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1. Harnessing Bayesian Deep Learning to Tackle Unseen and Uncertain Scenarios in Diagnosis of Machinery Systems

   https://doi.org/10.1115/1.4067001  

   Kai, Zhou; Zhou, Qianyu; Tang, Jiong (March 2025, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering)

   Abstract Direct inverse analysis of faults in machinery systems such as gears using first principle is intrinsically difficult, owing to the multiple time- and length-scales involved in vibration modeling. As such, data-driven approaches have been the mainstream, whereas supervised trainings are deemed effective. Nevertheless, existing techniques often fall short in their ability to generalize from discrete data labels to the continuous spectrum of possible faults, which is further compounded by various uncertainties. This research proposes an interpretability-enhanced deep learning framework that incorporates Bayesian principles, effectively transforming convolutional neural networks (CNNs) into dynamic predictive models and significantly amplifying their generalizability with more accessible insights of the model's reasoning processes. Our approach is distinguished by a novel implementation of Bayesian inference, enabling the navigation of the probabilistic nuances of gear fault severities. By integrating variational inference into the deep learning architecture, we present a methodology that excels in leveraging limited data labels to reveal insights into both observed and unobserved fault conditions. This approach improves the model's capacity for uncertainty estimation and probabilistic generalization. Experimental validation on a lab-scale gear setup demonstrated the framework's superior performance, achieving nearly 100% accuracy in classifying known fault conditions, even in the presence of significant noise, and maintaining 96.15% accuracy when dealing with unseen fault severities. These results underscore the method's capability in discovering implicit relations between known and unseen faults, facilitating extended fault diagnosis, and effectively managing large degrees of measurement uncertainties. 

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2. A Deep Long Short-Term Memory Network for Bearing Fault Diagnosis Under Time-Varying Conditions

   https://doi.org/10.1115/DETC2022-88808  

   Zhou, Kai (August 2022, ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Abstract The fault diagnosis of bearing in machinery system plays a vital role in ensuring the normal operating performance of system. Machine learning-based fault diagnosis using vibration measurement recently has become a prevailing approach, which aims at identifying the fault through exploring the correlation between the measurement and respective fault. Nevertheless, such correlation will become very complex for the practical scenario where the system is operated under time-varying conditions. To fulfill the reliable bearing fault diagnosis under time-varying condition, this study presents a tailored deep learning model, so called deep long short-term memory (LSTM) network. By fully exploiting the strength of this model in characterizing the temporal dependence of time-series vibration measurement, the negative consequence of time-varying conditions can be minimized, thereby improving the diagnosis performance. The published bearing dataset with various time-varying operating speeds is utilized in case illustrations to validate the effectiveness of proposed methodology. 

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3. The Design and Manufacture of a Gear-Coupled Serial Chain to Trace the Butterfly Curve

   https://doi.org/10.1115/DETC2017-68388  

   Liu, Yang; Wang, Peter Lee-Shien; McCarthy, J. Michael (August 2017, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   This paper presents a design and manufacturing methodology for a mechanical system that draws trigonometric curves assembled from a series of links connected by gears. We demonstrate this technique using 11 gear-coupled links that trace a butterfly curve. The equation of a butterfly curve is converted to the relative rotations of the links of a coupled serial chain assembled so it operates with one input. We present a procedure to determine the adjustments to the gear ratios and link dimensions necessary for practical manufacture of the mechanism. The results are demonstrated by a working prototype. 

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4. Study on Dynamic Behaviors of Hypoid Gears Under Variable Tidal Current Energy Harvesting Conditions

   https://doi.org/10.3390/machines13030178  

   Huang, Dequan; Li, Yan; Zheng, Xingyuan; Li, Gang (March 2025, Machines)

   This study investigates dynamic behaviors of hypoid gear rotor systems under variable tidal current energy harvesting conditions through numerical simulations and experimental validation. The study examines dynamic responses of a hypoid gear rotor system induced by cyclical tidal current variations, which generate fluctuating loads and bidirectional rotational speeds in tidal energy conversion systems. Two hypoid gear pairs, modified through precise manufacturing parameters, are evaluated to optimize tooth contact patterns for bidirectional tidal loading conditions. A coupled torsional vibration model is developed, incorporating variable transmission error and mesh stiffness. Experimental validation of dynamic performances of hypoid gear pairs was conducted on a bevel gear testing rig, which can measure both torsional and translational vibrations across diverse tidal speed profiles. The experimental results demonstrate that second-order primary resonances exhibit heightened vibration intensity during flow-reversal phases. This phenomenon has significant implications for system power efficiency and acoustic emissions. The findings extend the current understanding of hypoid gear optimization for tidal energy-harvesting applications. 

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5. Development of a speed invariant deep learning model with application to condition monitoring of rotating machinery

   Lee, Wo Jae (October 2021, Journal of intelligent manufacturing)

   The application of cutting-edge technologies such as AI, smart sensors, and IoT in factories is revolutionizing the manufacturing industry. This emerging trend, so called smart manufacturing, is a collection of various technologies that support decision-making in real-time in the presence of changing conditions in manufacturing activities; this may advance manufacturing competitiveness and sustainability. As a factory becomes highly automated, physical asset management comes to be a critical part of an operational life-cycle. Maintenance is one area where the collection of technologies may be applied to enhance operational reliability using a machine condition monitoring system. Data-driven models have been extensively applied to machine condition data to build a fault detection system. Most existing studies on fault detection were developed under a fixed set of operating conditions and tested with data obtained from that set of conditions. Therefore, variability in a model’s performance from data obtained from different operating settings is not well reported. There have been limited studies considering changing operational conditions in a data-driven model. For practical applications, a model must identify a targeted fault under variable operational conditions. With this in mind, the goal of this paper is to study invariance of model to changing speed via a deep learning method, which can detect a mechanical imbalance, i.e., targeted fault, under varying speed settings. To study the speed invariance, experimental data obtained from a motor test-bed are processed, and time-series data and time–frequency data are applied to long short-term memory and convolutional neural network, respectively, to evaluate their performance. 

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