Real-world application of machine-learning-based fault detection trained with experimental data

Bode, Gerrit; Thul, Simon; Baranski, Marc; Müller, Dirk

doi:10.1016/j.energy.2020.117323

Cited by 67 publications

(27 citation statements)

References 32 publications

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“…Even though it is the same type, the specific behavior might differ significantly. This problem was demonstrated in [65], in which several different FD models for a reversible heat pump were trained using an experimental dataset [66] and then applied in FD using a real building dataset [67]. The results can be seen in Figure 4.…”

Section: Data-based Methodsmentioning

confidence: 99%

“…The transferred system with improved fault labels uses the original models, but has better fault labels. This figure is adapted from[65], Figures7-9, which was published in the Energy journal, 198, G. Bode, S. Thul, M. Baranski, D. Müller, "Real-world application of machine-learning-based fault detection trained with experimental data", 5-6, Copyright Elsevier 2020.…”

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confidence: 99%

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Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

et al. 2022

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This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository.

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Section: Data-based Methodsmentioning

confidence: 99%

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confidence: 99%

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

et al. 2022

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“…They use simulated data labelled with different fault scenarios to evaluate the performance of their model. Bode et al [30] also leveraged supervised ML and trained different classifiers with real-world and synthetically generated data containing ground-truth faults. In their analysis, they applied and compared Logistic Regression (LR), k-Nearest-Neighbour (kNN), Classification and Regression Tree (CART), Random Forest (RF), Naive Bayes Classifier (NB), Support Vector Machine (SVM) and Multi-layer Perceptron.…”

Section: Review Of Recent Research Articlesmentioning

confidence: 99%

Opportunities for Machine Learning in District Heating

et al. 2021

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The district heating (DH) industry is facing an important transformation towards more efficient networks that utilise significantly lower water temperatures to distribute the heat. This change requires taking advantage of new technologies, and Machine Learning (ML) is a popular direction. In the last decade, we have witnessed an extreme growth in the number of published research papers that focus on applying ML techniques to the DH domain. However, based on our experience in the field, and an extensive review of the state-of-the-art, we perceive a mismatch between the most popular research directions, such as forecasting, and the challenges faced by the DH industry. In this work, we present our findings, explain and demonstrate the key gaps between the two communities and suggest a road-map ahead towards increasing the impact of ML research in the DH industry.

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“…Simulation-based optimization is a common approach in process engineering, and significant work has been performed in the field of surrogate model development. Particularly in the domains of molecular chemistry, supply chain management, residential energy systems, and chemical engineering, a large number of surrogate modeling applications are reported (Mirkouei and Haapala, 2014;Hansen et al, 2015;Bode et al, 2020). Furthermore, a variety of literature focuses on the general methodology for how to optimize surrogate models (Huang et al, 2006;Davis and Ierapetritou, 2009).…”

Section: State Of the Art In Surrogate Model Design For Process Engineeringmentioning

confidence: 99%

Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

2021

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Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm for replacing non-linear process simulation models integrated in multi-level optimization of a process and energy system superstructure with surrogate models, applying an active learning strategy to continuously enrich the database on which the surrogate models are trained and evaluated. Surrogate models are generated and trained on an initial data set, each featuring the ability to quantify the uncertainty with which a prediction is made. Until a defined prediction quality is met, new data points are continuously labeled and added to the training set. They are selected from a pool of unlabeled data points based on the predicted uncertainty, ensuring a rapid improvement of surrogate quality. When applied in the optimization superstructure, the surrogates can only be used when the prediction quality for the given data point reaches a specified threshold, otherwise the original simulation model is called for evaluating the process performance and the newly obtained data points are used to improve the surrogates. The method is tested on three simulation models, ranging in size and complexity. The proposed approach yields mean squared errors of the test prediction below 2% for all cases. Applying the active learning approach leads to better predictions compared to random sampling for the same size of database. When integrated in the optimization framework, simpler surrogates are favored in over 60% of cases, while the more complex ones are enabled by using simulation results generated during optimization for improving the surrogates after the initial generation. Significant time savings are recorded when using complex process simulations, though the advantage gained for simpler processes is marginal. Overall, we show that the proposed method saves time and adds flexibility to complex superstructure optimization problems that involve optimizing process operating conditions. Computational time can be greatly reduced without penalizing result quality, while the continuous improvement of surrogates when simulation is used in the optimization leads to a natural refinement of the model.

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Real-world application of machine-learning-based fault detection trained with experimental data

Cited by 67 publications

References 32 publications

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

Opportunities for Machine Learning in District Heating

Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

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