This paper investigates the application of one-dimensional convolutional neural networks (1D CNNs) for damage detection in standardized structural components, addressing the limitations of existing methods that typically focus on individual structures. To assess damage detection capabilities across populations, particularly in situations where no prior damage data from the structures are available

Deep learning methods for damage detection heavily depend on large labeled datasets, which are often lacking in actual structures. While numerical models can effectively simulate damage and generate labeled datasets, environmental uncertainties and modeling errors create a significant gap between these between the simulated data and the actual observations. Furthermore, the damage scenarios in actual

The intershaft bearing, a critical component of an aero-engine, is susceptible to failure. The restricted accessibility of labeled fault data across varying working conditions and fault types, poses a challenge to its intelligent diagnosis, which is referred to as ’missing training samples’. This issue requires methods to handle both domain generalization and open-set domain generalization, adding

Laser Doppler vibrometry (LDV) offers an attractive solution when radial vibration measurement directly from a rotor surface is required. Research has demonstrated application on rough rotors (typically coated with retro-reflective tape) but a significant cross-sensitivity to the orthogonal radial vibration component occurs and post-processing is essential to resolve individual radial vibration components

The placement of a Stockbridge damper within a powerline conductor is critical for its effectiveness in mitigating aeolian vibrations. This paper investigates the optimal positioning of Stockbridge dampers under varying operating conditions using the finite element method (FEM), incorporating both modal and harmonic response analyses. We compare the performance of two commercially available Stockbridge

The (pseudo-) cyclostationarity-based spectral analysis tools can be generated by arithmetic averaging or weighted averaging of spectral correlation/coherence for rotating machinery fault diagnosis. However, conventional arithmetic or weighted averaging can hardly adequately eliminate broadband interfering noise under harsh operating conditions, thus compromising fault diagnosis capability. To address

Accurate full-field vibration measurement through computer vision is crucial for dynamic analysis and structural health monitoring, providing intuitive insights into dynamic behaviour. Recently, phase-based motion estimation (PME) has advanced rapidly due to its high spatial sensing capability and sub-pixel accuracy. However, inappropriate filter parameters can hinder accurate vibration estimation

Research on triboelectric energy harvesting has advanced significantly in the last several years. Simultaneous harvesting and vibration control is now a new trend. This paper presents a novel triboelectric energy harvester (TEH), which consists of a cantilever beam with a rigid curved surface as a stop on either side whose geometric shape is described by a polynomial, inspired by a type of nonlinear

In the context of structural and industrial asset monitoring and twinning, the estimation of unknown inputs – typically reflecting the loads acting onto a system – stands as a critical factor in ensuring both the safety and performance of engineered systems. This research introduces a novel approach for inferring such unknown inputs from observed outputs, focusing particularly on dynamical systems

Autonomous electric vehicles (AEVs) are equipped with numerous advanced control systems that rely on measurements of longitudinal velocity, yaw rate, lateral speed, and sideslip angle. However, one of the main challenges is that mass-produced vehicles cannot accommodate overly expensive sensors. This paper proposes a novel hybrid physics-data driven observer (HPDD-Observer) for vehicle dynamics state

Pipeline leak detection technologies have been playing a crucial role in protecting the safety of pipeline delivery. However, there is a lack of effective leak detection solutions for high-density interwoven pipelines in high-value industrial equipment and facilities. To tackle this challenge, we have focused on developing a quartz resonant pressure transducer with high sensitivity and high resolution

Condition monitoring is crucial for the predictive maintenance of gear transmissions to avoid unexpected failures with downtimes, costly repairs, or dangerous incidents. While vibration-based condition monitoring is widespread in industry and research, using angle measurements as a primary source still offers much potential. This review provides insights into current methods of angle-based condition

Reciprocating machinery has compact and complex structures, many moving parts, and numerous vibration excitation sources. Impact signals caused by mechanical part faults can easily produce time–frequency coupling with multi-source impact signals from components normal movements. At the same time, variable operating conditions, such as different speed and load will lead to nonlinear changes in the time–frequency

Rotary vector reducer (RV reducer) serves as a crucial drive train component in industrial robots, and its time varying torsional stiffness significantly impacts the dynamic characteristics, vibration and transmission accuracy. Existing methods for calculating the torsional stiffness suffer from two limitations: (1) considering only the time varying meshing stiffness of the gear pair or the time varying

The presence of bearing defects significantly impacts the stable operation of rotating machinery, necessitating timely diagnosis of such faults. Understanding the fault mechanism serves as a theoretical foundation for fault diagnosis. As such, it is imperative to prioritize the study of vibration characteristics in rotor-casing systems affected by bearing faults. Given that rotor unbalance has the

