Periodic Maintenance Activities Research Articles

Combining Similarity Measures and Left-Right Hidden Markov Models for Prognostics of Items Subjected to Perfect and Imperfect Maintenance

In the nuclear industry, high system reliability requirements are essential since in-service failure can result in undesirable consequences in terms of costs or safety. However, the current approach to maintaining systems and components is costly and known to involve overly conservative periodic maintenance activities. It is, therefore, appropriate to develop monitoring, detection, and predictive tools to enable operators to create optimal maintenance strategies. These strategies can vary from the substitution of an item to its repair, intending to avoid unexpected consequences. The repair can restore the item’s functionality to an as-good-as-new condition (perfect repair) or sometimes can keep some accumulated degradation and change the item’s degradation rate (imperfect or partial repair). Current techniques and models that can perform prognostics with extraordinary accuracy are often designed on the assumption that following maintenance, the item is restored to an as-good-as-new condition. When these models are used to predict items that follow imperfect repairs, the predictions are likely to be inaccurate. Therefore, the present work focuses on the condition-based prognostics of items, considering and handling the criticalities that arise after the items undergo different kinds of repairs. The proposed solution involves a data-driven framework that employs Left-Right Gaussian Hidden Markov Models (LR-GHMMs). These models can intrinsically manage accumulated degradation. The idea is to train different LR-GHMMs, each specific to a degradation path, and then combine them to cover possible intermediate paths. The effectiveness of the approach is tested in two case studies. In the first one, we consider simple artificial sequences that are useful to explain the method’s capabilities. In the second case study, we consider semi-simulated nuclear data describing the degradation transients of a condenser that undergoes fouling. The framework is trained with data collected from items that start without accumulated degradation. The test data represent either new items or items that undergo imperfect repairs. The results demonstrate an attractive elasticity of the framework in adapting to nonstandard degradation behaviors. In addition, the applications provide interpretable and highly accurate outputs.

Mathematical programming formulations for the reclaimer scheduling problem with sequence-dependent setup times and availability constraints

In the context of Industry 4.0, which encompasses advanced technologies and interconnected systems, the integration of optimization techniques assumes a crucial role in addressing resource management challenges across various sectors, including manufacturing systems. In bulk ports, which are an integral part of the manufacturing and logistics chain, one of the most critical resource management challenges is the scheduling of automated or semi-automated reclaimers. These machines are responsible for reclaiming dry bulk materials for loading onto vessels using ship-loaders. The efficient operation of reclaimers directly impacts the overall efficiency of the port. However, the current works for the reclaimer scheduling problem (RSP) overlook the necessity of maintenance activities and assume continuous machine availability. Nonetheless, the inclusion of preventive maintenance activities is critical to ensuring optimal scheduling decisions that minimize production disruptions and downtime. To address these challenges, this paper proposes a novel approach that considers periodic preventive maintenance activities of the reclaimers. We consider two cases to tackle this problem. The first case involves the optimization of material handling within a system of two stockpads and one reclaimer machine. For this case, two novel Mixed Integer Programming (MIP) formulations are developed to drive efficient scheduling decisions and maximize productivity. Furthermore, for the second case representing a more complex system comprising three stockpads and two reclaimers, a novel MIP formulation is devised. The effectiveness of the proposed mathematical formulations is rigorously tested through the use of randomly generated instances based on a real-world case.

Impact of Machine Overhauls on the Breakdowns in the Manufacturing Industry

This study focuses on a new maintenance strategy called Machine overhaul, which uses the concepts of Reliability Centered Maintenance and how it is different from other traditional maintenance strategies that various manufacturing companies use to improve machine reliability and increase productivity by creating opportunities for longer production runs without suffering from breakdowns through performing minimal periodic maintenance activities and reducing overall cost. The framework of this study addresses the development of this strategy by working with different Original Equipment Manufacturers as they are the subject matter experts in their respective fields and how it is planned and executed by the Maintenance team in an organization. It helps the management and leadership team of an organization to make decisions using the data-driven approach through analysis and comparison of the impact of this strategy against traditional ones to improve reliability and productivity to achieve organizational objectives every year.

