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A deep adversarial approach based on multi-sensor fusion for remaining useful life prognostics.
TL;DR: In this article, a deep adversarial learning approach to damage the remaining useful life of turbofan engines is proposed, which is applied to a public multi-sensor data set.
Abstract: Multi-sensor systems are proliferating the asset management industry and by proxy, the structural health management community. Asset managers are beginning to require a prognostics and health management system to predict and assess maintenance decisions. These systems handle big machinery data and multi-sensor fusion and integrate remaining useful life prognostic capabilities. We introduce a deep adversarial learning approach to damage prognostics. A non-Markovian variational inference-based model incorporating an adversarial training algorithm framework was developed. The proposed framework was applied to a public multi-sensor data set of turbofan engines to demonstrate its ability to predict remaining useful life. We find that using the deep adversarial based approach results in higher performing remaining useful life predictions.
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TL;DR: A review-based study uses step-by-step guidelines to help determine the appropriate solution for any specific type of driven data and uses these guidelines to determine learning model limitations, reconstruction challenges, and future prospects.
Abstract: Prognosis and health management (PHM) are mandatory tasks for real-time monitoring of damage propagation and aging of operating systems during working conditions. More definitely, PHM simplifies conditional maintenance planning by assessing the actual state of health (SoH) through the level of aging indicators. In fact, an accurate estimate of SoH helps determine remaining useful life (RUL), which is the period between the present and the end of a system’s useful life. Traditional residue-based modeling approaches that rely on the interpretation of appropriate physical laws to simulate operating behaviors fail as the complexity of systems increases. Therefore, machine learning (ML) becomes an unquestionable alternative that employs the behavior of historical data to mimic a large number of SoHs under varying working conditions. In this context, the objective of this paper is twofold. First, to provide an overview of recent developments of RUL prediction while reviewing recent ML tools used for RUL prediction in different critical systems. Second, and more importantly, to ensure that the RUL prediction process from data acquisition to model building and evaluation is straightforward. This paper also provides step-by-step guidelines to help determine the appropriate solution for any specific type of driven data. This guide is followed by a classification of different types of ML tools to cover all the discussed cases. Ultimately, this review-based study uses these guidelines to determine learning model limitations, reconstruction challenges, and future prospects.
22 citations
07 Dec 2020
TL;DR: In this article, the authors proposed a set of operational standards to meet safety and environmental requirements, while maintaining economical returns in the oil and gas industry. But, they did not consider the impact of these standards on the quality of service.
Abstract: Due to its capital-intensive nature, the Oil and Gas industry requires high operational standards to meet safety and environmental requirements, while maintaining economical returns. In this contex...
12 citations
TL;DR: In this paper , the authors propose an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic, and discuss the current challenges during ML applications and provide an outline of possible directions for future developments.
Abstract: While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments.
3 citations
References
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Proceedings Article•
01 Jan 2014TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.
20,769 citations
TL;DR: A concept of an n -person game in which each player has a finite set of pure strategies and in which a definite set of payments to the n players corresponds to each n -tuple ofpure strategies, one strategy being taken for each player.
Abstract: One may define a concept of an n -person game in which each player has a finite set of pure strategies and in which a definite set of payments to the n players corresponds to each n -tuple of pure strategies, one strategy being taken for each player. For mixed strategies, which are probability distributions over the pure strategies, the pay-off functions are the expectations of the players, thus becoming polylinear forms …
7,047 citations
Posted Content•
TL;DR: In this paper, a stochastic variational inference and learning algorithm was proposed for directed probabilistic models with intractable posterior distributions and large datasets, which scales to large datasets.
Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.
4,883 citations
TL;DR: This paper systematically reviews the recent modeling developments for estimating the RUL and focuses on statistical data driven approaches which rely only on available past observed data and statistical models.
1,667 citations
TL;DR: A new data-driven approach for prognostics using deep convolution neural networks (DCNN) using time window approach is employed for sample preparation in order for better feature extraction by DCNN.
948 citations