Due to the complexities of compressors and the influence of varied operational factors, a gradual decline in their performance status is inevitable, ultimately leading to compressor stalls. Compressor stalls can inflict substantial damage, thus, it is imperative to detect anomalies promptly and issue early warnings as soon as initial signs of reduced performance or suboptimal operation become apparent

This paper provides the first statistically validated experimental demonstration of a novel characterization procedure based on fast frequency sweeps. A MEMS structure with a time constant of several seconds is characterized in linear regime under mechanical actuation and optical detection of its motion. 450 series of fast sweeps are analyzed with different sweep durations to evaluate the precision

At present, the tool remaining useful life prediction technology is important to the effectiveness of machining, because tool life prediction plays the role of safety maintenance, cost optimization and quality assurance. However, this the technology faces many challenges in practical applications. The main problems include that when the spatial distribution of data features is too different, the model

Deep learning has emerged as a formidable tool in bearing fault diagnosis, yet its effectiveness is often hampered by the opaqueness of feature interpretation and the scarcity of labeled data under varied industrial conditions. In response to these challenges, this paper introduces a mechanism-driven domain adaptation model with interpretability tailored for bearing fault diagnosis across various operating

Vision-based multi-target monitoring systems for bridge structures provide a comprehensive evaluation of structural safety. However, their application to field bridges has been constrained by challenges such as the trade-off between the field of view (FOV) and accuracy, as well as the impact of camera orientation and complex backgrounds on measurement effectiveness. This study introduces a robust monocular

Structural vibration control is an innovative technology designed to reduce vibrations of civil structures induced by external vibration sources such as earthquakes and wind. Unlike traditional methods that rely on structural strength and stiffness, this technology suppresses structural vibrations by employing energy dissipation devices. Over the past decades, various energy dissipation devices have

We apply fractional calculus, fuzzy logic, and nonlinear control to synthesize an adaptive control system for rubber tired gantry (RTG) cranes facing unknown winds, five uncertain parameters, and faults at all motors. Both dynamic model, nonlinear controller, and fuzzy estimator hold fractional derivatives. Due to considering the viscoelastic frictions, crane model is governed by fractional differential

Intelligent fault diagnosis has become increasingly relevant in predictive maintenance for rotating machinery. With advancements in data transmission and sensor technology, measurement systems can now gather vast amounts of data from multiple sensors. These multi-sensor datasets are multivariate time series with significant Spatial–temporal correlation. Utilizing this correlation to achieve accurate

Adaptive exploration and utilization of the correlations are the crucial factors in determining the performance of fusion based intelligent diagnosis methods. However, subject to the impact of harsh operating environments in industrial applications, collected multisource data are inevitably suffer from the challenge of incompleteness, directly put these correlations disabled incomplete multisource

Extensive simulations of complex computational models are typically required to acquire accurate prediction of structural responses under seismic loading. Traditional mechanics-based or phenomenological models however often struggle to capture the behavior of complex components. This study proposes integrating real-time hybrid simulation (RTHS) with deep gated recurrent units (GRU) network for accurate

The blurred concrete crack images are typically the result of unexpected camera motion in engineering. Consequently, deblurring and segmentation of them represents a challenging task due to the complexity of the issue. This paper presents a novel Deep Multi-stage Network (DMNet), integrated with a multi-stage feature fusion strategy, for the purpose of deblurring crack images. The proposed model is

The application of elastic supports and bearings has a great influence on the performance of rotor systems. However, the coupling effect between these two is ignored in the current research. The primary purpose of this work is to reveal the influence of the strong coupling effect between elastic supports and bearings on rotor systems. This is conducive to improving rotor systems and thereby improving

The thermal network is widely employed for real-time rotor temperature estimation, enhancing both control accuracy and functional safety of electric machines. However, the complex operating conditions of oil-cooled induction machines (OCIMs) in electric vehicles lead to significant variability in thermal parameters. Moreover, cost constraints often preclude using oil temperature sensors in OCIMs, resulting

During the manufacturing of the planet carrier, the pinhole and bushing will contain eccentric errors, which will cause misalignment of the pin and unwanted system dynamic response. The phase of the eccentricity error and the form of misalignment can be adjusted by changing the assembly angle of the bushing. Appropriate misalignment can improve the system’s dynamic performance. Based on the transmission

With the increasing diversity of spacecraft, the solar wing deployable mechanisms are gradually developing to be more lightweight. In this paper, a scissor-type flexible solar wing deployable mechanism is proposed. In particular, the geometric nonlinear large deformation problem after deployment locking is primarily investigated, and the structural properties such as static load deformation, spatial

