287 research outputs found

    Fault diagnosis-based SDG transfer for zero-sample fault symptom

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    The traditional fault diagnosis models cannot achieve good fault diagnosis accuracy when a new unseen fault class appears in the test set, but there is no training sample of this fault in the training set. Therefore, studying the unseen cause-effect problem of fault symptoms is extremely challenging. As various faults often occur in a chemical plant, it is necessary to perform fault causal-effect diagnosis to find the root cause of the fault. However, only some fault causal-effect data are always available to construct a reliable causal-effect diagnosis model. Another worst thing is that measurement noise often contaminates the collected data. The above problems are very common in industrial operations. However, past-developed data-driven approaches rarely include causal-effect relationships between variables, particularly in the zero-shot of causal-effect relationships. This would cause incorrect inference of seen faults and make it impossible to predict unseen faults. This study effectively combines zero-shot learning, conditional variational autoencoders (CVAE), and the signed directed graph (SDG) to solve the above problems. Specifically, the learning approach that determines the cause-effect of all the faults using SDG with physics knowledge to obtain the fault description. SDG is used to determine the attributes of the seen and unseen faults. Instead of the seen fault label space, attributes can easily create an unseen fault space from a seen fault space. After having the corresponding attribute spaces of the failure cause, some failure causes are learned in advance by a CVAE model from the available fault data. The advantage of the CVAE is that process variables are mapped into the latent space for dimension reduction and measurement noise deduction; the latent data can more accurately represent the actual behavior of the process. Then, with the extended space spanned by unseen attributes, the migration capabilities can predict the unseen causes of failure and infer the causes of the unseen failures. Finally, the feasibility of the proposed method is verified by the data collected from chemical reaction processes

    Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

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    International audienceHydrogenerators are strategic assets for power utilities. Their reliability and availability can lead to significant benefits. For decades, monitoring and diagnosis of hydrogenerators have been at the core of maintenance strategies. A significant part of generator diagnosis relies on Partial Discharge (PD) measurements, because the main cause of hydrogenerator breakdown comes from failure of its high voltage stator, which is a major component of hydrogenerators. A study of all stator failure mechanisms reveals that more than 85 % of them involve the presence of PD activity. PD signal can be detected from the lead of the hydrogenerator while it is running, thus allowing for on-line diagnosis. Hydro-Quรฉbec has been collecting more than 33 000 unlabeled PD measurement files over the last decades. Up to now, this diagnostic technique has been quantified based on global PD amplitudes and integrated PD energy irrespective of the source of the PD signal. Several PD sources exist and they all have different relative risk, but in order to recognize the nature of the PD, or its source, the judgement of experts is required. In this paper, we propose a new method based on visual data analysis to build a PD source classifier with a minimum of labeled data. A convolutional variational autoencoder has been used to help experts to visually select the best training data set in order to improve the performances of the PD source classifier

