Equipment Maintenance Scheduling Research Articles

Cloud Server-Assisted Remote Monitoring and Core Device Fault Identification for Dynamically Tuned Passive Power Filters

Reliability and safety are crucial for the operation of a dynamically tuned passive power filter (DTPPF). Safe performance of DTTPFs implies complete normal filtering without failure within a specified period. To prevent potential disaster or economic loss, it is desirable to achieve early warning of any core device faults in a DTPPF based on its running state and to optimize its harmonic mitigation performance. In this paper, we explore effective methods for identifying core device faults in DTPPFs. First, we summarize the characteristic parameters of faults, running state parameters, parameters required for fault monitoring, and fault type parameters. Then, a cloud server-assisted remote monitoring and fault identification system for DTPPF is proposed, which consists of monitoring system’s architecture and cloud servers’ software architecture as well as software design of the back-end service layer and functional design of the front-end application layer. Our experiments demonstrate that the proposed system can monitor the real-time operational status of the DTPPF, enabling remote diagnosis and identification of core device faults. Moreover, it is user-friendly, as it is capable of optimizing equipment maintenance schedules and utilizing manufacturers’ service capacities. Therefore, this research provides a theoretical foundation for harmonic mitigation in low-voltage distribution networks and is valuable for practical engineering applications in industrial power grids.

Service-oriented multi-skilled technician routing and scheduling problem for medical equipment maintenance with sudden breakdown

Motivated by the service-oriented intelligent maintenance activity under the development of the digital economy, we study the operation and maintenance strategies of the Maintenance Scheduling and Technician Routing Problem (MSTRP) for medical equipment implemented by multi-skilled technicians. We consider the maintenance services provided for both preventive maintenance (PM, e.g., periodic maintenance based on the contract schedule) and corrective maintenance (CM, e.g., repair action in the event of a sudden breakdown). As these activities are performed on equipment with various types of requirements at different geographically distributed systems, technicians with diverse qualifications need to build teams and visit the matching maintenance requests. Meanwhile, we consider the maintenance actions concerning the dynamic change in equipment failure rate and establish a flexible maintenance mechanism by balancing the total cost. This paper develops an efficient hybrid heuristic based on a genetic algorithm, variable neighborhood search, and other optimization strategies to solve this multi-period equipment maintenance scheduling and multi-skilled technician routing and scheduling problem. Based on the settings of the studied MSTRP, this paper designs different sizes of testing instances and multi-period explanatory cases before conducting extensive comparative experiments and sensitivity analysis to demonstrate the effectiveness of our proposed dynamic maintenance strategy and optimization method.

The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach

Due to the difficulties inherent in diagnostics and prognostics, maintaining machine health remains a substantial issue in industrial production. Current approaches rely substantially on human engagement, making them costly and unsustainable, especially in high-volume industrial complexes like fulfillment centers. The length of time that fulfillment center equipment failures last is particularly important because it affects operational costs dramatically. A machine learning approach for identifying long and short equipment failures is presented using historical equipment failure and fault data. Under a variety of hyperparameter configurations, we test and compare the outcomes of eight different machine learning classification algorithms, seven individual classifiers, and a stacked ensemble. The gradient boosting classifier (GBC) produces state-of-the-art results in this setting, with precision of 0.76, recall of 0.82, and false positive rate (FPR) of 0.002. This model has since been applied successfully to automate the detection of long- and short-term defects, which has improved equipment maintenance schedules and personnel allocation towards fulfillment operations. Since its launch, this system has contributed to saving over $500 million in fulfillment expenses. It has also resulted in a better understanding of the flaws that cause long-term failures, which is now being used to build more sophisticated failure prediction and risk-mitigation systems for fulfillment equipment.

Sustainable service oriented equipment maintenance management of steel enterprises using a two-stage optimization approach

Equipment maintenance management is essential for steel enterprises to provide sustainable production services. However, few studies have considered the equipment maintenance scheduling of steel enterprises due to the characteristics of large-scale and complex constraints. This study analyses the characteristics of equipment maintenance scheduling in the steel industry and incorporates multiple complex constraints into a mathematical model. To find an optimal or near optimal solution, a two-stage optimization approach is developed that introduces the rule-based prescheduling method to quickly obtain a reasonable maintenance plan in the first stage and employs a modified genetic algorithm to further optimize the prescheduling maintenance plan in the second stage. The case of Baowu Steel is used for verification and the experimental results demonstrate the superiority and effectiveness of the proposed optimization approach, which improves the comprehensive performance by 40.3% on the basis of prescheduling.

