rate is assumed to be unit-specific, considering the dynamic wear mechanism and a heterogeneous population. To adaptively update the model parameters for online RUL prediction, an expectation–maximization (EM) algorithm has been developed. The proposed method is illustrated using an example study. The experiments were performed on specimens of 7109 aluminum alloy by milling in the normalized state. The results reveal that the proposed method effectively evaluates the RUL of cutting tools according to the specified surface roughness, therefore improving cutting quality and efficiency.

Condition Based Maintenance Planning of low volume rural roads using GIS

The functional rural road network has a significant effect on the socio-economic growth of a nation. Newly constructed road pavements deteriorate with time, their regular maintenance will not only keep them functional but also increase their service life. Planning and scheduling of maintenance activities is an important part of any maintenance program for a large network of roads. The present study proposes a maintenance plan for low volume rural roads at network level as well as project level. In the first step of network level planning, the roads in the network are prioritized for pavement maintenance, depending primarily on their pavement condition and traffic volume. In the second step of project level planning, a suitable repair and maintenance technique is selected for each road based on the predominant pavement defects, traffic volume, and climatic conditions. ArcGIS was used to visualize the roads on a map and manage the data collected for each road. The maintenance planning was automated through a tool developed within ArcGIS. Verification of the methodology was done by applying it to a case study of 203 rural roads in the state of Himachal Pradesh in India. The results show that the pavement condition of a road (56.4%) is the most critical factor for prioritizing roads for maintenance. Pavement condition is followed by socio-economic facilities located along the road (26.2%), average daily traffic volume (11.7%), and type of connectivity provided by the road (5.7%). The outcome of the maintenance plan proposed in this study is the priority ranking along with the most suitable repair technique for each road in the network.

The Effect of Climatic Factors on the Electric Load of Thermal Power Plants (TPP) in the Southern Federal District by the Example of the Novocherkassk District Power Plant (GRES)

A statistical analysis was performed of the electrical load curve according to which the Novocherkassk District Power Plant (NсhGRES) operated in the period between 2009 and 2018. The effect of weather and climatic factors, such as daylight time, ambient air temperature, and wind strength, on the electrical load was examined. Climatic components of the NсhGRES electric load were estimated, and it has been demonstrated that the additional lighting load depending on the daylight time in Rostov oblast was as great as 35.4%, the seasonal heating load was up to 11.3%, and the seasonal cooling load was up to 20.2% of the normal baseload. It is shown that the basic heating load in Rostov oblast is carried by centralized and individual heat supply systems, and electric heating appliances are activated when the centralized heat supply systems cannot cover the heating load due to poor maneuverability. The heating component of the electric load depends not only on the ambient air temperature but also on the wind velocity. A formula was derived using the fuzzy logic technique for calculating heat consumption in a heating system depending on ambient air temperature and wind speed. It is noted that high wind speed aggravates the negative temperature factor, bringing about the need to turn on additional heating appliances and, thereby, increasing the electrical load. A fundamental change in the shape of the annual electric load curve within the studied period was revealed: the appearance of the summer maximum load whose amplitude in terms of the indicators approached the winter one and even exceeded it in certain years, which can be attributed to the climate change in the region of the power plant site and the advent of a new type of electrical equipment, i.e., climatic control cooling units. The maximum monthly power consumption of cooling units is 1.8 times the maximum monthly power consumption of heating units and 1.7 times less than the maximum additional monthly power consumption of lighting facilities. The maximum power consumption for each type of equipment occurs in different months of the year. This change is a stable trend that should be considered, in particular, in scheduling maintenance activities at the power plant.

Reliability Estimation under Scarcity of Data: A Comparison of Three Approaches

During the last decades, the optimization of the maintenance plan in process plants has lured the attention of many researchers due to its vital role in assuring the safety of operations. Within the process of scheduling maintenance activities, one of the most significant challenges is estimating the reliability of the involved systems, especially in case of data scarcity. Overestimating the average time between two consecutive failures of an individual component could compromise safety, while an underestimate leads to an increase of operational costs. Thus, a reliable tool able to determine the parameters of failure modelling with high accuracy when few data are available would be welcome. For this purpose, this paper aims at comparing the implementation of three practical estimation frameworks in case of sparse data to point out the most efficient approach. Hierarchical Bayesian modelling (HBM), maximum likelihood estimation (MLE), and least square estimation (LSE) are applied on data generated by a simulated stochastic process of a natural gas regulating and metering station (NGRMS), which was adopted as a case of study. The results identify the Bayesian methodology as the most accurate for predicting the failure rate of the considered devices, especially for the equipment characterized by less data available. The outcomes of this research will assist maintenance engineers and asset managers in choosing the optimal approach to conduct reliability analysis either when sufficient data or limited data are observed.

