Search Results (16)

The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure modes that may affect the overall reliability of a system. This work provides a comprehensive analysis intended as a benchmark for advancements in predictive maintenance and condition-based monitoring. The results not only offer insights into improving the operational lifespan of harmonic drives, but also provide guidance for engineers working with similar systems across various robotic platforms. Robotic systems have advanced significantly; however, maintaining their reliability is essential, especially in industrial applications where even minor faults can lead to costly downtimes. This article examines the impact of harmonic drive degradation on industrial robots, with a focus on collaborative robotic arms. Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) approaches are discussed, highlighting how digital twins and data-driven models can enhance fault detection. A case study using the UR5 collaborative robot illustrates the importance of fault diagnosis in harmonic drives. The analysis of fault-to-failure mechanisms, including wear, pitting, and crack propagation, shows how early detection strategies, such as vibration analysis and proactive maintenance approaches, can improve system reliability. The findings offer insights into failure mode identification, criticality analysis, and recommendations for improving fault tolerance in robotic systems. Full article

The finishing mill is a critical link in the hot rolling process, influencing the final product’s quality, and even economic efficiency. The distribution box of the finishing mill plays a vital role in power transmission and distribution. However, harsh operating conditions can frequently lead to distribution box damage and even failure. To diagnose faults in the distribution box promptly, a fault diagnosis network model is constructed in this paper. This model combines depthwise separable convolution and Bi-LSTM. Depthwise separable convolution and Bi-LSTM can extract both spatial and temporal features from signals. This structure enables comprehensive feature extraction and fully utilizes signal information. To verify the diagnostic capability of the model, five types of data are collected and used: the pitting of tooth flank, flat-headed sleeve tooth crack, gear surface crack, gear tooth surface spalling, and normal conditions. The model achieves an accuracy of 97.46% and incorporates a lightweight design, which enhances computational efficiency. Furthermore, the model maintains approximately 90% accuracy under three noise conditions. Based on these results, the proposed model can effectively diagnose faults in the distribution box, and reduce downtime in engineering. Full article

The North China Plain is among the regions most afflicted by ground fissure disasters in China. Recent urbanization has accelerated ground fissure activity in the three counties of the northern North China Plain, posing significant threats to both the natural environment and socioeconomic sustainability. Despite the increased attention, a lack of comprehensive understanding persists due to delayed recognition and limited research. This study conducted field visits and geological surveys across 43 villages and 80 sites to elucidate the spatial distribution patterns of ground fissures in the aforementioned counties. By integrating these findings with regional geological data, we formulated a causative model to explain ground fissure formation. Our analysis reveals a concentration of ground fissures near the Niuxi and Rongxi faults, with the former exhibiting the most extensive distribution. The primary manifestations of ground fissures include linear cracks and patch-shaped collapse pits, predominantly oriented in east-west and north-south directions, indicating tensile failure with minimal vertical displacement. Various factors contribute to ground fissure development, including fault activity, ancient river channel distribution, bedrock undulations, rainfall, and ground settlement. Fault activity establishes a concealed fracture system in shallow geotechnical layers, laying the groundwork for ground fissure formation. Additionally, the distribution of ancient river channels and bedrock undulations modifies regional stress fields, further facilitating ground fissure emergence. Rainfall and differential ground settlement serve as triggering mechanisms, exposing ground fissures at the surface. This research offers new insights into the causes of ground fissures in the northern North China Plain, providing crucial scientific evidence for sustaining both the natural environment and the socio-economic stability of the region. Full article

The direct-shift gearbox is widely used in many applications, such as automotive and aerospace, due to its large transmission ratio and high transmission efficiency. Rough and heavy-duty working conditions induce various faults, such as scratches, fatigue cracks, pitting, and missing teeth due to breakage. These defects may lead to the failure of one or more components attached to an automatic transmission system. A fault identification scheme for the direct-shift gearbox has been developed, making use of variational mode decomposition (VMD) and convolutional neural network (CNN). The acquired raw signal from the gearbox under different health conditions (healthy, pitting, and chipping) is decomposed into different modes using VMD. The prominent mode is selected based on kurtosis, which is utilized to obtain scalograms. An image matrix is formed utilizing scalograms. Such matrices from different scalograms are divided into training and testing matrices. The training matrices train the CNN model, whereas the testing matrices validate the efficacy of the built CNN model. The proposed scheme identifies faults with 100% accuracy. The proposed scheme has also been compared with other neural networks. These results suggest that the proposed scheme outperforms other networks. Full article

Analytical modelling is an effective approach to obtaining a gear dynamic response or vibration pattern for health monitoring and useful life prediction. Many researchers have modelled this response with various fault conditions commonly observed in gears. The outcome of such models provides a good idea about the changes in the dynamic response available between different gear health states. Hence, a catalogue of the responses is currently available, which ought to aid predictions of the health of actual gears by their vibration patterns. However, these analytical models are limited in providing solutions to useful life prediction. This may be because a majority of these models used single fault conditions for modelling and are not valid to predict the remaining life of gears undergoing more than one fault condition. Existing reviews related to gear faults and dynamic modelling can provide an overview of fault modes, methods for modelling and health prediction. However, these reviews are unable to provide the critical similarities and differences in the single-fault dynamic models to ascertain the possibility of developing models under combined fault modes. In this paper, existing analytical models of spur gears are reviewed with their associated challenges to predict the gear health state. Recommendations for establishing more realistic models are made especially in the context of modelling combined faults and their possible impact on gear dynamic response and health prediction. Full article