    ๋งค๊ฐœ๋ถ„ํฌ๊ทผ์‚ฌ๋ฅผ ํ†ตํ•œ ๊ณต์ •์‹œ์Šคํ…œ ๊ณตํ•™์—์„œ์˜ ํ™•๋ฅ ๊ธฐ๊ณ„ํ•™์Šต ์ ‘๊ทผ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2021.8. ์ด์ข…๋ฏผ.With the rapid development of measurement technology, higher quality and vast amounts of process data become available. Nevertheless, process data are โ€˜scarceโ€™ in many cases as they are sampled only at certain operating conditions while the dimensionality of the system is large. Furthermore, the process data are inherently stochastic due to the internal characteristics of the system or the measurement noises. For this reason, uncertainty is inevitable in process systems, and estimating it becomes a crucial part of engineering tasks as the prediction errors can lead to misguided decisions and cause severe casualties or economic losses. A popular approach to this is applying probabilistic inference techniques that can model the uncertainty in terms of probability. However, most of the existing probabilistic inference techniques are based on recursive sampling, which makes it difficult to use them for industrial applications that require processing a high-dimensional and massive amount of data. To address such an issue, this thesis proposes probabilistic machine learning approaches based on parametric distribution approximation, which can model the uncertainty of the system and circumvent the computational complexity as well. The proposed approach is applied for three major process engineering tasks: process monitoring, system modeling, and process design. First, a process monitoring framework is proposed that utilizes a probabilistic classifier for fault classification. To enhance the accuracy of the classifier and reduce the computational cost for its training, a feature extraction method called probabilistic manifold learning is developed and applied to the process data ahead of the fault classification. We demonstrate that this manifold approximation process not only reduces the dimensionality of the data but also casts the data into a clustered structure, making the classifier have a low dependency on the type and dimension of the data. By exploiting this property, non-metric information (e.g., fault labels) of the data is effectively incorporated and the diagnosis performance is drastically improved. Second, a probabilistic modeling approach based on Bayesian neural networks is proposed. The parameters of deep neural networks are transformed into Gaussian