Simultaneous optimization of design and maintenance for systems using multi-objective evolutionary algorithms and discrete simulation

When projecting and building new industrial facilities, getting integrated design alternatives and maintenance strategies are of critical importance to achieve the physical assets optimal performance, which is needed to be competitive in the actual global markets. Coupling Evolutionary Algorithms with Discrete Event Simulation has been explored both in relation to systems design and their maintenance strategy. However, it was not simultaneously considered when both the corrective and the preventive maintenance—consisting of achieving the optimum period of time to carry out a preventive maintenance activity—are taken into account before being considered by the authors of the present paper. This work couples Multi-objective Evolutionary Algorithms with Discrete Event Simulation in order to enhance the knowledge and efficiency of the methodology presented, which consists of exploring and optimizing simultaneously systems design alternatives and their preventive maintenance strategies. The aim consists of finding the best set of non-dominated solutions by using the system availability (first maximized objective function) with taking into consideration associated operational cost (second minimized objective function), while automatically selecting the system devices. Each solution proposed by the Multi-Objective Evolutionary Algorithm is analyzed by using Discrete Event Simulation in a procedure that looks at the effect of including periodic preventive maintenance activities all along the mission time. An industrial application case study is solved, and a comparison of the performance of five state-of-the-art and three more recently developed Multi-objective Evolutionary Algorithms is handled; moreover, the gap in the literature reviewed about the analysis regarding the effect of the discrete event simulation sampling size is faced with useful insights about the synergies of Multi-objective Evolutionary Algorithms and Discrete Event Simulation. Finally, the methodology is expanded to more complex systems which are successfully solved.

Single-machine scheduling with periodic maintenance and learning effect

This paper discusses a single-machine scheduling problem with periodic maintenance activities and position-based learning effect to minimize the makespan. To obtain exact solutions of small-scale problems, one new two-stage binary integer programming model is formulated. In addition, a branch and bound algorithm combining boundary method and pruning rules is also proposed. According to the property of the optimal solution, a special search neighborhood is constructed. A hybrid genetic-tabu search algorithm based on genetic mechanism with tabu technique as an operator is proposed to solve medium-scale and large-scale problems. Moreover, to improve the efficiency of genetic algorithm and hybrid genetic-tabu search algorithm, Taguchi method is used for parameter tuning. Furthermore, computational experiments are carried out to compare the efficiency and performance of these algorithms.

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.

Constructive and composite heuristics for the 2-stage assembly scheduling problem with periodic maintenance and makespan objective

This paper addresses the 2-stage assembly scheduling problem to minimise makespan with availability constraints, where the machines are not always available due to periodic maintenance activities. To the best of our knowledge, the periodic maintenance constraint has never been considered for the problem under investigation. In the proposed model, the preemption of operations is not allowed, and therefore, if an operation cannot be finished within the current availability period, then it has to be completely processed in the next one. Firstly, two constructive heuristics powered by a partial local search mechanism are proposed. Secondly, two composite heuristics are proposed incorporating an interchange-based local search mechanism. After that, two computational evaluations are carried out. On the one hand, 84 dispatching rules adapted from the literature, involving two bin-packing policies, are compared among them. On the other hand, the proposed methods are compared with existing heuristics from the literature. The computational results show that the adapted dispatching rules perform well for the two bin-packing policies, and the new proposals outperform the existing heuristics.

A new approach for ground resistance measurements in onshore wind farms based on clamp-on meters and artificial neural network

Monitoring the grounding resistance of wind turbine generators (WTG) over the lifespan of a wind farm is important to ensure the safety of the personnel and equipment on the facilities. Conventional methods used to measure grounding resistance are not feasible in periodic maintenance activities of onshore wind farms due to test setup complexity and associated high costs. The clamp-on meter is an alternative method proposed to measure ground resistance in multi-grounded electrical systems. However, a significant error occurs in clamp-on meters on large grounding systems, such as those found in wind farms. Hence, we propose a solution to minimize methodological errors resulting from applying the clamp-on method in wind farms based on a neural network. Besides presenting lower errors than compared methods for a real Brazilian wind farm complex, it offers even greater practicality and safety to the workers involved in the measurement, as well as less test execution time.