Application of Analytical Hierarchical Process in Equipment Maintenance Scheduling and Decision-Making Process in Oil/Gas

Operational efficiency and reliability of equipment directly impact production and revenue streams. Maintenance, an essential aspect of Oil/Gas operations, is a set of activities that aim at preserving the condition of equipment to reduce the probability of failure and increase operating life cycle of equipment. The culture of maintenance evolves considerably from run to failure and corrective to preventive maintenance practices. These set of activities are expensive and significantly affect the cost of sales and operating expenditure especially in Oil and Gas sector. With the need to optimize operating cost, use of data in prioritizing equipment maintenance becomes necessary. This paper provides a novel approach for prioritizing critical equipment maintenance activities using Analytical Hierarchical Process (AH). A family of Weibull distribution function are use to define five parameters (criteria) namely the Weibull Continuous Distribution Function (CDF), Weibull Probability Density Function, Reliability function, failure rate and equipment availability. To validate the use of AHP method, data from Nine (9) critical equipment from a pump station in Port-Harcourt, Nigeria was used to prioritize maintenance activities. The slope shape parameter values of \(\gamma\) \(\epsilon\){0.5,1,2} are considered, which affects the shape of the distribution functions. The results show that multi-criterion AHP-approach supports subjective decision making by providing quantitative weighted ranking of equipment based on priority. The result indicates that equipment with best values has lowest priority ranking.

ESPDS

In this paper, we introduce a Microsoft Excel Workbook containing the software Equipment Selection Problem DS (ESPDS) that recognizes the special structure of the equipment selection problem. The ESPDS approach is based on the context of the Brazilian forestry sector using detailed equipment maintenance schedules. No special restrictions are needed on cost inputs over time or technologies. The output is an equipment schedule that can be used to project equipment investment needs, operational costs, and tree harvesting costs. ESPDS can be applied to support companies and contractors in order to choose the best option for their operations, as well as to achieve better equipment purchase agreements. We will show how ESPDS will also be useful in providing longer term estimates of production costs. The sensitivity analysis shows how different inputs and maintenance polices can affect the best alternative. A numerical example is included considering the entrance of a specific technology that increases the equipment productivity in order to examine whether it can change the solution. ESPDS is intuitive, flexible, and easy to calculate. Although designed for the forestry industry, the approach is readily transferable to other sectors. ESPDS may be found on the web at the following URL: <a target="_blank" rel="nofollow" href="https://www.researchgate.net/publication/350811380_ESPDS_workbook">https://www.researchgate.net/publication/350811380_ESPDS_workbook</a>.

The initialization of initial generating equipment maintenance schedules while a heuristic method is used

Currently, heuristic methods based on iterative changing of feasible solutions set provide a perspective tool for generation equipment maintenance scheduling in power systems. Wherein effectiveness of a heuristic method depends significantly on the initial set of possible schedules or in other words quality of the method initialization. In this case, a widely used methodology of building the initial array of solutions on the basis of pseudorandom uniform generation of control variables seems to be only palliative way to access the problem.
 This paper proposes alternative initialization procedure drawing on the example of generating units maintenance planning with heuristic differential evolution method. The principle of this method is to get initial set of solutions utilizing normal probability distribution to generate pseudorandom deviations from the suboptimal maintenance schedule which is to be preliminarily formed using directed search method. Following this approach allows to improve probabilistic characteristics of resultant maintenance schedule in particular to decrease median value of an objective function and its coefficient of variation, and to maximize probability to get the combination of units outage moments completely suiting operational constraints.

