Operating Conditions Research Articles

Defect Trends in Fire Alarm Systems: A Basis for Risk-Based Inspection (RBI) Approaches

This article presents a comprehensive statistical evaluation of defect frequency in fire alarm systems under real operating conditions, focusing on risk-based factors. The aim is not to introduce a complete RBI approach but rather to assess defect trends that can inform future RBI-based inspection strategies. The study categorizes and evaluates defects by frequency, particularly examining components such as cable and wire systems, acoustic signal devices, and the impact of detector contamination. These findings establish a foundation for developing tailored risk-based inspection and predictive maintenance strategies. A three-stage explanatory research design was employed, analyzing 4629 inspection reports with findings verified through expert surveys and cross-sample analysis. Results indicate that certain components, including acoustic devices and detectors, exhibit a significant increase in defects after 10 years, especially under challenging environmental conditions. Additionally, while ring bus technology supports less frequent functional testing, cable and wire systems require heightened attention in the early operational years. The study also identifies statistically significant trends and their potential for application to a broader system population, supporting enhanced RBI-based maintenance practices. These insights contribute to refining current maintenance approaches and offer practical recommendations for optimizing inspection routines based on risk factors. The article does not propose a system overhaul but lays essential groundwork for further research and improvement in fire alarm system reliability through targeted, risk-informed practices.

Development and Assessment of a Miniaturized Test Rig for Evaluating Noise Reduction in Serrated Blades Under Turbulent Flow Conditions

The implementation of serrated stator blades in axial compressor and fan stages offers significant advantages, such as enhanced performance and reduced noise levels, making it a practical and cost-effective solution. This study explores the impact of serrated blade design on noise reduction under specific engine operating conditions. A small-scale experimental test setup with a turbulence-inducing grid was designed for testing multiple grid sizes in order to identify the most promising configuration which replicates rotor–stator interaction. Numerical simulations and early experimental tests in an anechoic chamber using a four-blade cascade configuration at an airflow speed of 50 m/s revealed a small but notable noise reduction in the 1–6 kHz range for a partially matched grid–blade geometry. Serrated blades demonstrated an overall sound pressure level reduction of 1.5 dB and up to 12 dB in tonal noise, highlighting the potential of cascade configurations to improve acoustic performance in gas turbine applications.

Enhancing Electric Vehicle Propulsion: PMSM Motor Speed Control via a Fuzzy PI Controller Developed Using a Line-Integral Lyapunov Fuzzy Function

This paper suggests a robust control strategy to enhance the stability and ensure optimal tracking of reference speed for electric vehicles (EVs) equipped with Permanent Magnet Synchronous Motors (PMSM). The strategy involves designing a specific Proportional-Integral (PI) controller using the Takagi-Sugeno (TS) fuzzy model and employing the Linear Integral Lyapunov Function (LILF). Additionally, an H∞ approach is integrated to effectively manage external disturbances such as load torque. The PI controller's gains are determined using the Linear Matrix Inequality (LMI) tool, ensuring efficient and reliable performance under varying operating conditions. Simulation studies confirm the effectiveness of the proposed control strategy and highlighting its capability to achieve robust speed regulation and disturbance rejection in EV propulsion systems

Methods and equipment for analysis and diagnosis of marine engines during navigation

ABSTRACT Expert diagnostic systems are required for the development of intelligent marine engines and other ship systems. Measurement and analysis of marine engine parameters are fundamental in ensuring the efficient operation of these key propulsion systems. This paper introduces a novel measurement device and method capable of real-time monitoring of cylinder pressure and emissions in marine engines. The experimental procedure involved measuring critical engine parameters and emissions, such as NOx, CO2, and particulate matter, under various operating conditions. The device provided accurate real-time data acquisition, enabling detailed analysis of combustion processes and emission profiles under various operating conditions. Furthermore, a numerical model was developed, like digital twins, which utilized the measured data to simulate engine performance, predict potential failures, and optimize operational parameters to enhance efficiency and reduce environmental impact. The two most important parameters: the indicated cylinder pressure and NOx emissions were simulated and validated. Measured NOx was measured 889 ppm at 90% and simulated results was 985 ppm, which is 10% difference. Specific fuel consumption has less than 1% difference between measured and simulated data. Novelty of this work is the methodology used in the testing and calibration processes. Current actual parameters of engine operation are monitored, based on which a comparative analysis is made in real time. Previous diagnostic control systems did not provide diagnostics, comparative analysis and optimization in real time. The integration with digital twin allowed potential wear and tear predictions, fuel consumption optimization, and emission reduction strategies, showcasing the potential of this technology to improve marine engine diagnostics and maintenance. The device and its virtual counterpart worked well together to provide a complete understanding of the engine’s response. This facilitates proactive maintenance strategies and contributes to eco-efficiency in the maritime sector.