distributions and trained using variational inference. The redundancy of the parameter is autonomously inferred during the model training, and insignificant parameters are eliminated a posteriori. Through a verification study, we demonstrate that the proposed approach can not only produce high-fidelity models that describe the stochastic behaviors of the system but also produce the optimal model structure. Finally, a novel process design framework is proposed based on reinforcement learning. Unlike the conventional optimization methods that recursively evaluate the objective function to find an optimal value, the proposed method approximates the objective function surface by parametric probabilistic distributions. This allows learning the continuous action policy without introducing any cumbersome discretization process. Moreover, the probabilistic policy gives means for effective control of the exploration and exploitation rates according to the certainty information. We demonstrate that the proposed framework can learn process design heuristics during the solution process and use them to solve similar design problems.๊ณ„์ธก๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๋กœ ์–‘์งˆ์˜, ๊ทธ๋ฆฌ๊ณ  ๋ฐฉ๋Œ€ํ•œ ์–‘์˜ ๊ณต์ • ๋ฐ์ดํ„ฐ์˜ ์ทจ๋“์ด ๊ฐ€๋Šฅํ•ด์กŒ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋งŽ์€ ๊ฒฝ์šฐ ์‹œ์Šคํ…œ ์ฐจ์›์˜ ํฌ๊ธฐ์— ๋น„ํ•ด์„œ ์ผ๋ถ€ ์šด์ „์กฐ๊ฑด์˜ ๊ณต์ • ๋ฐ์ดํ„ฐ๋งŒ์ด ์ทจ๋“๋˜๊ธฐ ๋•Œ๋ฌธ์—, ๊ณต์ • ๋ฐ์ดํ„ฐ๋Š” โ€˜ํฌ์†Œโ€™ํ•˜๊ฒŒ ๋œ๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ๊ณต์ • ๋ฐ์ดํ„ฐ๋Š” ์‹œ์Šคํ…œ ๊ฑฐ๋™ ์ž์ฒด์™€ ๋”๋ถˆ์–ด ๊ณ„์ธก์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋…ธ์ด์ฆˆ๋กœ ์ธํ•œ ๋ณธ์งˆ์ ์ธ ํ™•๋ฅ ์  ๊ฑฐ๋™์„ ๋ณด์ธ๋‹ค. ๋”ฐ๋ผ์„œ ์‹œ์Šคํ…œ์˜ ์˜ˆ์ธก๋ชจ๋ธ์€ ์˜ˆ์ธก ๊ฐ’์— ๋Œ€ํ•œ ๋ถˆํ™•์‹ค์„ฑ์„ ์ •๋Ÿ‰์ ์œผ๋กœ ๊ธฐ์ˆ ํ•˜๋Š” ๊ฒƒ์ด ์š”๊ตฌ๋˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ์˜ค์ง„์„ ์˜ˆ๋ฐฉํ•˜๊ณ  ์ž ์žฌ์  ์ธ๋ช… ํ”ผํ•ด์™€ ๊ฒฝ์ œ์  ์†์‹ค์„ ๋ฐฉ์ง€ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋Œ€ํ•œ ๋ณดํŽธ์ ์ธ ์ ‘๊ทผ๋ฒ•์€ ํ™•๋ฅ ์ถ”์ •๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ์ด๋Ÿฌํ•œ ๋ถˆํ™•์‹ค์„ฑ์„ ์ •๋Ÿ‰ํ™” ํ•˜๋Š” ๊ฒƒ์ด๋‚˜, ํ˜„์กดํ•˜๋Š” ์ถ”์ •๊ธฐ๋ฒ•๋“ค์€ ์žฌ๊ท€์  ์ƒ˜ํ”Œ๋ง์— ์˜์กดํ•˜๋Š” ํŠน์„ฑ์ƒ ๊ณ ์ฐจ์›์ด๋ฉด์„œ๋„ ๋‹ค๋Ÿ‰์ธ ๊ณต์ •๋ฐ์ดํ„ฐ์— ์ ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค๋Š” ๊ทผ๋ณธ์ ์ธ ํ•œ๊ณ„๋ฅผ ๊ฐ€์ง„๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ๋งค๊ฐœ๋ถ„ํฌ๊ทผ์‚ฌ์— ๊ธฐ๋ฐ˜ํ•œ ํ™•๋ฅ ๊ธฐ๊ณ„ํ•™์Šต์„ ์ ์šฉํ•˜์—ฌ ์‹œ์Šคํ…œ์— ๋‚ด์žฌ๋œ ๋ถˆํ™•์‹ค์„ฑ์„ ๋ชจ๋ธ๋งํ•˜๋ฉด์„œ๋„ ๋™์‹œ์— ๊ณ„์‚ฐ ํšจ์œจ์ ์ธ ์ ‘๊ทผ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋จผ์ €, ๊ณต์ •์˜ ๋ชจ๋‹ˆํ„ฐ๋ง์— ์žˆ์–ด ๊ฐ€์šฐ์‹œ์•ˆ ํ˜ผํ•ฉ ๋ชจ๋ธ (Gaussian mixture model)์„ ๋ถ„๋ฅ˜์ž๋กœ ์‚ฌ์šฉํ•˜๋Š” ํ™•๋ฅ ์  ๊ฒฐํ•จ ๋ถ„๋ฅ˜ ํ”„๋ ˆ์ž„์›Œํฌ๊ฐ€ ์ œ์•ˆ๋˜์—ˆ๋‹ค. ์ด๋•Œ ๋ถ„๋ฅ˜์ž์˜ ํ•™์Šต์—์„œ์˜ ๊ณ„์‚ฐ ๋ณต์žก๋„๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•˜์—ฌ ๋ฐ์ดํ„ฐ๋ฅผ ์ €์ฐจ์›์œผ๋กœ ํˆฌ์˜์‹œํ‚ค๋Š”๋ฐ, ์ด๋ฅผ ์œ„ํ•œ ํ™•๋ฅ ์  ๋‹ค์–‘์ฒด ํ•™์Šต (probabilistic manifold learn-ing) ๋ฐฉ๋ฒ•์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ๋ฐ์ดํ„ฐ์˜ ๋‹ค์–‘์ฒด (manifold)๋ฅผ ๊ทผ์‚ฌํ•˜์—ฌ ๋ฐ์ดํ„ฐ ํฌ์ธํŠธ ์‚ฌ์ด์˜ ์Œ๋ณ„ ์šฐ๋„ (pairwise likelihood)๋ฅผ ๋ณด์กดํ•˜๋Š” ํˆฌ์˜๋ฒ•์ด ์‚ฌ์šฉ๋œ๋‹ค. ์ด๋ฅผ ํ†ตํ•˜์—ฌ ๋ฐ์ดํ„ฐ์˜ ์ข…๋ฅ˜์™€ ์ฐจ์›์— ์˜์กด๋„๊ฐ€ ๋‚ฎ์€ ์ง„๋‹จ ๊ฒฐ๊ณผ๋ฅผ ์–ป์Œ๊ณผ ๋™์‹œ์— ๋ฐ์ดํ„ฐ ๋ ˆ์ด๋ธ”๊ณผ ๊ฐ™์€ ๋น„๊ฑฐ๋ฆฌ์  (non-metric) ์ •๋ณด๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์‚ฌ์šฉํ•˜์—ฌ ๊ฒฐํ•จ ์ง„๋‹จ ๋Šฅ๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ๋ณด์˜€๋‹ค. ๋‘˜์งธ๋กœ, ๋ฒ ์ด์ง€์•ˆ ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง(Bayesian deep neural networks)์„ ์‚ฌ์šฉํ•œ ๊ณต์ •์˜ ํ™•๋ฅ ์  ๋ชจ๋ธ๋ง ๋ฐฉ๋ฒ•๋ก ์ด ์ œ์‹œ๋˜์—ˆ๋‹ค. ์‹ ๊ฒฝ๋ง์˜ ๊ฐ ๋งค๊ฐœ๋ณ€์ˆ˜๋Š” ๊ฐ€์šฐ์Šค ๋ถ„ํฌ๋กœ ์น˜ํ™˜๋˜๋ฉฐ, ๋ณ€๋ถ„์ถ”๋ก  (variational inference)์„ ํ†ตํ•˜์—ฌ ๊ณ„์‚ฐ ํšจ์œจ์ ์ธ ํ›ˆ๋ จ์ด ์ง„ํ–‰๋œ๋‹ค. ํ›ˆ๋ จ์ด ๋๋‚œ ํ›„ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ์œ ํšจ์„ฑ์„ ์ธก์ •ํ•˜์—ฌ ๋ถˆํ•„์š”ํ•œ ๋งค๊ฐœ๋ณ€์ˆ˜๋ฅผ ์†Œ๊ฑฐํ•˜๋Š” ์‚ฌํ›„ ๋ชจ๋ธ ์••์ถ• ๋ฐฉ๋ฒ•์ด ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ๋ฐ˜๋„์ฒด ๊ณต์ •์— ๋Œ€ํ•œ ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋Š” ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ๊ณต์ •์˜ ๋ณต์žกํ•œ ๊ฑฐ๋™์„ ํšจ๊ณผ์ ์œผ๋กœ ๋ชจ๋ธ๋ง ํ•  ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋ชจ๋ธ์˜ ์ตœ์  ๊ตฌ์กฐ๋ฅผ ๋„์ถœํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ถ„ํฌํ˜• ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง์„ ์‚ฌ์šฉํ•œ ๊ฐ•ํ™”ํ•™์Šต์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ํ™•๋ฅ ์  ๊ณต์ • ์„ค๊ณ„ ํ”„๋ ˆ์ž„์›Œํฌ๊ฐ€ ์ œ์•ˆ๋˜์—ˆ๋‹ค. ์ตœ์ ์น˜๋ฅผ ์ฐพ๊ธฐ ์œ„ํ•ด ์žฌ๊ท€์ ์œผ๋กœ ๋ชฉ์  ํ•จ์ˆ˜ ๊ฐ’์„ ํ‰๊ฐ€ํ•˜๋Š” ๊ธฐ์กด์˜ ์ตœ์ ํ™” ๋ฐฉ๋ฒ•๋ก ๊ณผ ๋‹ฌ๋ฆฌ, ๋ชฉ์  ํ•จ์ˆ˜ ๊ณก๋ฉด (objective function surface)์„ ๋งค๊ฐœํ™” ๋œ ํ™•๋ฅ ๋ถ„ํฌ๋กœ ๊ทผ์‚ฌํ•˜๋Š” ์ ‘๊ทผ๋ฒ•์ด ์ œ์‹œ๋˜์—ˆ๋‹ค. ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ด์‚ฐํ™” (discretization)๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š๊ณ  ์—ฐ์†์  ํ–‰๋™ ์ •์ฑ…์„ ํ•™์Šตํ•˜๋ฉฐ, ํ™•์‹ค์„ฑ (certainty)์— ๊ธฐ๋ฐ˜ํ•œ ํƒ์ƒ‰ (exploration) ๋ฐ ํ™œ์šฉ (exploi-tation) ๋น„์œจ์˜ ์ œ์–ด๊ฐ€ ํšจ์œจ์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค. ์‚ฌ๋ก€ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๊ณต์ •์˜ ์„ค๊ณ„์— ๋Œ€ํ•œ ๊ฒฝํ—˜์ง€์‹ (heuristic)์„ ํ•™์Šตํ•˜๊ณ  ์œ ์‚ฌํ•œ ์„ค๊ณ„ ๋ฌธ์ œ์˜ ํ•ด๋ฅผ ๊ตฌํ•˜๋Š” ๋ฐ ์ด์šฉํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ค€๋‹ค.Chapter 1 Introduction 1 1.1. Motivation 1 1.2. Outline of the thesis 5 Chapter 2 Backgrounds and preliminaries 9 2.1. Bayesian inference 9 2.2. Monte Carlo 10 2.3. Kullback-Leibler divergence 11 2.4. Variational inference 12 2.5. Riemannian manifold 13 2.6. Finite extended-pseudo-metric space 16 2.7. Reinforcement learning 16 2.8. Directed graph 19 Chapter 3 Process monitoring and fault classification with probabilistic manifold learning 20 3.1. Introduction 20 3.2. Methods 25 3.2.1. Uniform manifold approximation 27 3.2.2. Clusterization 28 3.2.3. Projection 31 3.2.4. Mapping of unknown data query 32 3.2.5. Inference 33 3.3. Verification study 38 3.3.1. Dataset description 38 3.3.2. Experimental setup 40 3.3.3. Process monitoring 43 3.3.4. Projection characteristics 47 3.3.5. Fault diagnosis 50 3.3.6. Computational Aspects 56 Chapter 4 Process system modeling with Bayesian neural networks 59 4.1. Introduction 59 4.2. Methods 63 4.2.1. Long Short-Term Memory (LSTM) 63 4.2.2. Bayesian LSTM (BLSTM) 66 4.3. Verification study 68 4.3.1. System description 68 4.3.2. Estimation of the plasma variables 71 4.3.3. Dataset description 72 4.3.4. Experimental setup 72 4.3.5. Weight regularization during training 78 4.3.6. Modeling complex behaviors of the system 80 4.3.7. Uncertainty quantification and model compression 85 Chapter 5 Process design based on reinforcement learning with distributional actor-critic networks 89 5.1. Introduction 89 5.2. Methods 93 5.2.1. Flowsheet hashing 93 5.2.2. Behavioral cloning 99 5.2.3. Neural Monte Carlo tree search (N-MCTS) 100 5.2.4. Distributional actor-critic networks (DACN) 105 5.2.5. Action masking 110 5.3. Verification study 110 5.3.1. System description 110 5.3.2. Experimental setup 111 5.3.3. Result and discussions 115 Chapter 6 Concluding remarks 120 6.1. Summary of the contributions 120 6.2. Future works 122 Appendix 125 A.1. Proof of Lemma 1 125 A.2. Performance indices for dimension reduction 127 A.3. Model equations for process units 130 Bibliography 132 ์ดˆ ๋ก 149๋ฐ•