An evaluation of horizontal equity in surgical care for gallstone disease in a Caribbean country

Background: The aim of this study was to identify social injustice by evaluating horizontal equity (allocation of similar resources to patients with the same clinical needs) in surgical care for gallstone disease in a Caribbean Country. Methods: We compared access to laparoscopic cholecystectomy (LC) for gallstone disease between a fee-for-service (private) and a government-funded (public) health-care system between 1st January and 31st August, 2018. We defined a prolonged waiting time as an interval of >90 days between the index diagnosis and LC. The data were examined using SPSS v23. Results: Ninety-nine patients underwent LC in public (60) or private (39) hospitals. There was a significantly longer mean waiting time in public hospitals (336.4 ± 647.9 days vs. 20.2 ± 25.5 days; P 0.002) and significantly more hospital readmissions while awaiting LC (72% vs. 0%). In the public hospital system, fewer patients had ambulatory operations (8.4% vs. 51.3%; P < 0.00001) and the mean duration of operation was longer (2.4 ± 0.88 vs. 0.92 ± 0.34 h; P < 0.0001). There were no differences in morbidity or mortality between the health-care systems. Thirty-eight patients had prolonged waiting times due to: long pre-existent waiting lists (13), consumables out of stock (10), equipment malfunction (6), patient request (3), pre-operative Endoscopic retrograde cholangiopancreatography (2), unspecified (2) and anaesthetic optimisation (1). One patient in the private hospital had a delay >90 days while awaiting insurance pre-authorisation. Conclusion: Horizontal inequity exists between the health-care systems. There is room for improvement through increased accountability for administrators, better resource allocation, proper equipment maintenance schedules and efforts to reduce waiting lists.

Optimal Inspection and Preventive Maintenance Scheduling of Mining Equipment

A typical mining company has three important assets: the human labor-force, the orebody, and the equipment. Trucks, excavators, drilling machines, crushers, grinders, classifiers, and concentrators represent the equipment. Mining operations that want to take advantage of economies of scale have huge equipment fleet, and the worth of the equipment could be more than a hundred million dollars. The reliability and availability of this equipment play critical roles in increasing the efficiency and productivity of a mining operation. This paper proposes an effective maintenance management approach to be used in the mining industry such that equipment availability and reliability are improved while potential failures are prevented. Using failure data of a mining truck fleet in an open-pit Canadian mining operation, a case study is conducted in two steps. The first step focuses on determining optimal inspection intervals based on the desired reliability level in such a way as to detect potential catastrophic failures that represent considerable maintenance and downtime costs. The second step develops a preventive maintenance scheduling plan that differentiates between physical and virtual age considering the degradation of systems and their rejuvenation after each repair. The research outcomes show that the proposed approach has the potential to increase the benefit obtained from the mining equipment and can be used in mining operations.

The Product Quality Improvement: An Example from a Rice Milling in Indonesia

This study aimed to determine the quality attributes of rice products which were important for the consumers and the level of consumer satisfaction with these attributes. Recommendations were then given to improve the quality of rice products based on consumer voices. The research was conducted at the Sri Margo Joyo (SMJ) rice milling that produces packaged rice with the King Banana brand. QFD was used in this study for product design, process planning, process design, and process control. Variables in this research were determined based on quality dimension. Respondents in this study were 60 King Banana rice consumers and 2 expert respondents. The results showed that King Banana rice products need to make improvements to the attributes of colour, texture, product damage level when received by the consumers, and variations in packaging weight. The priority of technical response to improve the quality attributes of rice products were the improvement of storage processes, proper maintenance of machinery and equipment, and scheduling of spraying and fumigation every 1 month. Process control that needs to be applied to the SMJ rice mill were monitoring the mixing of grade A and grade B grain, adding the process of ignition, monitoring temperature, humidity, and insecticide use in the rice storage room, monitoring the maintenance scheduling of rice processing machinery and equipment, and monitoring the vacuum packaging.