Experimental Investigation of Heat Pipe Flow Dynamics and Performance

Due to their safety, efficiency, and passive operation, heat pipes have found diverse applications that include nuclear microreactors. Heat pipes enable increased reliability in microreactors, as they eliminate the need for reactor coolant pumps and their associated auxiliary systems while resulting in a greatly reduced spatial footprint. Experimental work is needed to support and expedite the design and licensing of heat pipe microreactors, especially the validation of heat pipe performance, as key heat transfer components. The present work develops a comprehensive heat pipe experimental database covering a wide range of heat pipe operating conditions. In addition, two-phase thermosyphon experiments are conducted to serve as a benchmark for performance. The operating conditions are determined based on previously developed scaling laws for heat pipes and two-phase thermosyphons using low-temperature working fluids. The tested heat pipe is about 2 m long and equipped with in-house-developed annulus screen wicks. To allow for the investigation of heat pipe flow dynamics, various instruments are incorporated to acquire heat pipe pressures, pressure drops, and temperatures. In particular, a fiberoptic sensor is implemented to measure temperatures along the centerline of the entire heat pipe. The results can be directly applied to the advancement of numerical tools currently under development for heat pipe microreactor analysis.

A Hierarchical Planning Method for AUV Search Tasks Based on the Snake Optimization Algorithm.

In a complex and dynamic battlefield environment, enabling autonomous underwater vehicles (AUVs) to reach dynamic targets in the shortest possible time using global autonomous planning is a key issue affecting the completion of search tasks. In this study, ahierarchicalAUV task planning method that uses a combination of hierarchical programming and a snake optimization algorithm is proposed for two typical cases where the platform can provide initial target information. This method decomposes the search task problem into a three-level programming problem, with the outer task planning goal of achieving the shortest encounter time between AUV and dynamic targets; the goal of task planning in the middle layer is to achieve the shortest actual navigation time for AUVs under different operating conditions; and the internal task planning is responsible for considering the comprehensive trajectory optimization under navigation constraints such as threat zone, path length, and path smoothness. The snake optimization algorithm was used for solving each layer of task planning. The feasibility of the proposed method was verified through simulation experiments of AUV search tasks under two types of initial target information conditions. The simulation results show that this method can achieve task planning for AUV searching for dynamic targets under various constraint conditions, optimize the encounter time between AUV and dynamic targets, and have strong engineering practical value. It has certain reference significance for task planning problems similar to underwater unmanned equipment.

An Underwater Methane Sensor Based on Laser Spectroscopy in a Hollow Core Optical Fiber.

Existing sensors for measuring dissolved methane in situ suffer from excessively slow response times or large size and complexity. The technology reported here realizes improvements by utilizing a hollow core optical fiber (HFC) as the detection cell in an underwater infrared laser spectrometer. The sensor operates by using a polymer membrane inlet to continuously extract dissolved gas from water. Once inside the sensor, the gas passes through an HCF, within which tunable diode laser spectroscopy is used to quantify methane. The use of an HCF for the optical cell enables advantages of sensitivity, selectivity, compactness, response time, and ease of integration. A submersible prototype has been developed, characterized in the laboratory, and tested in the ocean to a depth of 2000 m. Initial laboratory environmental testing showed a pCH4 detection range up to 10,000 μatm, an uncertainty of 5.6 μatm or ±1.4% (whichever is greater) and a response time of 4.6 min over a range of controlled operating conditions. Operation at sea demonstrated its utility in generating dissolved methane maps, targeted point sampling, and water column profiling.

In-Device Ballistic-Electron-Emission Spectroscopy for Accurately In Situ Mapping Energy Level Alignment at Metal-Organic Semiconductors Interface.