Multi-agent maintenance scheduling based on the coordination between central operator and decentralized producers in an electricity market

Condition-based and predictive maintenance enable early detection of critical system conditions and thereby enable decision makers to forestall faults and mitigate them. However, decision makers also need to take the operational and production needs into consideration for optimal decision-making when scheduling maintenance activities. Particularly in network systems, such as power grids, decisions on the maintenance of single assets can affect the entire network and are, therefore, more complex.This paper proposes a bi-level multi-agent decision support system for the generation maintenance decision (GMS) of power grids in an electricity market in the context of predictive maintenance. The GMS plays an important role in increasing the reliability at the network level. The aim of the GMS is to minimize the generation cost while maximizing the system reliability. The proposed framework integrates a central coordination system, i.e. the transmission system operator (TSO), and distributed agents representing power generation units that act to maximize their profit and decide about the optimal maintenance time slots while ensuring the energy balance. In the proposed bi-level approach, n upper levels and one lower level (i.e. sub-problems) are solved by the independent agents and by the TSO, respectively. We derive the optimal strategy of the agents that are subject to predictive maintenance via a distributed algorithm, through which agents make optimal maintenance decisions and communicate them to the central coordinator, i.e. the TSO. The TSO decides whether to accept the agents’ decisions by considering market reliability aspects and power supply constraints. To solve this coordination problem, we propose a negotiation algorithm using an incentive signal to coordinate the agents’ and the central system’s decisions, such that all the agents’ decisions can be accepted by the central system. We demonstrate the effectiveness of the proposed algorithm with reference to the IEEE 39 bus system.

Robust Maintenance Scheduling of Aircraft Fleet: A Hybrid Simulation-Optimization Approach

We study the maintenance task scheduling problem for an aircraft fleet in an uncertain environment from the viewpoint of robust optimization. Given a daily horizon, the maintenance tasks delegated to a shop should be scheduled in such a way that sufficient aircrafts are available on time to meet the demand of planned missions. The tasks are either scheduled maintenance activities or unexpected repair jobs when a major fault is detected during pre- or after-flight check of each mission. The availability of skilled labour in the shop is the main constraint. We propose a robust formulation so that the maintenance tasks duration is subject to unstructured uncertainty due to the environmental and human factors. As a result of the specific structure of the primary model and non-convexity of the feasible space, the classical robust optimization methods cannot be applied. Thus, we propose an $\epsilon $ -Conservative model in tandem with Monte-Carlo sampling to extract the set of all feasible solutions corresponding to various disturbance vectors. Since the one-way sampling-then-optimization approach does not guarantee the probabilistic feasibility, we employ a hybrid simulation-optimization approach to ensure that the solutions provided by the $\epsilon $ -Conservative model are robust to all uncertainty scenarios. The experimental results confirm the scalability of the proposed methodology by generating the robust optimal solutions, satisfying all conservatism levels and uncertainty scenarios irrespective of the problem size.

Using digital twins and data analytics to make informed operational decisions – a centrifugal compressor case study

An Australian operator with an expansive portfolio of liquefied natural gas, pipeline gas and oil assets wanted to gain greater visibility of how their compressor fleet was operating. To help the operator gain a greater understanding of the performance of their assets, Worley Digital built digital twins of 17 of their compressors. This involved detailed first principle calculations supported by hundreds of thousands of offline simulations. This created the thermodynamic correlations necessary to accurately model the most important operating statistics surrounding each stage of those assets. We then used dimensional analysis and data analytics to benchmark the operation of these machines. This represents a hybrid approach; first principles engineering and simulation coupled with data science and analytics. A suite of tools was then delivered to the operator in their preferred real-time data analytics and visualisation platform, providing them with information about the efficiency of their compressors. They were then able to perform net present value calculations based on different operating and maintenance strategies. This gave them the detailed information they needed in order to be able to make more informed decisions. An example of this might be a decision to bring a scheduled maintenance activity forward based on the knowledge that there are net operational and financial incentives to do so. The resulting tool has proved extremely valuable, quantifying some business-critical metrics that have led to several financially impactful operational decisions.

Time‐delay concept‐based approach to maintenance scheduling of HV cables

The associated daily maintenance effort and cost has been increasing with the growing use of high-voltage (HV) cable circuits. This study proposes a time-delay concept-based approach to maintenance scheduling of individual HV cable components, to allow periodicity of inspection to be optimised with consideration of time-delay interval between the instant of defect inception and final failure. Here, the progress from a defect to its resultant final failure can be represented as a two-stage process, one being a stochastic process of defect arrival and the other being a delay-time interval from defect to failure. Then an optimisation model is established to schedule maintenance activities based on the two statistical processes, where the objective function is set as the minimal maintenance cost. Cable circuits are divided into five components in accordance with the objects of inspection and testing. The components within different service ages are considered individually in the statistical models, and their accumulate failure numbers during each service year are used to automatically formulate and update periodicity of inspection. To illustrate the feasibility of the approach proposed, theoretical analysis on thousands of cable circuits are carried out. Results show that the economic expenditure could be reduced by 22.2% after optimisation.