    Deep Learning for predictive maintenance

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    Recently, with the appearance of Industry 4.0 (I4.0), machine learning (ML) within artificial intelligence (AI), industrial Internet of things (IIoT) and cyber-physical system (CPS) have accelerated the development of a data-orientated applications such as predictive maintenance (PdM). PdM applied to asset-dependent industries has led to operational cost savings, productivity improvements and enhanced safety management capabilities. In addition, predictive maintenance strategies provide useful information concerning the source of the failure or malfunction, reducing unnecessary maintenance operations. The concept of prognostics and health management (PHM) has appeared as a predictive maintenance process. PHM has become an unavoidable tendency in smart manufacturing to offer a reliable solution for handling industrial equipmentโ€™s health status. This later requires efficient and effective system health monitoring methods, including processing and analysing massive machinery data to detect anomalies and perform diagnosis and prognosis. Prognostics is considered a key PHM process with capabilities for predicting future states, mainly based on predicting the residual lifetime during which a machine can perform its intended function, i.e., estimating the remaining useful life (RUL) of a system. The prognostic research domain is far from being mature, which is still new and explains the various challenges that must be addressed. Therefore, the work presented in this thesis will mainly focus on the prognostic of monitored machinery from an RUL estimation point of view using Deep Learning (DL) algorithms. Capitalising on the recent success of the DL, this dissertation introduces methods and algorithms dedicated to predictive maintenance. We focused on improving the performance of aero-engine prognostic, particularly in estimating an accurate RUL using ensemble learning and deep learning. To this end, two contributions have been proposed, and the results obtained were validated by an extensive comparative analysis using public C-MAPSS turbofan engine benchmark datasets. The first contribution, for RUL predictions, we proposed two-hybrid methods based on the promising DL architectures to leverage the power of the multimodal and hybrid deep neural network in order to capture various information at different time intervals and ultimately achieve more accurate RUL predictions. The proposed end-to-end deep architectures jointly optimise the feature reduction and RUL prediction steps in a hierarchical manner, intending to achieve data representation in low dimensionality and minimal variable redundancy while preserving critical asset degradation information with minimal preprocessing effort. The second contribution, in a practical situation, RUL is usually affected by uncertainty. Therefore, we proposed an innovative RUL estimation strategy that assesses degrading machineryโ€™s health status (provides the probabilities of system failure in different time windows) and provides the prediction of RUL window. Keywords: Prognostics and Health Management (PHM), Remaining useful life (RUL), Predictive Maintenance (PdM), C-MAPSS dataset, Ensemble learning, Deep learnin

    D'ya like DAGs? A Survey on Structure Learning and Causal Discovery

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    Causal reasoning is a crucial part of science and human intelligence. In order to discover causal relationships from data, we need structure discovery methods. We provide a review of background theory and a survey of methods for structure discovery. We primarily focus on modern, continuous optimization methods, and provide reference to further resources such as benchmark datasets and software packages. Finally, we discuss the assumptive leap required to take us from structure to causality.Comment: 35 page

    Un enfoque de sustentabilidad utilizando lรณgica difusa y minerรญa de datos

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    [ES] Sustainable development goals are now the agreed criteria to monitor states, and this work will demonstrate that numerical and graphical methods are valuable tools in assessing progress. Fuzzy Logic is a reliable procedure for transforming human qualitative knowledge into quantitative variables that can be used in the reasoning of the type โ€œif, thenโ€ to obtain answers pertaining to sustainability assessment. Applications of machine learning techniques and artificial intelligence procedures span almost all fields of science. Here, for the first-time, unsupervised machine learning is applied to sustainability assessment, combining numerical approaches with graphical procedures to analyze global sustainability. CD HJ-Biplots to portray graphically the sustainability position of a large number of countries are a useful complement to mathematical models of sustainability. Graphical information could be useful to planners it shows directly how countries are grouped according to the most related sustainability indicators. Thus, planners can prioritize social, environmental, and economic policies and make the most effective decisions. One could graphically observe the dynamic evolution of sustainability worldwide over time with a graphical approach used to draw relevant conclusions. In an era of climate change, species extinction, poverty, and environmental migration, such observations could aid political decision-making regarding the future of our planet. A large number of countries remain in the areas of moderate or low sustainability. Fuzzy logic has proven to be an uncontested numerical method as it occurs with SAFE. An unsupervised learning method called Variational Autoencoder interplay Graphical Analysis (VEA&GA) has been proposed, to support sustainability performance with appropriate training data. The promising results show that this can be a sound alternative to assess sustainability, extrapolating its applications to other kinds of problems at different levels of analysis (continents, regions, cities, etc.) further corroborating the effectiveness of the unsupervised training methods