Energy level alignment at metal/organic semiconductors (OSCs) interface governs electronic processes in organic electronics devices, making its precise determination essential for understanding carrier transport behaviors and optimizing device performance. However, it is proven that accurately characterizing the energy barrier at metal/OSC interface under operational conditions remains challenging due to the technical limitations of traditional methods. Herein, through integrating highly-improved device constructions with an ingenious derivative-assisted data processing method, this study demonstrates an in-device ballistic-electron-emission spectroscopy using hot-electron transistors to accurately characterize the energy barrier at metal/OSC interface under in-operando conditions. This technique is found that a remarkable improvement in measurement accuracy, reaching up to ±0.03eV, can be achieved-surpassing previous techniques (±0.1-0.2eV). The high accuracy allows us to monitor subtle changes in energy barriers at metal/OSC interface caused by variations in the aggregation state of OSCs, a phenomenon that is theoretically possible but failed to be directly demonstrated through conventional methods. Moreover, this study makes demonstration that this technology is universally applicable to various metal/OSC interfaces consisting of electron-transporting, hole-transporting, and ambipolar OSCs. These findings manifest the great potential of this method to advance both theoretical exploration and technical applications in organic electronics.

Effect of Geometric Parameters of High-Speed Helical Gears on Friction Flash Temperature and Scuffing Load Capacity in Electric Vehicles

High-speed reducers in electric vehicles, characterized by high rotation speeds, heavy loads, large helix angles, and high contact ratios, are prone to tooth surface scuffing due to high sliding speeds. This scuffing is caused by adhesion wear from excessive instantaneous friction flash temperatures. The prevailing approach to gear scuffing analysis relies on the standard formula method, which is a relatively rudimentary technique. This method lacks the precision required to accurately assess the intricate distribution of tooth surface flash temperature (TSFT), limiting its efficacy in targeted tooth optimization. This study introduces an enhanced semi-analytical method to calculate TSFT and analyzes its variation under different conditions: increased tooth number and reduced module, altered pressure angle, and varied helix angle. The aim is to understand how these geometric parameters affect TSFT and the scuffing load capacity of high-speed reducer gears. This study calculates load distribution and TSFT under peak operating conditions and shows that increasing the tooth number, pressure angle, and helix angle can reduce maximum TSFT by more than 30%, improving scuffing safety and load capacity. However, these improvements must consider the gear’s allowable bending safety factor and bearing service life. The research concludes that optimizing these geometric parameters can significantly enhance the scuffing load capacity of gearsets.

A bifunctional catalyst for direct CO2 conversion to clean fuels: Mechanistic insights and a comprehensive kinetic model

The escalating global concern over CO2 emissions has spurred extensive research aimed at developing innovative solutions for capturing, storing, and utilizing CO2, crucial for establishing a closed carbon loop. Thermo-catalytic CO2 hydrogenation stands out as a promising approach, though challenged by CO2's high stability, hindering the production of heavy liquid hydrocarbons. This study explores the design and performance of a bifunctional cobalt-based catalyst, promoted by Ru and supported by multiple shells of carbon, mesoporous silica, and ceria for CO2 hydrogenation in the Modified Fischer-Tropsch Synthesis (MFTS) route. Through meticulous characterization and evaluation, the catalyst demonstrates suitable textural properties, reducibility, and dispersion of active sites, promoting CO2 conversion and selectivity towards heavier hydrocarbons, highlighting the significance of catalyst design and operating conditions. The catalyst exhibits notable stability across catalyst deactivation, attributed to its thermal conductivity provided by SiC matrices. SiC-supported catalysts play a pivotal role in enhancing the efficiency, selectivity, and stability of CO2 hydrogenation catalysts. Moreover, in this study, through meticulous evaluation of elementary reactions based on molecular dynamic (MD) computations, a detailed mechanism for MFTS is presented. Key to this mechanism is the H-assisted CO2 dissociation pathway, supported by computational analysis. The pathway involves sequential reactions starting from CO2 adsorption on catalyst sites, followed by successive transformations leading to the formation of hydrocarbon building blocks. Ultimately, a developed MFTS kinetic model based on the MD-evaluated mechanism, which accurately predicts product selectivity across various operational conditions, indicating its robustness and reliability, is presented.