Optimization Model of Key Equipment Maintenance Scheduling for an AC/DC Hybrid Transmission Network Based on Mixed Integer Linear Programming

The unbalanced distribution of resource and consuming centers in China has prompted the AC/DC hybrid transmission technology. The maintenance scheduling of an AC/DC hybrid transmission network is the key technology to ensure its safety and reliability. In this study, the mutual influence mechanism of an AC/DC system in a maintenance period was analyzed in detail. The overhead transmission line and transformer are key equipment within an AC/DC hybrid transmission network, and an optimization model of the key equipment maintenance scheduling was established. The objective of the model was to improve the system reliability during the maintenance scheduling. By considering the constraints of maintenance cost, maintenance resources, and maintenance workload, the maintenance scheduling of overhead transmission lines and transformer branches was obtained. The over-limit situation of power flow and the weakness of the system during the maintenance period was evaluated. The “double-layer substitution method” was adopted to convert the nonlinear constraints into its bilinear formulation such that it could then be solved. The random number sampling method was used to quantify the system reliability, and the commercial optimization software was used to solve the optimized scheduling. Based on the improved IEEE RTS-79 system and the Hubei Province electrical system, the simulation results showed the effectiveness of the proposed method.

Equipment replacement policy for forest machines in Brazil

ABSTRACT In this work, we present a model to assist in the equipment replacement decision process in the context of the Brazilian forestry sector using detailed equipment maintenance schedules. The approaches are based on the economic life (EL) method for three scenarios. In the first scenario, we considered buying a new machine during the first period. For the second scenario, we considered keeping the current machine for 24 months, and then buying a new one while for the third scenario, we considered selling the current machine, leasing a new one for 24 months and then buying a new machine. Our example draws from five tree harvesting machines that cut, delimb, and process the tree stem into logs in forest plantations of a large integrated Brazilian forestry company. The algorithms were coded in Visual Basic for Applications (VBA). The results show the new machine discounted cost (CNM) and rebuild machine discounted cost (CRM) scenarios presented higher values than the leased machine discounted cost (CRE) scenario, and those solutions should be rejected. The economic life policy proved optimum with 52 months for a new machine, while the best decision to make is sell the current machine, lease a new one for 24 months and then buy a new one. The solution and the algorithms used are described.

Scheduling of periodic services to customers in dispersed locations from heterogeneous multi-agent companies considering uncertainty: A real case study

The scheduling problem of periodic services from service providers to customers located in different places and need different services. The service centers are also located in different positions, each of which has limited number of teams with the capability of performing one or some services. The goal is to simultaneously minimize ‘total service costs’ and ‘total earliness/tardiness’ in providing services to customers. Providing an optimal maintenance schedule is a big challenge in those companies with dispersed supply centers. In this paper, a novel bi-objective mixed integer linear programming model along with augmented epsilon constraint method is presented to exactly solve this problem. Then, a bi-objective meta-heuristic technique based on genetic algorithm is proposed and its performance in solving large-scaled problems is assessed. The uncertain parameters are faced through robust possibilistic programming approach to diminish the risk of decision making. Finally, the performance of the proposed model and solution approaches are evaluated through a real case study in maintenance scheduling of CNG stations equipment in Iran.

Maintenance Scheduling of Power Equipment Considering Opportunistic Maintenance Strategy

This paper investigates the impact of opportunistic maintenance strategy on power equipment maintenance scheduling under economic dependence. The model focuses on power equipment with two types of failures: random failure and deterioration failure. Unlike previous research which didn’t consider opportunistic maintenance between different failures modes, opportunistic maintenance strategy is proposed in this paper. In the opportunistic maintenance strategy, whenever random failure occurs, decision has to be made on whether deterioration failure is preventively maintained based on the degree of equipment deterioration and economic dependence. Firstly, Markov process is used to describe equipment state-space diagram. And then, in order to determine the optimal inspection rate, preventive maintenance threshold and opportunistic maintenance threshold of power equipment, the analytical expressions of inspection cost, maintenance cost, repair cost and outage cost are quantified. Finally, the total cost is minimized to get the optimal maintenance strategy. Case studies demonstrate the feasibility of the proposed model.

Short-term Power System Maintenance Scheduling with Imperfect Repair

Most of the existing research in short-term power system maintenance scheduling assumes that repair is minimal when equipment fails. However, this may not always be the truth because sometimes repair after failure is imperfect. This paper investigates the impact of imperfect repair on power system maintenance scheduling. Unlike previous research which didn’t consider imperfect repair, a condition-based maintenance optimization model is proposed. The model focuses on equipment deterioration failure. Firstly, a new equipment state-space diagram under imperfect repair is given. And then, the availability of power equipment considering maintenance schedule and imperfect repair is derived using the Markov process theory. Based on that, in order to determine the optimal equipment maintenance schedules, the expression of system outage cost is given. Finally, system outage cost is minimized to optimize equipment maintenance schedules. A case study is used to illustrate the effectiveness and significance of the proposed model.

