Remaining Useful Life Prognosis for Turbofan Engine Using Explainable Deep Neural Networks with Dimensionality Reduction

Hong, Chang Woo; Lee, Changmin; Lee, Kwangsuk; Ko, Min-Seung; Kim, DaeEun; Hur, Kyeong

doi:10.3390/s20226626

Cited by 52 publications

(23 citation statements)

References 38 publications

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“…Recently, Hong et al [39] applied DL techniques to prognose the remaining useful life of a turbofan engine. The proposed model consists of a network with a one-dimensional convolutional neural network (CNN), LSTM and bidirectional LSTM.…”

Section: State Of the Artmentioning

confidence: 99%

SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets

et al. 2021

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Predictive maintenance has lately proved to be a useful tool for optimizing costs, performance and systems availability. Furthermore, the greater and more complex the system, the higher the benefit but also the less applied: Architectural, computational and complexity limitations have historically ballasted the adoption of predictive maintenance on the biggest systems. This has been especially true in military systems where the security and criticality of the operations do not accept uncertainty. This paper describes the work conducted in addressing these challenges, aiming to evaluate its applicability in a real scenario: It presents a specific design and development for an actual big and diverse ecosystem of equipment, proposing an semi-unsupervised predictive maintenance system. In addition, it depicts the solution deployment, test and technological adoption of real-world military operative environments and validates the applicability.

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Section: State Of the Artmentioning

confidence: 99%

SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets

et al. 2021

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“…Rodriguez et al employed this in their work. 58 for explanation generation for pharmaceutical industry data set, by Hong et al 33 used with DNN to predict health analytic tool.…”

Section: Model Agnostic Local Surrogatementioning

confidence: 99%

Towards Explainable Artificial Intelligence in Banking and Financial Services

Hanif¹

2021

Preprint

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Artificial intelligence (AI) enables machines to learn from human experience, adjust to new inputs, and perform human-like tasks. AI is progressing rapidly and is transforming the way businesses operate, from process automation to cognitive augmentation of tasks and intelligent process/data analytics. However, the main challenge for human users would be to understand and appropriately trust the result of AI algorithms and methods. In this paper, to address this challenge, we study and analyze the recent work done in Explainable Artificial Intelligence (XAI) methods and tools. We introduce a novel XAI process, which facilitates producing explainable models while maintaining a high level of learning performance. We present an interactive evidence-based approach to assist human users in comprehending and trusting the results and output created by AI-enabled algorithms. We adopt a typical scenario in the Banking domain for analyzing customer transactions. We develop a digital dashboard to facilitate interacting with the algorithm results and discuss how the proposed XAI method can significantly improve the confidence of data scientists in understanding the result of AI-enabled algorithms. KEYWORDS Business Process Analytics; Explainable AI; Machine learning 1 INTRODUCTIONThis section present an overview of the completed study and explains the author's rationale to conduct this research. We will go through the problem we are addressing and discuss the impacts of the proposed approach for the business organizations. Furthermore, we present the project's contribution to research and academia to meet the goals and its response to the demands of the finance industry. OverviewIn the last decade, the world has envisioned tremendous growth in technology with the improved accessibility of data, cloud resources, and machine learning (ML) algorithms evolution. The intelligent system has achieved significant performance with this growth. The super performance of these algorithms in various domains has increased the popularity of artificial intelligence (AI). However, alongside these achievements, the non-transparent, ambiguity, and inability to expound and interpret the majority of the state-of-the-art techniques are considered ethical issues. These flaws in AI algorithms impede the acceptance of complex ML models in a variety of fields such as medical, banking and finance, security, and education and have prompted many concerns about the security and safety of ML system users. According to the current regulations and policies, these systems must be transparent to meet the right to explanation. Due to a lack of trust in existing ML-based systems, explainable artificial intelligence (XAI)-based methods are gaining popularity. Although neither the domain nor the methods are novel, they are gaining popularity due to their ability to unbox the black box.

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“…ANN is based on data from continuous monitoring programs, which include preparation samples [18]. The most widely used method for calculating the useful life of turbofan engines is Artificial Neural Networks (ANN) [19]. In certain literature, related work has been conducted.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of an Aircraft Turbofan Engine Using Deep Layer Recurrent Neural Networks

Thakkar

Chaoui

2022

Actuators

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The turbofan engine is a pivotal component of the aircraft. Engine components are susceptible to degradation over the life of their operation, which affects the reliability and performance of an engine. In order to direct the necessary maintenance behavior, remaining useful life prediction is the key. This research uses machine learning to provide a prediction framework for an aircraft’s remaining useful life (RUL) based on the entire life cycle data and deterioration parameter data (ML). For the engine’s lifetime assessment, a Deep Layer Recurrent Neural Network (DL-RNN) model is presented. The suggested method is compared to Multilayer Perceptron (MLP), Nonlinear Auto Regressive Network with Exogenous Inputs (NARX), and Cascade Forward Neural Network (CFNN), as well as the Prognostics and Health Management (PHM) conference Challenge dataset and NASA’s C-MAPSS dataset. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are calculated for both the datasets, and the values are in the range of 0.15% to 0.203% for DL-RNN, whereas for the other three topologies, they are in the range of 0.2% to 4.8%. Comparative results show a better predictive accuracy with respect to other ML algorithms.

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Remaining Useful Life Prognosis for Turbofan Engine Using Explainable Deep Neural Networks with Dimensionality Reduction

Cited by 52 publications

References 38 publications

SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets

SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets

Towards Explainable Artificial Intelligence in Banking and Financial Services

Remaining Useful Life Prediction of an Aircraft Turbofan Engine Using Deep Layer Recurrent Neural Networks

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