Due to spatial constraints or transmission layout considerations, gear pairs are sometimes distributed non-uniformly. This non-uniform distribution can lead to a non-uniform load distribution along the tooth width direction, thereby increasing the risk of failures near the tooth edge. This paper introduces a novel model aimed at analyzing the meshing characteristics of non-uniformly distributed gear

Ultrasonic guided wave-based structural health monitoring (UGW-SHM) technology has a highly efficient damage detection capability. However, changes in environmental factors such as temperature can lead to changes in the signal waveform, which greatly affects the damage detection performance. In this paper, a novel unsupervised temperature compensated damage localization method called damage to baseline

The aim of this paper is to provide a feedback on the signal processing contest organized at the Survishno/Resonance conference held in 2023 in Toulouse, France. The aim of the competition was to demonstrate the possibility of diagnosing a bearing operating at a variable rotation speed using high-speed video data only. To this end, a video of an operating faulty bearing was proposed to registered people

Monitoring equipment conditions in industrial environments with variable operating conditions and complex failure modes presents significant challenges. Traditional health indicators (His) often focus on isolated failure types and fail to account for the competitive interactions between gradual wear and sudden failures. To address these challenges, this paper proposes a dependent competing failure-based

Micro-nano textured surfaces exhibit unique functional properties and therefore are extensively applied in the industrial fields such as healthcare and defense. Generally, ultra-precision machining is believed to be an ideal means for fabricating micro-nano textured, especially for vibration-assisted single-point diamond turning. However, vibration-assisted single-point diamond turning also presents

Blades serve as the primary elements for converting mechanical energy into electrical energy in wind turbines, making their operational parameters crucial for structural safety evaluation. The traditional monitoring methods for the rotating blades require labor-intensive attachment of artificial markers, and also face challenges in image background processing. This paper proposes a visual monitoring

Blade tip timing (BTT) is a promising vibration measurement technique for rotating blades owing to its non-contact nature, high efficiency, and long service life. However, due to the mismatch between the high vibration frequency of blades and the low achievable sampling frequency, BTT signal is undersampled and thus it is hard to extract characteristic parameters. To address this critical issue in

For tower crane systems widely used in various industrial sectors, their sensors, controllers, and computing units are often of limited capacity. Frequent control tasks tend to cause sensor failures and controller failures. In response to this problem, it is of great significance to reduce unnecessary frequent operations and calculations, thereby reducing system energy consumption and at the same time

Force control plays a critical role in the robotic polishing process. Traditional force control methods primarily emphasize regulating the precise contact force value while paying limited attention to the distribution of force within the contact surface. Drawing inspiration from manual polishing techniques that employ tactile sensing, this study introduces an innovative robotic adaptive polishing system

In contemporary intelligent manufacturing environments, scalability and flexibility have emerged as critical requirements for human-robot collaboration (HRC) systems. As the cornerstone of effective HRC implementations, human activity recognition (HAR) is indispensable for accurate understanding of human operational intent and improving HRC in dynamic industrial environments. Recently, graph convolutional

Model order reduction approaches such as proper orthogonal decomposition (POD) can be used to model and simulate mechanical systems with geometric nonlinearity in high efficiency. However, the complexity of a POD-based reduced-order model (ROM) in online computation is still related to the dimension of its full order model (FOM). In this work, the POD-based hybrid strain method is proposed for the

Severe vibrations during the drilling process are a primary cause of drill string failure and reduced drilling efficiency. However, from a strength theory perspective, material failure results from excessive loads. Among various vibration types, lateral vibration is considered the most detrimental. Therefore, we investigate the force characteristics of the drill string under different lateral motion

Fault feature representation and extraction using time–frequency spectrograms, such as short-time Fourier transform (STFT) and wavelet transform (WT) have been extensively applied in fault detection and diagnostics. However, the weak fault characteristics present during the early stages of fault initiation are often obscured by noise, posing significant challenges for machine health monitoring, particularly

Blisks-coatings rubbing are one of common faults in rotating machinery since the blade tip clearance is getting increasingly small for higher efficiency. As the rubbing-induced external load propagates from the rubbing blisk to the whole rotor, the rubbing margin elsewhere would get smaller to involve more blisks in rubbing. Yet the mechanism and development of single blisk rubbing to multiple blisks

Federated multi-source transfer fault diagnosis can address discrepancies between machine group nodes by utilizing an intermediate distribution in data decentralization scenarios. However, two key challenges remain: (1) most existing methods ignore the conditional distributions during multiple domain adaptation, and (2) the intermediate distribution used is often inadequate for bridging the domain

Efficient dynamic modeling and vibration transfer analysis of a fluid-conveying parallel pipeline (FCPP) are of paramount importance for the vibration suppression and damage detection of the pipeline system. Therefore, this paper initially proposes a hybrid finite element and extended transfer matrix method of the multi-body system (FE-EMSTMM) to perform reduced-order modeling of FCPP. Furthermore

In this paper, a horizontal 3-DOF vibration isolation system with adjustable stiffness is proposed to achieve superior isolation performance while ensuring stability. The system is composed of four isolator units, each comprising a negative-stiffness element and a positive-stiffness element. The mechanism of the system is derived using the Energy Method, and the system’s stiffness adjustability is

