Timely condition-based maintenance planning for multi-component systems

Verbert, K.; Schutter, Bart De; Babuška, Robert

doi:10.1016/j.ress.2016.10.032

Cited by 71 publications

(35 citation statements)

References 35 publications

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“…Pendekatan yang diusulkan dibagi atas pengoptimal tingkat tinggi, tingkat menengah, dan tingkat rendah, yang memungkinkan pemisahan proses perhitungan pada tingkat yang berbeda. Optimalisasi berbasis hierarki untuk pemeliharaan infrastruktur kereta api juga telah diusulkan [10]. Namun, skema hierarki ini sangat tergantung pada struktur utama dari masalah masing-masing dan dengan demikian menjadi kurang fleksibel untuk menyelesaikan masalah berskala besar.…”

Section: Pendahuluanunclassified

Penjadwalan Pemeliharaan Trek Kereta dengan Metode Distributed Model Predictive Control

Faris

Ircham

2019

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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This work addresses the development of Distributed Model Predictive Control (DMPC) approaches for the planning of maintenance operations of large-scale railway infrastructure formulated as a Mixed-Integer Linear Programming (MILP) problem. The proposed optimization problem is solved using two different decomposition schemes: Alternating Direction Method of Multipliers (ADMM) and Distributed Robust Safe But Knowledgeable (DRSBK). The original distributed algorithms are modified to handle the non-convex nature of the optimization problem, hence improving the solution quality. The results of large-scale test instances show that DRSBK can outperform the conventional centralized approach and ADMM, by providing the closest-to-optimum solution while requiring the least computation time. Intisari-Makalah ini mengembangkan metode Distributed Model Predictive Control (DMPC) untuk perencanaan operasi pemeliharaan infrastruktur kereta api skala besar yang diformulasikan sebagai masalah Mixed-Integer Linear Programming (MILP). Masalah optimasi yang diusulkan kemudian diselesaikan dengan menggunakan dua skema optimasi DMPC yang berbeda: Alternating Direction Method of Multipliers (ADMM) dan Distributed Robust Safe But Knowledgeable (DRSBK). Selain itu, algoritme standarnya dimodifikasi untuk menangani sifat non-convex dari masalah optimasi, sehingga dapat meningkatkan kualitas solusi. Hasil pengujian dalam masalah skala besar menunjukkan bahwa DRSBK dapat mengungguli metode centralized yang konvensional dan ADMM, dengan menghasilkan solusi mendekati ke global optimum tetapi membutuhkan waktu komputasi minimum. Kata Kunci-Penjadwalan, rekayasa perkeretaapian, optimisasi, MIP, kendali prediktif.

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Section: Pendahuluanunclassified

Penjadwalan Pemeliharaan Trek Kereta dengan Metode Distributed Model Predictive Control

Faris

Ircham

2019

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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“…Predictive maintenance utilises an estimation of the current and future condition of a component in order to provide optimised maintenance scheduling that prevents failures without however resorting to over-maintenance (Verbert et al 2017). This optimised maintenance scheduling offers extended machine lifespan, coupled with reduced maintenance costs and downtime.…”

Section: Maintenance Strategiesmentioning

confidence: 99%

Investigating an SVM-driven, one-class approach to estimating ship systems condition

Lazakis

Gkerekos

Theotokatos

2018

Ships and Offshore Structures

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Maintenance is a major point that can affect vessel operation sustainability and profitability. Recent literature shows that condition monitoring of ship systems has a great potential at the cost of significant data requirements. In this respect, this paper presents a novel methodology for intelligent, system-level modelling for the monitoring of main engine performance utilising data acquired through noon-reports, with a minimal amount of data assumptions. The proposed methodology is utilised to train a one-class Support Vector Machine (SVM) that represents a diesel generator's normal behaviour. Acquired data representing the engine's new conditions are input to the model and model output reflects a gauge of their normality, when compared to the original dataset, for analogous operating conditions. This aids the dynamic detection of ship machinery incipient faults, contributing to the minimisation of ship downtime and increase of its operability. A case study presenting applications of this modelling technique on actual ship machinery raw data is included, complemented by a sensitivity analysis. The case study demonstrates the applicability of the developed methodology in the identification of deviant, abnormal ship machinery conditions.

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“…A significant innovation in this field is represented by the combination of Condition Based Monitoring (CBM) with the Internet of Things (IoT) concepts and Machine Learning (ML) principles. This combination has led to what is nowadays called "Industry 4.0" where already known and developed tools, such as CBM, are empowered by the analysis and processing of data collected using IoT and cloud-based solutions and processed using ML applications [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Performance Prediction using Neural Network and Confidence Intervals: a Gas Turbine application

Cisotto

Herzallah

2018

2018 IEEE International Conference on Big Data (Big Data)

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The combination of Condition Based monitoring techniques with the predictive capabilities of neural networks represents a topic of central importance when it comes to maximizing production profits and consequently reducing costs and downtime. The ability to plan the best strategy based on the prediction of potential damaging events can represent a significant contribution, especially for the maintenance function. In fact, optimization of the management of the equipment is a fundamental step to guarantee the competitiveness of companies in the current market. In this paper, a tool based on the implementation of Radial Basis Function Neural Networks was developed to support the maintenance function in the decision-making process. In addition to providing an indication of the status of the equipment, the current approach provides an additional level of information in terms of predicting the confidence interval around the prediction of the neural network. The confidence interval combined with the prediction of the future state of the equipment can be of fundamental importance in order to avoid strategic decisions based on a low level knowledge of the system status or prediction performance of the applied algorithm. The developed tool is tested on the prediction of a naval propulsion system gas turbine performance decay, where the statuses of both the turbine and the compressor of the system are predicted as well as predicting their confidence intervals.

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Timely condition-based maintenance planning for multi-component systems

Cited by 71 publications

References 35 publications

Penjadwalan Pemeliharaan Trek Kereta dengan Metode Distributed Model Predictive Control

Penjadwalan Pemeliharaan Trek Kereta dengan Metode Distributed Model Predictive Control

Investigating an SVM-driven, one-class approach to estimating ship systems condition

Performance Prediction using Neural Network and Confidence Intervals: a Gas Turbine application

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