Milling of flexible and thin workpieces is widely used in the industry, but traditional tool control is ineffective. The innovation of this paper is in realistic theoretical methods and explores chatter suppression in the milling of a large, low-frequency workpiece through the application of piezoelectric patches and a reinforcement learning-based controller, which has not been employed before. Due

Forecasting the dynamic response of in-service bridges is essential for real-time structural condition assessment and early abnormal detection. Current research efforts mainly focus on statistical learning for near-term forecasting (e.g., one-step ahead) of structural response, such as displacements and strains. However, there is a growing need for data-driven methods capable of long-range (e.g., multi-step

Vibration behavior is an important evaluation index in the friction and wear performance test of materials. However, the current method is not comprehensive enough in the data representation of vibration behavior, which easily leads to the loss of features. Therefore, this paper proposes a vibration behavior data representation method based on dual time–frequency analysis and multiple algorithms. Firstly

Localizing and identifying multiple sound sources typically requires numerous sensors and complex control hardware. Compressive sensing imaging can reduce the number of sensors to a single pixel, forming a zero-dimensional compressive sensing imaging system. However, due to its relatively low resolution and rough orientation judgment, rather than simultaneously azimuth-distance location, the zero-dimensional

An automatic seismic response monitoring method for building structures and equipment based on a single indoor surveillance camera is proposed in this study. The computational model for monitoring the seismic responses of structures and equipment using rigidly connected cameras is established. Combining computer vision technology and image processing methods, a seismic response monitoring process for

This study explores the application of the amplitude-modulated vibro-acoustic (AMVA) technique for in-situ monitoring of structures under compressive loads. The AMVA technique employs an amplitude-modulated pump wave combining two low frequencies, fL1 and fL2 (fL1

In ultrasonic reflection measurements of lubricant film thickness, an incident wave is needed as a reference signal and is typically calibrated under controlled ex-situ conditions. However, existing calibration methods have two limitations: 1) significant measurement errors are introduced due to the discrepancy between the calibration environment and the varying operational conditions (for example

Given the weak noise resistance and low identification efficiency of traditional bridge modal parameter identification methods, this study proposes a data-driven bridge modal parameter identification method based on synchronous clustering theory using the dynamic response of a moving, passing dual-axle vehicle. Firstly, the dual axle vehicle equipped with sensors on both axles traverses the bridge

The article presents a multiaxial fatigue criterion in the frequency domain. The criterion uses the power spectral density of the hydrostatic stress, the stress consistent with the von Mises criterion and determines the equivalent power spectral density as the sum of these quantities for further use in fatigue analyses. The methodology for deriving the criterion weights in two cases of determining

Interfacial debonding defects in rectangular steel–concrete composite structures (RSCCS) can significantly diminish the confinement effect of the steel on the concrete core and reduce load transfer efficiency, potentially impacting the overall performance of the structure. Detection of these defects in RSCCS is therefore considered critical. This study investigates the complexities of stress wave propagation

This paper explores the application of tri-stable energy harvesting technology in tire rotation and proposes an innovative approach of optimizing the output of energy harvester by adjusting the centrifugal distance of the magnet at the free end of a cantilever beam. By leveraging the tunable linear stiffness in the electromechanical coupling equations of the rotating tri-stable state, this study derives

This research introduces an innovative method for real-time monitoring thin film growth and surface roughness using a single mode optical fiber without any additional treatment. The cleaved end of the fiber was installed within the deposition chamber, allowing the thin film to be deposited directly onto the fiber tip. During the deposition process, a Fabry-Pérot interferometer was formed with its cavity

The existing reconstruction methods for dam monitoring data have the problems of being unable to reconstruct in the non-complete dataset and the reconstruction accuracy is not high enough. Therefore, this paper proposes the dam monitoring data reconstruction method (DSVD) to realize the accurate reconstruction of dam monitoring data in non-complete datasets. The method first adopts the sorting method

The aerostatic bearing-rotor system plays a critical role in ensuring the stable operation of high-speed, high-precision rotating machinery. Despite its importance, the system is often affected by nonlinear sub-synchronous vibration instabilities, which limit its performance and development. To address this issue, this study proposes a novel tangentially supplied (TS) bearing. Modified gas lubrication

Ultrasonic sensing, for instance for damage or fouling detection, is commonly carried out using rigid transducer collars, carefully placed for monitoring a well-defined local area of a structure. A distributed sensing network consisting of individually placed transducers offers significant opportunities for monitoring larger areas or more complex geometries. For analyzing the signals of such a distributed