    Machine-learning-based condition assessment of gas turbine: a review

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    Condition monitoring, diagnostics, and prognostics are key factors in todayโ€™s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machinelearning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.This research was funded by Siemens Energy.Peer ReviewedPostprint (published version

    Data-Driven Modeling For Decision Support Systems And Treatment Management In Personalized Healthcare

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    Massive amount of electronic medical records (EMRs) accumulating from patients and populations motivates clinicians and data scientists to collaborate for the advanced analytics to create knowledge that is essential to address the extensive personalized insights needed for patients, clinicians, providers, scientists, and health policy makers. Learning from large and complicated data is using extensively in marketing and commercial enterprises to generate personalized recommendations. Recently the medical research community focuses to take the benefits of big data analytic approaches and moves to personalized (precision) medicine. So, it is a significant period in healthcare and medicine for transferring to a new paradigm. There is a noticeable opportunity to implement a learning health care system and data-driven healthcare to make better medical decisions, better personalized predictions; and more precise discovering of risk factors and their interactions. In this research we focus on data-driven approaches for personalized medicine. We propose a research framework which emphasizes on three main phases: 1) Predictive modeling, 2) Patient subgroup analysis and 3) Treatment recommendation. Our goal is to develop novel methods for each phase and apply them in real-world applications. In the fist phase, we develop a new predictive approach based on feature representation using deep feature learning and word embedding techniques. Our method uses different deep architectures (Stacked autoencoders, Deep belief network and Variational autoencoders) for feature representation in higher-level abstractions to obtain effective and more robust features from EMRs, and then build prediction models on the top of them. Our approach is particularly useful when the unlabeled data is abundant whereas labeled one is scarce. We investigate the performance of representation learning through a supervised approach. We perform our method on different small and large datasets. Finally we provide a comparative study and show that our predictive approach leads to better results in comparison with others. In the second phase, we propose a novel patient subgroup detection method, called Supervised Biclustring (SUBIC) using convex optimization and apply our approach to detect patient subgroups and prioritize risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach not only finds patient subgroups with guidance of a clinically relevant target variable but also identifies and prioritizes risk factors by pursuing sparsity of the input variables and encouraging similarity among the input variables and between the input and target variables. Finally, in the third phase, we introduce a new survival analysis framework using deep learning and active learning with a novel sampling strategy. First, our approach provides better representation with lower dimensions from clinical features using labeled (time-to-event) and unlabeled (censored) instances and then actively trains the survival model by labeling the censored data using an oracle. As a clinical assistive tool, we propose a simple yet effective treatment recommendation approach based on our survival model. In the experimental study, we apply our approach on SEER-Medicare data related to prostate cancer among African-Americans and white patients. The results indicate that our approach outperforms significantly than baseline models

    IoT Anomaly Detection Methods and Applications: A Survey

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    Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly expanding field. This growth necessitates an examination of application trends and current gaps. The vast majority of those publications are in areas such as network and infrastructure security, sensor monitoring, smart home, and smart city applications and are extending into even more sectors. Recent advancements in the field have increased the necessity to study the many IoT anomaly detection applications. This paper begins with a summary of the detection methods and applications, accompanied by a discussion of the categorization of IoT anomaly detection algorithms. We then discuss the current publications to identify distinct application domains, examining papers chosen based on our search criteria. The survey considers 64 papers among recent publications published between January 2019 and July 2021. In recent publications, we observed a shortage of IoT anomaly detection methodologies, for example, when dealing with the integration of systems with various sensors, data and concept drifts, and data augmentation where there is a shortage of Ground Truth data. Finally, we discuss the present such challenges and offer new perspectives where further research is required.Comment: 22 page
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