Metaheuristics for the flow shop scheduling problem with maintenance activities integrated

This paper deals with a flow shop scheduling problem in which machines are not available during the whole planning horizon and the periods of unavailability are due to random faults. Since they are subject to failures, both corrective maintenance activities and scheduled maintenance activities are performed to increase their availability. Hence, jobs and maintenance tasks are jointly considered to find the optimal schedule. The objective is to find the optimal integrated job-planned maintenance sequence that minimises the makespan and the earliness-tardiness penalty. To this aim, we propose two novel meta-heuristic algorithms obtained modifying a standard Genetic Algorithm (GA) and Harmony Search (HS). Numerical results obtained from experiments considering different problem sizes and configurations show that the proposed Harmony Search and Genetic Algorithm are efficient to approach the integrated job-maintenance scheduling problem.

Incorporating pavement deterioration uncertainty into pavement management optimization

ABSTRACT Pavement management systems can be used to efficiently allocate limited maintenance budgets to better align with pavement deterioration. However, pavement deterioration is subject to uncertain factors that complicate the prediction of future pavement conditions accurately, entailing differences in the optimum maintenance strategy. This paper addresses this challenge by introducing a method to aid local engineers in optimising the scheduling of maintenance activities under uncertain pavement deterioration conditions. Markov chains are used to simulate the variability of life-cycle performance. Moreover, a multi-objective optimisation of an urban network is carried out to find the maintenance programme that minimises the mean life-cycle cost, maximises the mean user benefit, and minimises the standard deviation of life-cycle cost. This third objective enables the optimisation routine to minimise the possibility of unintentionally increasing the life-cycle cost due to system variability. This approach results in a reduction of the life-cycle cost variability by up to 62%, provides pavement strategies that benefit road users as a result of better pavement conditions, and reduces the risk of resorting to costly future maintenance activities.

Adaptive and noncyclic preventive maintenance to augment production activities

PurposeProduction schedules, if not met as per timelines may result in heavy losses to a company in terms of its standing and the overall profit. Production scheduling is generally planned by not taking preventive maintenance schedules into consideration. Most of the plants allocate discrete hours/time for preventive maintenance activities. These hours allocated for preventive maintenance will be in addition to the hours which would be lost during breakdown maintenance. These lost hours may be reduced if production scheduling and preventive maintenance activities are integrated. This advocates that we need to devise a methodology which can take care of lost hours.Design/methodology/approachAdaptive and noncyclic maintenance strategy describes the modification of existing maintenance practices, policies and procedures to meet new dynamic tasks/opportunities. It demands a high degree of flexibility and mental agility from maintenance staff members. The maintenance team has to be on a lookout for an opportunity message received from the central server and has to act promptly. The moment an opportunity arises, a message is forwarded to a central maintenance server (opportunity is captured). The central server then assigns individuals/team, based on their expertise and the maintenance task due on that machine/equipment. This action is completely automated and implemented without delay.FindingsThe total man-hours saved by executing adaptive and noncyclic preventive maintenance methodology comes to 705 h during 15 days on 30 machines installed in three different sections. There was a contribution of 71 innovative ideas from the repair teams. Out of these 71 innovative ideas, 16 were found suitable for execution. A quantum jump in the morale and motivation of the maintenance team was noticed from the feedback forms. Mutual understanding and respect for each other among employees has been enhanced. The optimization of resources and infrastructure including tools, gauges, testing equipment, etc. could truly be attained.Practical implicationsThe developed adaptive and noncyclic preventive maintenance model assists in capturing lost hours and make the system proactive and lean. The suggested model optimizes the preventive and predictive maintenance activities and results in substantial saving of efforts, manpower, resources and allocated budget.Originality/valueThe adaptive and noncyclic preventive maintenance model discussed in the article is a novel approach for the optimization of resources. The technique assists in capturing lost hours and utilization of these hours for preventive maintenance tasks. The model will also encourage creative and innovative ideas from employees and take the organization toward Continual Maintenance Optimization.

Performance-Based Approach for Deciding Concrete Age for Healer Sealer Application on New Concrete

Healer sealer application is one of the capital schedule maintenance (CSM) activities for enhancing concrete bridge deck durability and extending the service life. A healer sealer is expected to seal the cracks and reduce the rate of chloride ion ingress. Highway agency policies and manufacturer specifications require maintaining a total of 28-day curing period (7-day wet and 21-day dry curing) before the application of a healer sealer on bridge decks with new concrete in patches and repairs. Consequently, the contractors have to wait for 28 days to apply a healer sealer. Delaying application time increases project completion time and cost of construction and mobility. This paper presents a performance-based approach for evaluating the possibility of reducing the 28-day waiting period for healer sealer application on bridge decks with new concrete. An experimental program was developed and executed to evaluate the impact of healer sealer application parameters on crack sealing ability. A healer sealer was applied at 14, 21, and 28 days of concrete age. The treated surface was ponded with 3% NaCl. The performance of the treated concrete was evaluated using the acid-soluble chloride content test. The results showed the possibility of applying a sealer before the end of the 28-day curing period stipulated in the current specifications. The performance-based approach presented in this paper can be used to identify the age of concrete for healer sealer application.