Gear wear is an inevitable consequence of friction and load during operation. However, the nonlinear and non-stationary nature of the vibration signals under variable speed conditions and their complex interaction with gear wear make it extremely challenging to extract wear-related features from them. Since the gear wear area visually indicates the severity of wear under varying speed conditions, we

Enhancing anomaly detection performance is essential for effective gas turbine condition monitoring and health maintenance. However, in industrial applications, gas turbine operating conditions frequently change, and fault data are scarce or even unavailable. Therefore, identifying anomalies in unknown conditions with training based only on normal data is challenging. Inspired by human communication

Quasi-zero-stiffness (QZS) mechanism is widely exploited in tailoring vibration isolators for balancing the load capacity and low/ultra-low frequency isolation performance. For these isolators, broadening QZS region, improving manufacturability, and enhancing tunability are relentless design pursuits. We here propose a cutting-edge tensegrity-based quasi-zero-stiffness (TQZS) isolator for torsional

Digital hydraulic systems are composed of parallel fast switching valves (FSVs) and have unique fault tolerance characteristics, while fault identification and localization are the premise of fault tolerance. However, due to the similar fault features, it is difficult to accurately diagnose the faulty valve in an equal-coded digital hydraulic system (EDHS) without its additional sensors. Aiming at

Clamp-on ultrasonic flowmeters suffer from crosstalk—i.e., measurement errors due to the interference of signals generated in solid regions and solid–fluid interfaces with the required signal from the fluid. Although several approaches have been proposed to alleviate crosstalk, they only work in specific ranges of flow rates and pipe diameters, and some also introduce additional issues. We propose

Chatter detection is crucial for both micro- and macro-milling, as chatter can cause detrimental damage on machining process and machined surface. Compared to macro-milling, micro-milling is more susceptible to external non-Gaussian noise interference, making it extremely difficult to extract chatter features and identify chatter modes at the micrometer scale due to the lower chatter component which

It has been observed experimentally that flow-induced ambient noise propagated in an icy thin-plate structure decays quickly. The attenuation rate is sensitive to the ice thickness and is thus potentially an important feature for passive ice detection. The main goal of the present paper is to develop a theoretical model to explain this damping behavior qualitatively and quantitatively. The wave propagation

Aerostatic spindle utilizes gas as the lubricating and supporting medium, enabling it to exhibit outstanding characteristics such as high precision, low temperature rise, and environmental friendliness during operation, thereby fulfilling the requirements of high-speed machining applications. However, during the operation of the aerostatic spindle, the increase in spindle speed induces velocity effects

The quest for low-frequency and broadband underwater sound absorption materials with lightweight and pressure-resistant properties is constantly pursued in engineering applications. However, existing underwater absorbers are restricted by conventional design concepts and struggle to strike a balance among these requirements. Notably, the acoustic performance of these absorbers tends to deteriorate

Bayesian inference has been demonstrated as a rigorous tool for updating models and predicting responses in structural dynamics. Most often, the likelihood function within the Bayesian framework is formulated based on a point-to-point probabilistic description of the discrepancy between the measurements and model predictions. This description results in an underestimation of uncertainties due to the

Thin-sheet components are widely used in many industrial applications. Structural integrity of these components largely depends on the strength of the joints. Ensuring the durability of these joints is essential, and therefore, routine inspection is critical to maintain the safety and performance of these components. However, most traditional Non-Destructive Evaluation (NDE) techniques fall short for

Reliability analysis of dynamic structural systems and its implications for structural design have garnered increasing attention. Sample-based methods prove insensitive to the dimension of the probability integral. Nontheless, a substantial number of realizations is necessary for estimating small failure probabilities, resulting in time-consuming computations. Recently, the Directional Importance Sampling

In the last decade, the class of polynomial chaos (PC) methods for non-Gaussian random field modeling has received considerable attention. However, these methods have been limited to low random dimension problems due to the curse of dimensionality in the Rosenblatt transformation. In this paper, we develop an optimal transport method for the PC representation of non-Gaussian fields. Our method firstly

The nonlinear time-varying vibrations of the axially moving nested composite cantilever wing are investigated by using the theoretical and experimental methods when the axially moving wing deploys and retracts. Subjected to the in-plane force and first- order aerodynamic press, the axially moving nested composite cantilever wing is simplified to an axially moving cantilever composite stepped plate

The monitored seismic response data provides useful insight into the dynamic characteristics of cable-stayed bridges during an earthquake shaking. However, the unavailable earthquake input data, as is the case for most cable-stayed bridges, would prevent the system identification using the during-shaking structural response. This study proposes to employ the post-shaking structural response only for