Analysis of factors influencing Y-ring sealing under ultra-high-pressure, high-temperature and low-temperature conditions

Abstract To study the factors affecting the sealing performance of a Y-shaped sealing ring at the stem of a 140-MPa cage throttle valve under -46°25C and 180°C operating conditions, uniaxial tensile tests were conducted on polytetrafluoroethylene (PTFE) and its modified materials. Finite element simulation software was then used to perform a simulation study of the Y-shaped sealing ring. The finite element simulation software was used to perform a sensitivity analysis on the five most critical parameters of the Y-shaped sealing ring and to perform dimensional optimization by using the multi-island genetic algorithm. The results show that compared with 25°C, under a 180°C condition, the contact stress at both ends of the sealing lip decreases when the valve stem moves upward . Under the condition of -46°C, the contact stress at the sealing lip increases when the valve stem goes down, the contact stress at both ends of the sealing lip decreases when the valve stem goes up; and the impact of the stem movement speed on the maximum value of the Y-shaped sealing ring contact stress is small.The design dimensions that have the greatest influence on the maximum von Mises stress and the maximum contact stress of the inner lip of the Y-seal during stem movement are the width and the tip height of the lip, respectively. The optimum values of the design variables are determined and the feasibility of the theoretical design is verified by testing.

Electronic Structure Modulating of W18O49 Nanospheres by Niobium Doping for Efficient Hydrogen Evolution Reaction.

Hydrogen, known for its high energy density and environmental benefits, serves as a prime substitute for fossil fuels. Nonetheless, the hydrogen evolution reaction (HER), essential in electrolysis, encounters challenges with slow kinetics and significant overpotential, which elevate costs and reduce efficiency. Thus, developing efficient electrocatalysts to reduce HER overpotential is vital to enhance hydrogen production efficiency and minimize energy consumption. Adjusting the electronic structure of transition metal oxides via elemental doping is a potent strategy to improve the effectiveness of electrocatalysts for hydrogen evolution. In this work, we synthesized a set of niobium-doped tungsten oxides (Nbx-W18O49) under anoxic conditions using a straightforward "one-pot" solvothermal approach. After doping Nb, the oxygen vacancy content inside W18O49 was increased, which induced a synergistic effect with the active sites of tungsten. In acidic environments, the hydrogen evolution activity of the Nb0.6-W18O49 electrocatalyst is second only by 20 wt % Pt/C. It attains a current density of -10 mA cm-2 at an overpotential of 102 mV. By comparison with W18O49, Nb0.4-W18O49 and Nb0.5-W18O49, Nb0.6-W18O49 demonstrates a reduced charge transfer resistance, which significantly enhances its conductivity and the speed of electron movement across interfaces. Coupled with this feature are notably faster HER kinetics. Additionally, it exhibits excellent stability, meaning it maintains its performance and structural integrity over prolonged periods and under various operational conditions. This article provides a new perspective for discovering inexpensive and efficient hydrogen evolution electrocatalyst materials.

Thermal stability and coalescence dynamics of exsolved metal nanoparticles at charged perovskite surfaces

Exsolution reactions enable the synthesis of oxide-supported metal nanoparticles, which are desirable as catalysts in green energy conversion technologies. It is crucial to precisely tailor the nanoparticle characteristics to optimize the catalysts’ functionality, and to maintain the catalytic performance under operation conditions. We use chemical (co)-doping to modify the defect chemistry of exsolution-active perovskite oxides and examine its influence on the mass transfer kinetics of Ni dopants towards the oxide surface and on the subsequent coalescence behavior of the exsolved nanoparticles during a continuous thermal reduction treatment. Nanoparticles that exsolve at the surface of the acceptor-type fast-oxygen-ion-conductor SrTi0.95Ni0.05O3−δ (STNi) show a high surface mobility leading to a very low thermal stability compared to nanoparticles that exsolve at the surface of donor-type SrTi0.9Nb0.05Ni0.05O3−δ (STNNi). Our analysis indicates that the low thermal stability of exsolved nanoparticles at the acceptor-doped perovskite surface is linked to a high oxygen vacancy concentration at the nanoparticle-oxide interface. For catalysts that require fast oxygen exchange kinetics, exsolution synthesis routes in dry hydrogen conditions may hence lead to accelerated degradation, while humid reaction conditions may mitigate this failure mechanism.

