Yunhan Kim

Bio: Yunhan Kim is an academic researcher from Seoul National University. The author has contributed to research in topics: Fault detection and isolation & Fault (power engineering). The author has an hindex of 7, co-authored 14 publications receiving 122 citations.

Toothwise Fault Identification for a Planetary Gearbox Based on a Health Data Map

Abstract: Vibration-based fault diagnosis of a planetary gearbox is challenging due to the revolving planet gears inducing modulation of vibration signals. For accurate fault diagnosis, researchers have suggested that vibration signals should be extracted by window function when the planet gears are positioned under the sensor. However, vibration modulation characteristics can be affected by operational and geometrical uncertainties. Thus, fault-related features can be inadvertently discarded when the window function is employed. Alternatively, periodicity analysis of anomalies can be employed with the entire vibration signal without the use of window function. However, it is challenging in a planetary gearbox because the fault-related features are also modulated. This paper proposes a health data map for toothwise fault identification in a planetary gearbox. In the proposed approach, samplewise health data are aligned in the domains of a pair of gear teeth of the planetary gearbox. The proposed approach represents the health state of every pair of gear teeth, while making it possible to isolate the location of any faulty teeth in the gears. For demonstration of the proposed method, two case studies are presented: an analytical model and a 2-kW testbed. The results suggest that the proposed method performs well even under unexpected vibration modulation characteristics.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Challenges to IoT-Enabled Predictive Maintenance for Industry 4.0

Abstract: The Industry 4.0 paradigm is boosting the relevance of predictive maintenance (PdM) for manufacturing and production industries. PdM strongly relies on Internet of Things (IoT), which digitalizes the physical actions allowing human-to-human, human-to-machine, and machine-to-machine connections for intelligent perception. Several issues still need to be addressed for reaching the maturity stage for the widespread application of PdM. To do this, IoT needs to be empowered with data science capabilities, to reach the ultimate objective of digitalization, which is supporting decision making to optimally act on the physical systems. In this article, we present a comprehensive outlook of the current PdM issues, with the final aim of providing a deeper understanding of the limitations and strengths, challenges and opportunities of this dynamic maintenance paradigm. This is done through extensive research and analysis of the scientific and technical literature. On this basis, this article outlines some main research issues to be addressed for the successful development and deployment of IoT-enabled PdM in industry.

A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning

Abstract: The objective of this paper is to present a comprehensive review of the contemporary techniques for fault detection, diagnosis, and prognosis of rolling element bearings (REBs). Data-driven approaches, as opposed to model-based approaches, are gaining in popularity due to the availability of low-cost sensors and big data. This paper first reviews the fundamentals of prognostics and health management (PHM) techniques for REBs. A brief description of the different bearing-failure modes is given, then, the paper presents a comprehensive representation of the different health features (indexes, criteria) used for REB fault diagnostics and prognostics. Thus, the paper provides an overall platform for researchers, system engineers, and experts to select and adopt the best fit for their applications. Second, the paper provides overviews of contemporary REB PHM techniques with a specific focus on modern artificial intelligence (AI) techniques (i.e., shallow learning algorithms). Finally, deep-learning approaches for fault detection, diagnosis, and prognosis for REB are comprehensively reviewed.