Penilaian Kinerja dan Penyusunan AKNOP Embung di Provinsi Bali

Performance assessment and preparation of AKNOP (operation and maintenance costs) were carried out on 13 reservoirs in the province of Bali, including Embung Badeg, Embung Batudawa, Embung Baturinggit, Embung Besakih, Embung Bukit, Embung Datah I, Embung Datah II, Embung Dukuh, Embung Muntig, Embung Seraya Timur, Embung Seraya, Embung Telung Buana, and Embung Untalan located in Karangasem Regency. The purpose of this study is to evaluate the reservoir performance against the specified assessment aspects and to obtain the planned operating and maintenance costs for the reservoir facilities and infrastructure based on the damage criteria. The evaluation of the operation performance and maintenance of the reservoir refers to the Circular Letter of the General Director of Water Resources, Ministry of Public Works and Housing. Meanwhile, the AKNOP value is calculated from the total of multiplication volume, frequency, and unit price of work for each reservoir operation and maintenance activity. The reservoir performance evaluation is based on four criteria, physical performance, operating and service performance, security and environmental system performance, and institutional performance. From the study, the result shows that 13 reservoirs' performances are in adequate condition. The operation and maintenance can be done by routine and periodic maintenance activities based on operating and maintenance cost plans. This activity is expected to reduce reservoir damage.

Deep Reinforcement Learning for Distributed Flow Shop Scheduling with Flexible Maintenance

A common situation arising in flow shops is that the job processing order must be the same on each machine; this is referred to as a permutation flow shop scheduling problem (PFSSP). Although many algorithms have been designed to solve PFSSPs, machine availability is typically ignored. Healthy machine conditions are essential for the production process, which can ensure productivity and quality; thus, machine deteriorating effects and periodic preventive maintenance (PM) activities are considered in this paper. Moreover, distributed production networks, which can manufacture products quickly, are of increasing interest to factories. To this end, this paper investigates an integrated optimization of the distributed PFSSP with flexible PM. With the introduction of machine maintenance constraints in multi-factory production scheduling, the complexity and computation time of solving the problem increases substantially in large-scale arithmetic cases. In order to solve it, a deep Q network-based solution framework is designed with a diminishing greedy rate in this paper. The proposed solution framework is compared to the DQN with fixed greedy rate, in addition to two well-known metaheuristic algorithms, including the genetic algorithm and the iterated greedy algorithm. Numerical studies show that the application of the proposed approach in the studied production-maintenance joint scheduling problem exhibits strong solution performance and generalization abilities. Moreover, a suitable maintenance interval is also obtained, in addition to some managerial insights.

Hazard and Operability (HAZOP) Study at Separation System in the Offshore Platform

Energy is needed to maintain the cycle of life. It could come from natural oil and gas. Energy sources (oil and gas) are obtained by mining activities on land or sea. At sea, the offshore platform plays very important function in the exploration of natural oil and gas. It consists of several systems; one of these systems is a separation system, which is useful for separating oil, gas and water from the hydrocarbon stream. The separation system consists of several types of equipment such as separators, filters, pumps, pig launchers, and others. Therefore, it is necessary to carry out periodic maintenance activities for equipment on the offshore platform. Maintenance activities could be carried out by knowing the risk level of each equipment on the offshore platform. This research uses the hazard and operability study in order to determine the level of risk in each equipment in the offshore platform as a case study. After the risk level is obtained, the intensive maintenance activities can be carried out on the equipment that has the highest risk level. In this research, it is known that the two types of separators have the highest risk level. Therefore, intensive maintenance activities should be carried out on the two equipments.

Preventive maintenance for heterogeneous industrial vehicles with incomplete usage data

Large fleets of industrial and construction vehicles require periodic maintenance activities. Scheduling these operations is potentially challenging because the optimal timeline depends on the vehicle characteristics and usage. This paper studies a real industrial case study, where a company providing telematics services supports fleet managers in scheduling maintenance operations of about 2000 construction vehicles of various types. The heterogeneity of the fleet and the availability of historical data fosters the use of data-driven solutions based on machine learning techniques. The paper addresses the learning of per-vehicle predictors aimed at forecasting the next-day utilisation level and the remaining time until the next maintenance. We explore the performance of both linear and non-liner models, showing that machine learning models are able to capture the underlying trends describing non-stationary vehicle usage patterns. We also explicitly consider the lack of data for vehicles that have been recently added to the fleet. Results show that the availability of even a limited portion of past utilisation levels enables the identification of vehicles with similar usage trends and the opportunistic reuse of their historical data.