Stick-slip friction (SSF), as an interesting dynamic behavior, is widely presented in mechanical systems, and has a significant effect on the mechanical systems’ lifespan and operation accuracy. Accurate SSF models are crucial for the effective evaluation of the dynamic performance of mechanical systems. Therefore, this paper focuses on proposing the parameter identification method for SSF models to

The boundary conditions of turnout rails are complex, involving three types of constraints: double-sided fastening (ordinary rails), single-sided fastening (stock rails), and without fasteners (switch rails). This paper presents a systematic investigation of the nonlinear vibration characteristics of turnout rails using a combination of simulation and experimental approaches. The simulation phase utilized

The propeller rotor, a core power component of turboprop engines, is prone to whirl flutter, a common aeroelastic issue that can lead to rotor instability and aero-engine failure. Whirl flutter analysis requires considering complex blade geometries and accurate flow velocity distributions. In this paper, a three-dimensional blade element momentum method is introduced to calculate induced velocities

The use of emotional words and expressive voices in robots alters the attribution of agency and experience by humans.

Magnetic fields enable remote manipulation of objects and are ideal for medical applications because they pass through human tissue harmlessly. This capability is promising for surgical robots, allowing navigation deeper into the human anatomy and accessing organs beyond the reach of current technologies. However, magnetic manipulation is typically limited to a maximum two–degrees-of-freedom orientation

Future marine operations objectives and tasks require a paradigm shift from robots to autonomous robotic organizations (AROs). These AROs must have advanced cooperative skills, control capabilities, and resilience both as individuals and as heterogeneous robot teams operating in space and air, on the sea surface, and underwater.

Operating in the brain for deep-seated tumors or surgical targets for epilepsy is technically demanding and normally requires a large craniotomy with its attendant risk and morbidity. Neuroendoscopic surgery has the potential to reduce risk and morbidity by permitting surgical access through a small incision with burr hole and a narrow corridor through the brain. However, current endoscopic neurosurgical

Structural health monitoring (SHM) is crucial for ensuring that structures meet operational requirements. However, monitoring large structures often requires a complex and redundant sensor array, which can hinder efficient structural verification. The challenge lies in minimizing sensor redundancy while maintaining accuracy in capturing the structure’s full-field health, particularly in static experiments

Compressive sensing (CS) emerges as a potent strategy for the recovery of blade tip timing (BTT) signal spectrum under conditions of severe undersampling. Yet, the efficacy of prevailing CS methods is contingent upon meticulous parameter tuning, limiting their flexibility across varying operational scenarios. This paper presents a novel block sparse Bayesian learning (BSBL) methodology designed to

The Steel–Concrete Hybrid Tower (SCHT) structure is complex and exhibits significant nonlinear characteristics, presenting a longstanding challenge in dynamic modeling and analysis within the industry. In this study, a wind-rotor-tower integrated coupling model is established using the subsystem co-simulation method to facilitate the dynamic analysis of detailed structures. A multi-body model of the

In this paper, we study the surface effects that bulk flexoelectric models exhibit in finite samples. We first show that flexoelectric materials do not exhibit electromechanical response under homogeneous loading when the body is infinite. However, when the size of the body is finite, due to the symmetry-breaking nature of surfaces, homogeneous loading (mechanical or electrical) can cause an electromechanical

In order to improve the energy harvesting efficiency of acoustic black hole (ABH) structures under low-frequency excitation, this paper proposed a vibro-impact energy harvester, which can greatly improve the energy output through collision under such conditions. The equations of motion are established by using the Bernoulli-Euler beam theory and Rayleigh-Ritz method. Subsequently, the nonlinear impact

Acoustic emission (AE)-based damage location in orthotropic steel decks (OSDs), typical thin-walled spatial structures, remains challenging due to the multi-solution problem induced by complex wave propagation paths. This study proposes a robust AE location method based on topology-aided multi-objective optimization and A0 arrival time correction. Here, the multi-objective optimization model is presented

Remaining Useful life (RUL) prediction is important to ensure the stable operation of mechanical systems. Recently, deep learning (DL) has achieved success in RUL prediction tasks of mechanical systems. However, existing DL-based RUL prediction methods face two significant limitations: (1) they are difficult to perceive the change time from normal state to degradation state. (2) their prediction performance

This paper proposes an inverse neural network approach for stochastic model calibration, focusing on the conversion of high-dimensional system sequential responses into RGB (Red, Green, and Blue) images, which significantly enhances the efficiency of calibration processes. By encoding multi-nodal, multi-directional data sequence into RGB images and employing advanced neural network architectures, including

The original orthogonal PVEH composed of a horizontal beam and vertical beam can produce two resonant peaks with large amplitudes. However, an anti-resonance existing between these two peaks significantly reduces the dynamic responses and energy generation. To achieve high-performance piezoelectric vibration energy harvester (PVEH) under broadband vibrations, a novel orthogonal PVEH with magnet-spring

Axle box bearing fault diagnosis is challenging due to the fault-induced impulses are often buried in heavy background noise from wheel-rail interaction. Recently, various methods have been devised for extracting the hidden repetitive transients, among which blind deconvolution (BD) has arouse much attention but also suffer from some drawbacks. For example, maximum sparsity based BD techniques are

A growing body of research on the prediction of remaining useful life (RUL) considers multiple uncertainties. Still, most studies neglect to quantify multiple uncertainties simultaneously, which makes it difficult to quantitatively assess each uncertainty involved in the full process of RUL prediction. Moreover, most of these RUL prediction methods only apply to large sample systems with much information