A Semi-supervised Approach for Steam Turbine Health Prognostics Based on GAN and PF

Que, Zijun; Xiong, Yi; Xu, Zhengguo

doi:10.1109/ieem44572.2019.8978717

Cited by 9 publications

(7 citation statements)

References 15 publications

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“…Since GANs essentially place two networks in opposition to one another, game theory benchmarks such as Nash Equilibrium are sometimes used to determine when the training is good enough [58,59]. GANs were initially created as an unsupervised learning technique, but they have also succeeded in unsupervised and semisupervised PM applications [60].…”

Section: ) Generative Modelsmentioning

confidence: 99%

“…LED Degradation [28] Li-ion Battery Aging [178] [42, 66, 67, 111, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200] Mikron Milling Machine [201] Proton Exchange Membrane Fuel Cell [202,203,204,205,206] Planetary Gearbox Experiment Platform [108,207] Rail Fastener of Subway Operation Line [208] Steam turbines of Power Generator [60] Switched-Mode Power Supply [209] Super Capacitors [124,210] Transformer Deterioration [30,74,211] UH-60 Helicopter Planetary Gear Plate [212] Vehicle [213] Vertical Roller Mill [214] Wind Turbines [215,216,217] This section evaluates and summarizes the state-of-the-art ML algorithms by dividing them into five categories: neural network-based algorithms, generative models, kernel-based learning, probabilistic models, and fuzzy logic-based methods. Table 1 lists the datasets used by the articles evaluated in the present manuscript.…”

Section: Ml-based Approaches For Rul Predictionmentioning

confidence: 99%

“…) Generative Adversarial Network Que et al[60] used GANs and Particle Filters (PF) for anomaly detection. They created an HI as the output of theVOLUME 11, 2023 …”

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confidence: 99%

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Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Ramezani¹,

Cummins²,

Killen³

et al. 2023

Preprint

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<ul> <li>This work summarizes the state-of-the-art data-driven methods for prediction of the Remaining Useful Life<br> (RUL)<br> </li> <li>It discusses challenges and open problems faced in PdM<br> </li> <li>This study presents a discussion on the new problems that need to be considered towards the Industry 4.0 goals<br> </li> <li>We propose the future direction for each challenge discussed in this article</li> </ul>

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Section: ) Generative Modelsmentioning

confidence: 99%

Section: Ml-based Approaches For Rul Predictionmentioning

confidence: 99%

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Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Ramezani¹,

Cummins²,

Killen³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Since GANs essentially place two networks in opposition to one another, game theory benchmarks such as Nash Equilibrium are sometimes used to determine when the training is good enough [60,61]. GANs were initially created as an unsupervised learning technique, but they have also succeeded in unsupervised and semisupervised PM applications [62].…”

Section: ) Generative Modelsmentioning

confidence: 99%

“…LED Degradation [28] Li-ion Battery Aging [194] [42, 68, 69, 126, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216] Mikron Milling Machine [217] Proton Exchange Membrane Fuel Cell [218,219,220,221,222] Planetary Gearbox Experiment Platform [123,223] Rail Fastener of Subway Operation Line [224] Steam turbines of Power Generator [62] Switched-Mode Power Supply [225] Super Capacitors [139,226] Transformer Deterioration [30,76,227] UH-60 Helicopter Planetary Gear Plate [228] Vehicle [229] Vertical Roller Mill [230] Wind Turbines [231,232,233] This section evaluates and summarizes the state-of-the-art ML algorithms by dividing them into five categories: neural network-based algorithms, generative models, kernel-based learning, probabilistic models, and fuzzy logic-based methods. Table 2 lists the datasets used by the articles evaluated in the present manuscript.…”

Section: Ml-based Approaches For Rul Predictionmentioning

confidence: 99%

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

et al. 2023

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Early detection of faulty patterns and timely scheduling of maintenance events can minimize risk to the underlying processes and increase a system's lifespan, reliability, and availability. Two main data-driven approaches are used in the literature to determine the Remaining Useful Life (RUL): direct calculation from raw data and indirect analysis by revealing the transitions from one latent state to another and highlighting degradation in a system's Health Indices. The present study discusses the state-of-theart data-driven methods introduced for RUL prediction in predictive maintenance, by looking at their capabilities, scalability, performance, and weaknesses. We will also discuss the challenges faced with the current approaches and the future directions to tackle the current limitations.

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Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Sabuhi¹,

Zhou

Bezemer

et al. 2021

IEEE Access

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Anomaly detection has become an indispensable tool for modern society, applied in a wide range of applications, from detecting fraudulent transactions to malignant brain tumors. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: lack of data that represents anomalous behaviour. As anomalous behaviour is usually costly (or dangerous) for a system, it is difficult to gather enough data that represents such behaviour. This, in turn, makes it difficult to develop and evaluate anomaly detection techniques. Recently, generative adversarial networks (GANs) have attracted much attention in anomaly detection research, due to their unique ability to generate new data. In this paper, we present a systematic review of the literature in this area, covering 128 papers. The goal of this review paper is to analyze the relation between anomaly detection techniques and types of GANs, to identify the most common application domains for GAN-assisted and GAN-based anomaly detection, and to assemble information on datasets and performance metrics used to assess them. Our study helps researchers and practitioners to find the most suitable GAN-assisted anomaly detection technique for their application. In addition, we present a research roadmap for future studies in this area. In summary, GANs are used in anomaly detection to address the problem of insufficient amount of data for the anomalous behaviour, either through data augmentation or representation learning. The most commonly used GAN architectures are DCGANs, standard GANs, and cGANs. The primary application domains include medicine, surveillance and intrusion detection.

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A Semi-supervised Approach for Steam Turbine Health Prognostics Based on GAN and PF

Cited by 9 publications

References 15 publications

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

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