PENGUKURAN KUALITAS LAYANAN JARINGAN KABEL SERAT OPTIK LINK BENCULUK-JIMBARAN

Optical fiber is a telecommunication transmission medium that has a high bandwidthand bit rate so that it can meet the needs of today's information services with high reliability andefficiency. Optical fiber applications are increasingly widespread for underwater networks,terrestrial networks, metropolitan and regional network coverage, also small-scale networksincluded. Optical fiber communication system has internal and external factors that affect thequality of network performance. Those factors possibly able to reduce the performance of theoptical fiber networks. Then, can cause attenuation and other transmission losses. In an effortto prevent sudden and significant deterioration in the quality of a network, it is necessary tocarry out periodic maintenance activities such as measuring the quality of scheduled fiber opticcable network services. These maintenance activities can help determine the decision toincrease network capacity at a later date. The service quality parameters that often measuredare the attenuation of the transmission and the received signal power (power receive). Thisstudy examines the measurement of attenuation of the Benculuk-Jimbaran fiber optic cableusing OTDR EXFO FTB-150. The measurement results of 8 idle cores were analyzed usingOptisystem 7.0 and the calculation of total attenuation and receive sensitivity. Themeasurement results of core 1 to core 12 have total attenuation ranging from 29, 942 dB -30.654 dB at a distance of 169, 646 km - 170, 323 km. The value of power received on cores 1to 12 has a value ranging from -17,339 dBm - 18,204 dBm, where all idle cores fulfill the receivesensitivity requirements set by ITU-T, which is greater than -28 dBm. Based on the performance analysis according to total attenuation and power receive parameters, the Benculuk-Jimbaranfiber optic link network has good performance quality.

Improving the carrying capacity of irrigation canals: Al-Tawfiky diversion

ABSTRACT The morphological changes in the irrigation channel cross-section affect its hydraulic efficiency and carrying capacity with time. Periodic maintenance activities are applied to reach maximum carrying capacity. In many cases, removing the total amount of deposit inside canal cross-section above the design bed level is not the optimum maintenance solution hydraulically and economically. Additional discharge must be applied to canal intake to achieve the design water level. Al-Tawfiky Diversion is one of the main irrigation canals in the Delta region. It extended with a length of 175 km to serve 716,540 feddan. The study encloses the first reach from intake to km 36.400 upstream Jamjara regulator. This research aims to detect the most inefficient reaches along the selected study canal reach based on the actual characteristics of the surveyed canal cross-sections, and identifying the most proper maintenance scenario hydraulically and economically. Three hundred and fifty-one cross-sections along the study reach were surveyed. The different hydraulic parameters of these cross-sections were calculated. The data was analyzed and criterion for detecting the most inefficient sub-reaches was introduced. An empirical equation connecting the applied maintenance activities with channel conveyance was deduced. A mathematical model for the study reach was built and calibrated using (SOBEK-1D) to evaluate the proposed four maintenance scenarios.

Feature-Extraction-Based Iterated Algorithms to Solve the Unrelated Parallel Machine Problem With Periodic Maintenance Activities

This paper considers an unrelated parallel machine problem with job release times and maintenance activities, in which machines have to periodically undergo maintenance since the status of the machines will be deteriorated by job-induced dirt. The problem is inspired by a wet station for cleaning operations in a semiconductor manufacturing process. The objective is to minimize the makespan. Since the considered problem is proven to be NP-hard, obtaining optimal solutions is almost impossible in a reasonable computational time when the problem becomes large. We develop specific feature-extraction procedures to recognize important information in a job sequence and linkage encoding (LE) procedures to generate new job sequences. The two above procedures are embedded into an iterated algorithm, called a feature-extraction-based iterated algorithm (FEBIA), to obtain optimal or better solutions for the considered problem. To examine the performance of the FEBIA, the FEBIA is compared with two population-based algorithms, the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA), using many test data. The results reveal that the proposed FEBIA perform better than the two population-based algorithms, demonstrating the potential of the FEBIA to solve the unrelated parallel machine problem with periodic maintenance and job release times.

Anodic aluminium oxide based humidity sensor for online moisture monitoring of power transformer

The monitoring of moisture inside a transformer is necessary for its continuous reliable operation. It is monitored by the utility as a part of its periodic maintenance activities. Online monitor permits the timely control of moisture buildup inside the transformer. In this work, the development of a capacitive humidity sensor using porous alumina prepared through the anodization of 99.9 % pure aluminium (Al) sheet for online moisture monitoring of transformers has been reported. The capacitive responses of the sensor are studied with varying %RH and ppm moisture. The sensitivity of the sensor is optimized by controlling the pore morphology of the nano structured thin film of Al2O3. The responses of the three sensors developed through the different anodization conditions are compared. The sensor is found to be sensitive for detection of moisture from 180 ppm to 1000 ppm. The designed sensor is used to monitor the moisture generated in highly contaminated transformer oil at different temperatures. A model for detecting the moisture in oil based on the response of the designed sensor is developed. Corrective action based on the response of the humidity sensor will improve the condition of the transformer in the long run.

