Smart Maintenance Decision Support Systems (SMDSS)

Bumblauskas, Daniel

doi:10.31274/etd-180810-2419

Cited by 3 publications

(8 citation statements)

References 59 publications

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“…where the expressions of the terms are the same as ( 8)- (10). In constraints ( 13) and ( 14), the computation of the total output power of the zones omits the generation powers of the DGs and batteries in the zones, such that (14) we have: where W m is the total number of consuming components in zone p regardless of the powers of the DGs and the batteries, and P p,k,s (t) is the power generated or consumed by the kth consuming component in zone p in time slot t of scenario s. In addition in (15), the power limitations on the components in the zones can be described as: (20), they are not included in the comparison model. The modifications of the programming problem for comparison are shown in Table IV.…”

Section: B Comparison Of Methodsmentioning

confidence: 99%

“…For example, in [13]- [16], the preventive maintenance actions are performed to avoid failures of generation units. Besides, in [17]- [20] a new preventive maintenance concept "smart maintenance" has been proposed. Smart maintenance utilizes smart inspections based on big data analysis technologies, smart devices to collect the data, smart services, asset management, and other techniques to make preventive maintenance decisions.…”

Section: Index Terms-mentioning

confidence: 99%

“…Smart maintenance utilizes smart inspections based on big data analysis technologies, smart devices to collect the data, smart services, asset management, and other techniques to make preventive maintenance decisions. In particular, in [20], a review of the possible applications of big data on failure diagnosis, the internet of things on data collecting, and other technologies on smart maintenance can be found.…”

Section: Index Terms-mentioning

confidence: 99%

See 2 more Smart Citations

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

Núñez

Schutter

2021

IEEE Trans. Power Syst.

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Section: B Comparison Of Methodsmentioning

confidence: 99%

Section: Index Terms-mentioning

confidence: 99%

Section: Index Terms-mentioning

confidence: 99%

See 1 more Smart Citation

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

Núñez

Schutter

2021

IEEE Trans. Power Syst.

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“…Note that each individual contains an extra position for the l parameter. Subsequently, each individual from the initial population can be constructed according to equation (8) The evaluation function is as follows…”

Section: Population Initialization and Evaluation Functionmentioning

confidence: 99%

“…Scout bees' phase The food source can be abandoned under the condition of not improving quality after a predetermined number of cycles. Namely, the employed bee is turned into a scout bee and generated a new solution randomly by equation (8). The pseudo-code of measure identification based on ABC algorithm is shown in Figure 3.…”

Section: Population Initialization and Evaluation Functionmentioning

confidence: 99%

Evaluation of equipment maintenance quality: A hybrid multi-criteria decision-making approach

Zhang

Shan

2019

Advances in Mechanical Engineering

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Maintenance plays a crucial role in the entire life cycle of equipment. With the acceleration of industrialization, the evaluation of equipment maintenance quality has undoubtedly become more challenging due to the complex mechanical structure, various maintenance modes, and so on. In order to make decisions scientifically, a hybrid multi-criteria decision-making approach integrating triangle fuzzy number, l-fuzzy measure, TOPSIS, and Choquet fuzzy integral is proposed in this article. First, the interaction among criteria can be handled reasonably by fuzzy integral based on l-fuzzymeasure. Second, fuzzy numbers which are given by experts are applied to deal with fuzzy linguistic value. In addition, artificial bee colony algorithm is first introduced to identify l-fuzzy-measure. The comparison results of three optimization algorithms which include artificial bee colony algorithm, genetic algorithm, and particle swarm optimization prove artificial bee colony algorithm is more effective than genetic algorithm and particle swarm optimization. A case study which contains six maintenance alternatives is practiced to prove the effectiveness of the proposed hybrid multi-criteria decision-making approach. Finally, the comparison is made between the proposed method and two classical multi-criteria decision-making approaches which refer to TOPSIS and gray correlation, and the results demonstrate the proposed method is suitable to solve maintenance quality evaluation problem.

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A Markov decision process model case for optimal maintenance of serially dependent power system components

Bumblauskas

2015

Journal of Quality in Maintenance Engineering

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Purpose – Using a case study for electrical power equipment, the purpose of this paper is to investigate the importance of dependence between series-connected system components in maintenance decisions. Design/methodology/approach – A continuous-time Markov decision model is formulated to find a minimum cost maintenance policy for a circuit breaker as an independent component while considering a downstream transformer as a dependent component. Maintenance of the dependent component is included implicitly in terms of the costs associated with certain state-action pairs. For policy and cost comparisons, a separate model is also formulated that considers only the circuit breaker as the independent component. After uniformizing the continuous-time models to discrete time, standard methods are used to solve for the average-cost-optimal policies of each model. Findings – The optimal maintenance policy and its cost differ significantly depending on whether or not the dependent component is considered. Research limitations/implications – Data used are from manufacturer databases; additional model validation could be conducted if applied to an electric utility asset fleet within their generation, transmission, and/or distribution system. This model and methodology are already being applied in other contexts such as industrial machinery and equipment, jet engines, amusement park rides, etc. Practical implications – The outcome of this model can be utilized by asset and operations managers to make maintenance decisions based on prediction rather than more traditional time- or condition-based maintenance methodologies. This model is being developed for use as a module in a larger maintenance information system, specifically linking condition monitor data from the field to a predictive maintenance model. Similar methods are being applied to other applications outside the electrical equipment case detailed herein. Originality/value – This model provides a structured approach for managers to decide how to best allocate their resources across a network of inter-connected equipment. Work in this area has not fully considered the importance of dependency on systems maintenance, particularly in applications with highly variable repair and replacement costs.

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Smart Maintenance Decision Support Systems (SMDSS)

Cited by 3 publications

References 59 publications

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

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

Evaluation of equipment maintenance quality: A hybrid multi-criteria decision-making approach

A Markov decision process model case for optimal maintenance of serially dependent power system components

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