Revisiting the origin of enhanced C2+ selectivity on oxide-derived copper toward CO2 electroreduction

Oxide-derived copper (OD-Cu) exhibits remarkable efficiency toward selective electrochemical reduction of CO2 to high-energy-density multicarbon (C2+) products. The big challenge, however, lies in accurately discerning the actual chemical state of copper in OD-Cu that enhances C2+ selectivity, which are largely dictated by the intricating dynamic structural evolution of copper under operating conditions. Herein, we report the construction of a core-shell structured Cu wire model catalyst with a pristine metallic Cu core and an OD-Cu shell using a flame-quenching approach. The cross-section of this model catalyst, merging the surfaces of both pristine Cu and OD-Cu, serves as an ideal platform for probing the structural evolution and discerning the distinct roles of metallic and oxidative Cu in CO2 electroreduction. Through a combination of in situ and ex situ techniques, we unambiguously identify the presence of Cu+ and Cu0 within OD-Cu under electrochemical operating conditions. Furthermore, the cross-examination of theoretical and experimental infrared spectroscopic data unravels that the enhanced C2+ selectivity over the OD-Cu shell primarily stemmed from the tandem coupling of Cu0 with Cu+, which improves the kinetics of C-C coupling. These findings not only offer refreshing perspective on the chemical state of OD-Cu but also provide new guidelines for designing and constructing new high-performance electrocatalysts for CO2 reduction.

Research on temperature prediction model of molten steel of tundish in continuous casting

To achieve the desired superheat of molten steel during the continuous casting process, optimization of process parameters such as molten steel temperature in ladle furnace, casting speed, and baking temperature is necessary. Therefore, obtaining the superheat corresponding to these process parameters in advance is particularly important. To address this issue, a model for predicting the temperature of molten steel in the tundish during continuous casting is designed. The model adopts a combined modeling approach of mechanistic model and data model. To address the issue of the mechanism model’s inability to capture the variation of the lining’s thermal parameters, this article improves the traditional physics-informed neural network (PINN) algorithm. It combines the constraints from both the forward and inverse problems, allowing for obtaining solutions to the equations while capturing the variation of equation parameters. Actual data from multiple casting sequences at a steel plant are collected to validate the accuracy and interpretability of the model. The results show that the error of the model is about 2.1k which has better accuracy compared to pure mechanistic model and pure data model. Additionally, it can capture the variation patterns of tundish lining thermal parameters under different operating conditions. Therefore, the model designed in this article can provide both profound physical interpretation ability and more practical predictions of molten steel temperature.

Whistling potential of throttling orifices and its connection to liquid rocket combustion instability

Orifice whistling is recently established as a cause of high frequency combustion instability in liquid rocket engines. The series of studies on the experimental thrust chamber, known as BKD, developed at the German Aerospace Center (DLR), were instrumental in identifying this phenomenon. BKD encountered an injector-driven thermo-acoustic instability caused by the self-excitation of the LOX injector. The throttle orifice in the LOX injector plays a vital role in the self-excitation through whistling. In this work, the impedance of the BKD throttling orifice is characterized with the motive to find its whistling range. The CFD approach based on solving the unsteady Reynolds Averaged Navier Stokes equations proposed by Lacombe et al. [1] is used for this characterization. We characterize the orifice at the weak and the strong coupling conditions reported in the literature. The orifice impedance is calculated in the range of 4 kHz to 15 kHz, covering the entire potential coupling range of LOX injector eigen modes. The impedance results show that the orifice character follows the whistling behavior of the “medium” orifices. The resistance turns negative in the Strouhal number range of St=0.89 - 1.2 for both the weak and strong operating conditions. When plotted against the frequency, the resistance at the strong coupling condition occurs close to the second longitudinal (2L) mode of the LOX injector, suggesting direct excitation of the injector through the orifice whistling. The coexistence of the 2L mode and the first transverse (1T) mode of the combustion chamber at the same frequency leads to combustion instability.

An Optimized Strategy for the Integration of Photovoltaic Systems and Electric Vehicles into the Real Distribution Grid

The increasing spread of photovoltaic systems for private households (PVs) and electric vehicles (EVs) in order to reduce carbon emissions significantly impacts operation conditions in existing distribution networks. Variable and unpredictable PVs can stress distribution network operation, mainly manifested in voltage violations during the day. On the other hand, variable loads such as EV chargers which have battery storage in their configuration have the ability of storying a surplus energy and, if it is necessary, support a distribution network with energy, commonly known as vehicle-to-grid concept (V2G), to help voltage stability network enhancement. This paper proposes an optimal power flow (OPF)-based model for EV charging to minimize power exchange between the superior-10 kV grid and the observed distribution feeder. The optimization procedure is realized using the co-simulation approach that connects power flow analysis software and optimization method. Three different scenarios are observed and analysed. The first scenario is referred to as a base case without optimization. The second and third scenarios include optimal EV charging and discharging patterns under different constraints. To test the optimization model, a 90-bus unbalanced distribution feeder modelled based on real-life examples is used. The obtained results suggest that this optimization model does not only significantly reduce the power exchange between an external network and the distribution feeder but also improves voltage stability and demand curve in the distribution feeder.

