| 1. |
- Olsson, Tomas, et al.
(författare)
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A data-driven approach for predicting long-term degradation of a fleet of micro gas turbines
- 2021
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Ingår i: Energy and AI. - : Elsevier BV. - 2666-5468. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Predictive health monitoring of micro gas turbines can significantly increase the availability and reduce the operating and maintenance costs. Methods for predictive health monitoring are typically developed for large-scale gas turbines and have often focused on single systems. In an effort to enable fleet-level health monitoring of micro gas turbines, this work presents a novel data-driven approach for predicting system degradation over time. The approach utilises operational data from real installations and is not dependent on data from a reference system. The problem was solved in two steps by: 1) estimating the degradation from time-dependent variables and 2) forecasting into the future using only running hours. Linear regression technique is employed both for the estimation and forecasting of degradation. The method was evaluated on five different systems and it is shown that the result is consistent (r>0.8) with an existing method that computes corrected values based on data from a reference system, and the forecasting had a similar performance as the estimation model using only running hours as an input.
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| 2. |
- Ramentol, Enislay, et al.
(författare)
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Early Detection of Possible Undergraduate Drop Out Using a New Method Based on Probabilistic Rough Set Theory
- 2019
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Ingår i: Uncertainty Management with Fuzzy and Rough Sets: Recent Advances and Applications. - Cham : Springer International Publishing. ; , s. 211-232
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- For any educational project, it is important and challenging to know, at the moment of enrollment, whether a given student is likely to successfully pass the academic year. This task is not simple at all because many factors contribute to college failure. Being able to infer how likely is an enrolled student to present promotions problems, is undoubtedly an interesting challenge for the areas of data mining and education. In this paper, we propose the use of data mining techniques in order to predict how likely a student is to succeed in the academic year. Normally, there are more students that success than fail, resulting in an imbalanced data representation. To cope with imbalanced data, we introduce a new algorithm based on probabilistic Rough Set Theory (RST). Two ideas are introduced. The first one is the use of two different threshold values for the similarity between objects when dealing with minority or majority examples. The second idea combines the original distribution of the data with the probabilities predicted by the RST method. Our experimental analysis shows that we obtain better results than a range of state-of-the-art algorithms.
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| 3. |
- Ramentol, Enislay, et al.
(författare)
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Machine Learning Models for Industrial Applications
- 2021
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Ingår i: AI and Learning Systems. - : IntechOpen. - 9781789858785 - 9781789858778
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- More and more industries are aspiring to achieve a successful production using the known artificial intelligence. Machine learning (ML) stands as a powerful tool for making very accurate predictions, concept classification, intelligent control, maintenance predictions, and even fault and anomaly detection in real time. The use of machine learning models in industry means an increase in efficiency: energy savings, human resources efficiency, increase in product quality, decrease in environmental pollution, and many other advantages. In this chapter, we will present two industrial applications of machine learning. In all cases we achieve interesting results that in practice can be translated as an increase in production efficiency. The solutions described cover areas such as prediction of production quality in an oil and gas refinery and predictive maintenance for micro gas turbines. The results of the experiments carried out show the viability of the solutions.
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| 4. |
- Tahvili, Sahar, et al.
(författare)
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A novel methodology to classify test cases using natural language processing and imbalanced learning
- 2020
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Ingår i: Engineering applications of artificial intelligence. - : Elsevier Ltd. - 0952-1976 .- 1873-6769. ; 95
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Tidskriftsartikel (refereegranskat)abstract
- Detecting the dependency between integration test cases plays a vital role in the area of software test optimization. Classifying test cases into two main classes – dependent and independent – can be employed for several test optimization purposes such as parallel test execution, test automation, test case selection and prioritization, and test suite reduction. This task can be seen as an imbalanced classification problem due to the test cases’ distribution. Often the number of dependent and independent test cases is uneven, which is related to the testing level, testing environment and complexity of the system under test. In this study, we propose a novel methodology that consists of two main steps. Firstly, by using natural language processing we analyze the test cases’ specifications and turn them into a numeric vector. Secondly, by using the obtained data vectors, we classify each test case into a dependent or an independent class. We carry out a supervised learning approach using different methods for handling imbalanced datasets. The feasibility and possible generalization of the proposed methodology is evaluated in two industrial projects at Bombardier Transportation, Sweden, which indicates promising results. © 2020 The Authors
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| 5. |
- Zhang, Chongsheng, et al.
(författare)
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Multi-Imbalance : An open-source software for multi-class imbalance learning
- 2019
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Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 174, s. 137-143
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Tidskriftsartikel (refereegranskat)abstract
- Imbalance classification is one of the most challenging research problems in machine learning. Techniques for two-class imbalance classification are relatively mature nowadays, yet multi-class imbalance learning is still an open problem. Moreover, the community lacks a suitable software tool that can integrate the major works in the field. In this paper, we present Multi-Imbalance, an open source software package for multi-class imbalanced data classification. It provides users with seven different categories of multi-class imbalance learning algorithms, including the latest advances in the field. The source codes and documentations for Multi-Imbalance are publicly available at https://github.com/chongshengzhang/Multi_Imbalance.
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