Heuristic methods for the single-machine scheduling problem with periodical resource constraints

In the last years, researchers have been paying special attention to scheduling problems with scarce resource consumption and periodic maintenance activities with a view to the adoption of more realistic assumptions. This paper aims at presenting heuristics to a single-machine scheduling environment with periodical resource constraints. In this new variant of the single-machine scheduling problem, in each production period, there are resource consumption constraints. To the best of our knowledge, the proposed variant has not been addressed in the revised literature. An integer linear programming model is presented based on a bin packing formulation taking two packing constraints into consideration. The objective function adopted is makespan minimization, and relative deviation is used as a performance criterion. Since the problem under study is NP-hard, heuristic algorithms are proposed to obtain high-quality solutions in acceptable computational times. Eighteen constructive heuristics, two local search heuristics, and a hybrid matheuristics, based on the size reduction and simulated annealing algorithms, have been presented. The extensive computational experience carried out shows that the proposed local search algorithms, as well as the proposed matheuristics, are promising tools to solve large-sized instances.

Improved Approaches to Minimize the Makespan on Single-Machine Scheduling with Periodic Preventive Maintenance Activities

This paper addresses a single-machine scheduling problem with periodic preventive maintenance activities that are predeterministic so that the machine is not available all the time, and jobs have to be processed between two consecutive maintenance periods. We propose a mixed integer programming (MIP) model and two heuristics to minimize the makespan. With more constraints in our model, the model is more efficient than the recent model of Perez-Gonzalez and Framinan , and our model could solve problems with up to fifty jobs. Two heuristic algorithms, namely, H (MW) and H (LB∗), are also proposed, in which two bin-packing policies of the minimum waste and minimum lower bound are used, respectively. Furthermore, we also proposed an improvement procedure. The results showed that the heuristic H (MW) outperformed other heuristics of the paper, indicating that the bin-packing policy of the minimum waste is more effective than well-known ones such as full batch and best fit. Additionally, all the heuristic algorithms addressed in this paper combined with the improvement procedure could achieve a similar and high quality of solutions with a very tiny increase in computational expense.

Use of Six Sigma Methods to Reduce Packaging Defect in Sweetened Condensed Milk Sachets: A Case Study in XYZ Milk Industry, Indonesia

The purpose of this study was to determine the causes of the defects in sweetened condensed milk sachets packaging of XYZ Milk Industry, Indonesia and take corrective actions on the problem. The use of Six Sigma method with DMAIC (Define, Measure, Analyze, Improve, and Control) is expected to increase sigma level. Pareto diagram, Ishikawa diagram, and why-why analysis were utilized as tools to analyze the cause of defects on products. Three major packaging defects were identified, namely horizontal leak, folded, and vertical leaks, which were caused by machine A, machine B, machine C, respectively. Based on the results of the analysis, the main problem was caused by inappropriate condition of the machine part such as dirty sealer, hollow on nozzle, and worn stator pump. By focusing on improvements of the problem, sigma level increased from 4.58 to 4.79. Therefore, periodic maintenance activities are important to do so that all machines may perform in its best conditions and comply with internal and regulatory quality standards.

Investigation of Distributed Moisture and Temperature Measurements in Transformers Using Fiber Optics Sensors

The measurement of water inside a transformer is essential to maintain its reliable operation, as the water content can increase as the transformer ages. This measurement is frequently performed by a utility as a part of its periodic maintenance activities. In this paper, the application of optical fiber sensors as an online method to measure water content of paper inside an operating transformer is presented. There are several advantages of using these sensors, including their size, lightweight, flexibility, multiplexing, no-electromagnetic interference, etc. A unique benefit of fiber optic technology is that sensors can be embedded into the winding structure and operate in areas of high electric field strength. For this work, fiber optic water and temperature sensors are investigated by comparing their measurements to a commercial water activity probe immersed in oil. The study found that the determination of water content by fiber optic sensors compared closely with other techniques, which lead to the conclusion that they can be an effective technology for water measurement in an operational transformer.