Rapid Optimization of Active Disturbance Rejection Controller Parameters for Quadrotor UAVs Using Kriging Surrogate Modeling

The Active Disturbance Rejection Controller (ADRC), celebrated for its superior resistance to interference, presents itself as an exemplary solution for the development of control systems that are designed to accommodate substantial changes in payload weight for quadrotor Unmanned Aerial Vehicles (UAVs) and to endure robust side winds along with other challenging operational scenarios. Despite the inherent complexity due to the numerous parameters required for the configuration of the ADRC, an innovative method utilizing the Kriging surrogate optimization algorithm has been introduced to automate and expedite the generation of these controller parameters. The development of the ADRC begins with the dynamics model of the quadrotor UAV, followed by the identification of key design parameters. These parameters are then rapidly optimized through the Kriging surrogate optimization algorithm. The controller’s effectiveness is confirmed by implementing the ADRC on Pixhawk flight control hardware, with a comparative analysis of the attitude response under various operating conditions, thereby validating the ADRC’s superior anti-disturbance performance.

A novel behavioral apparatus for spontaneous exploration and operant conditioning of social information under spatial conditions in rats.

Evidence from the fields of evolutionary biology and neuroscience supports the theory that spatial cognition and social cognition share neural mechanisms. Rodent models are widely used to study either spatial or social cognition, but few studies have explored the interactions between the spatial and social cognitive domains due to the lack of appropriate paradigms. Our study introduces the Vertical Maze (VM), a novel behavioral apparatus designed to measure multiple aspects of spatial and social cognition. The VM features a standard 3-chamber maze positioned above multilevel columns allowing for the presentation of conspecifics at varying spatial distances and familiarity levels. This arrangement enables conspecifics to serve as discriminative stimuli for both social and spatial spontaneous and goal-oriented assessments. The three-dimensional design of the VM allows rats to use multisensory cues to judge distance, direction, and social identity of conspecifics. In the present study, we found that rats can 1) discriminate the spatial distance of conspecifics located below them in an operant conditioning task, and 2) discriminate social novelty when conspecifics are presented below at the near, middle, and far distances in a spontaneous exploration task. Critically, it was necessary for rats to explore all levels of the maze to perform these discriminations. This new method advances the field by permitting the presentation of social information (conspecifics) at different spatial distances. The use of conspecifics to serve as stimuli for both social and spatial discriminations allows more direct comparison of behavioral measures across these information domains. Importantly, the presentation of conspecifics as stimuli below the 3-chamber level of the maze engages auditory, visual, and olfactory systems, encouraging a robust multisensory representation of conspecifics presented at a distance. Our results confirm that the VM is an effective tool for studying both spatial and social cognition, facilitating the development of novel automated tasks in these areas. This new method opens new avenues for investigating the neural and cognitive foundations of spatial and social behavior, as well as for exploring the possibility of shared mechanisms across these cognitive domains.

Improving unsupervised long-term damage detection in an uncontrolled environment through noise-augmentation strategy

Autoencoder reconstruction-based unsupervised damage detection is widely utilized in structural health monitoring. However, such methods typically necessitate a comprehensive collection of historical guided waves as training data. Acquiring such data presents challenges, as it requires prolonged monitoring to cover various environmental and operational conditions (EOCs), making these methods less practical for real-world applications. This paper proposes an unsupervised damage detection method solely trained on the current measurements directly. To improve the performance of the unsupervised damage detection method when the training data (the current measurements ) contains a large ratio of damage-induced guided waves, two noise-augmentation strategies are designed to limit the neural network’s learning ability to recover damage-induced guided waves from their segments, improving detection performance. Additionally, we use t-SNE to visualize the impact of noise augmentation on the separation of different types of guided waves within the recovery network’s latent space. Experimental results indicate that input signals with relatively low SNR can achieve better damage detection performance, and a strategy for estimating the optimal noise intensity in input signals is provided in this paper. The effectiveness of the unsupervised this damage detection method with noise-augmentation strategy is validated by 10 regions of 80-days guided waves collected from uncontrolled and dynamic environmental conditions.