Pembuatan Program Aplikasi Pemeliharaan Mesin Terjadual Di Laboratorium Mekanik Polman Negeri Bangka Belitung

Every company wants all its equipment or machinery operating in optimum condition, so that the necessary maintenance activities to fulfill that desire. Management of maintenance activities to be hard to do when the equipment or machines owned by increasingly numerous and complex, and activities increase the number of machines or equipment routinely performed. Therefore needed a system to manage maintenance activities quickly, especially in administration of maintenance of machine or equipment are handled. This program has the ability to manage personal data engines, machinery or equipment maintenance scheduling, managing work instruction sheet, a history of the machine, the data parts, data managing maintenance activities, preventive maintenance and expense reports. System maintenance is made by using a computer-based information system maintenance. In making the program used Microsoft Visual Basic 6.0 and used for the purpose of Microsoft Access database.

Analisa Perawatan Berbasis Keandalan Pada Sistem Bahan Bakar Mesin Utama KMP. Bontoharu

This study determines the prone components and the mean time to failure of KMP. Bontoharu. The method used in this research is the qualitative analysis, which comprises the failure mode, effect, analysis (FMECA), fault tree analysis (FTA), and quantitative analysis using Weibull distribution to determine the mean time to failure (MTTF) value. The risk method based inspection and maintenance is used to determine the value of equipment risk and maintenance scheduling model, and the inspection method used is visual check. The results revealed that the value of MTTF filter was 194 hours, separator was 502 hours, and pipe was 2508 hours. Filters and separators have the highest level of risk, while the mainframe, daily trays, transfer pumps, feed pumps, injection pumps, and injector service tanks have medium risk levels. Keywords: fuel system, Reliability, FMECA, FTA, Maintenance

Midterm system level maintenance scheduling of transmission equipment using inspection based model

Condition Based Maintenance (CBM) using inspection data is one of the available maintenance approaches for power system equipment. In Inspection Based Maintenance (IBM) as a specific type of CBM, decisions about the time and type of the maintenance tasks are made after determining the deterioration condition of the equipment. In this paper, an IBM model is proposed for yearlong transmission equipment maintenance scheduling. In order to quantify the effect of the inspection and maintenance tasks, the probability distribution of the deterioration condition is calculated using the transition probability matrix of the Markov process. The problem of system level maintenance scheduling is formulated with binary variables as weekly inspection time during the year. Inspection time of equipment is optimized by minimizing the total cost, including inspection, maintenance, redispatch and failure costs while network security constraints are considered. The proposed model is implemented for the maintenance of transformers and transmission lines in IEEE-RTS and IEEE 57-bus test systems.

Generation and transmission equipment maintenance scheduling by transmission switching and phase shifting transformer

AbstractThis paper models and investigates the incorporation of economic transmission switching (TS) into generation and transmission equipment maintenance scheduling (GTEMS) with the presence of phase shifting transformers. Adding TS and phase shifting transformer to the traditionally static transmission system makes it more flexible. The N‐1 secure GTEMS with TS problem is formulated as a mixed‐integer linear programming model. This problem is hard to solve by off‐the‐shelf commercial optimization solvers because on the one hand maintenance scheduling variables couple several time intervals together and on the other hand TS variables link normal and contingency states. A solution approach is proposed in this paper to address the problem's high computational burden, which decomposes the problem into a GTEMS subproblem and an optimal TS subproblem and solves them iteratively. The proposed model and decomposition approach are implemented on IEEE 30‐bus test system. The results demonstrate that considering TS in GTEMS alters the maintenance schedule and brings cost saving.

The Application of Improved Bacteria Foraging Algorithm to the Optimization of Aviation Equipment Maintenance Scheduling

The Application of Improved Bacteria Foraging Algorithm to the Optimization of Aviation Equipment Maintenance Scheduling