Geological structures such as joints and faults in rock mass have a significant influence on open-pit mining. Hence, it is critical to develop an understanding of dynamic joint behavior under blasting loading. This, in turn, can provide both theoretical and practical guidance to improve blasting rock fragmentation and associated bucket excavating efficiency. In this paper, delayed blasting on a highwall bench at an open-pit mine is used as an example; a nonlinear joint blasting model is also constructed. By simplifying the blasting wave propagation velocity and combining the relevant stress and displacement theories of type I and II cracks, equipotential diagrams of the stress and displacement field with the vibration velocity of the particle are obtained. Additionally, ANSYS is used to analyze the distribution of the stress field. This is able to be visualized by the degree of color change post-processing. It is concluded that, with the attenuation of the detonation wave energy, the stress exhibited a decreasing trend in this process. According to the distribution of the peak effective stress, it is found that the peak value first increases to 10–12 MPa and then shows a downward trend. Full article

Gear transmission systems are widely used in various fields. The occurrence of gear cracks, tooth pitting, and other faults will lead to the dynamic characteristics deterioration of the transmission system. In order to calculate the meshing stiffness of faulty gear pairs more effectively and precisely, this article improves the loaded tooth contact analysis (LTCA) method by analyzing the influence of different fault types on gear deformation, including bending-shearing deformation and contact deformation, which combines the accuracy of the finite element method (FEM) and the rapidity of the analytical method (AM). The improved LTCA method can model the fault areas accurately and optimize the deformation coordination equation under the actual meshing situation of the faulty gear tooth, making it suitable for calculating the meshing stiffness of faulty gears. Based on the calculation results of the finite element method, the accuracy of the improved meshing stiffness calculation method has been verified, and the sensitivity of different fault type parameters on meshing stiffness has been studied. Full article

The gear transmission system is an important part of the mechanical system, so it is essential to judge its running state accurately. To solve the difficult problem of identifying the components of coupling faults, this paper derives the calculation method of gear time-varying mesh stiffness for coupling faults of pitting and cracking based on the energy method and considering the coupling between teeth, establishes the dynamics model of two-stage gear transmission system with coupling faults and studies the influence of coupling faults on gear time-varying mesh stiffness and dynamic characteristics. The accuracy of the proposed method is verified by experiments. The results show that both pitting and cracking can lead to a reduction in mesh stiffness. The stiffness of pitting will fluctuate irregularly due to the influence of pitting on the tooth surface, while the stiffness of cracked teeth is relatively smooth. The coupling fault stiffness is dominated by more serious faults. By analyzing the periodic impact components in time domain and the sideband components around the harmonics in frequency domain the faulty gears in the transmission system can be distinguished. It provides an effective reference for the diagnosis of faulty gears. Full article

This work considers forced vibrations in a rotating structure consisting of a two-stage spur gear system with coexisting defects, specifically pitting and cracking. Numerical simulations and experimental analysis in various scenarios of the system in operation were conducted using the RPM–Frequency mapping technique. To identify fault characteristics, the analysis performed assumed the gear system had been misadjusted by a combination of pitting and cracking on the gear teeth. The correlation of the system-forced responses under regular and chaotic vibrations revealed that the system is far more sensitive to the crack than to the pitting when there are fluctuating harmonic peaks present at high vibration levels. Full article

In this study, a three-dimensional finite difference numerical model of the Jinfeng Gold Mine, including surface topography, ore body, shafts, and main faults, was built to estimate the shaft stability and surface deformation induced by underground mining transferred from open-pit mining. Satellite monitoring data of surface displacement at several points was used to calibrate the numerical model. The sequence of excavation and filling in the simulation was determined according to the mining schemes with appropriate simplification. The distribution of large deformations in simulation is consistent with the cracking areas on the slopes and surface. Besides, shaft deformation in the simulation is small, which is consistent with the reality that there are no large deformations of shafts in the underground mining activities above 30 m level. After the completion of simulated underground mining, the deformations of shafts and surface are generally far less than the critical deformation. Hence, we concluded that the shafts and surface of the Jinfeng Gold Mine can remain stable in the underground mining stage. Overall, the method in the study provides references for the estimation of shaft stability and surface deformation in the underground mining stage of mine transfer from open pit. Full article

Brillouin scattering-based distributed fiber optic sensing (DFOS) technologies such as Brillouin optical time domain reflectometry (BOTDR) and Brillouin optical time domain analysis (BOTDA) have broad applicability for the long term and real-time monitoring of large concrete structures, underground mine excavations, pit slopes, and deep subsurface wellbores. When installed in brittle media, however, the meter scale spatial resolution of the BOTDR/A technology prohibits the detection or measurement of highly localized deformations, such as those which form at or along cracks, faults, and other discontinuities. This work presents a novel hybrid fiber optic cable with the ability to self-anchor to any brittle installation media without the need for manual installation along fixed interval points. Laboratory scale testing demonstrates the ability of the hybrid fiber optic cable to measure strains across highly localized deformation zones in both tension and shear. In addition, results show the applicability of the developed technology for strain monitoring in high displacement environments. Linear relationships are proposed for use in estimating the displacement magnitude along discontinuities in brittle media from strain signals collected from the hybrid fiber optic cable. The hybrid fiber optic cable has broad potential applications, such as geomechanical monitoring in underground mines, surface pits, large civil infrastructure projects, and deep subsurface wellbores. The benefits of fiber optic sensing, such as the intrinsic safety of the sensors, the long sensing range, and real time capabilities make this a compelling technique for long term structural health monitoring (SHM) in a wide range of industrial and civil applications. Full article

Gearboxes are widely used in industrial processes as mechanical power transmission systems. Then, gearbox failures can affect other parts of the system and produce economic loss. The early detection of the possible failure modes and their severity assessment in such devices is an important field of research. Data-driven approaches usually require an exhaustive development of pipelines including models’ parameter optimization and feature selection. This paper takes advantage of the recent Auto Machine Learning (AutoML) tools to propose proper feature and model selection for three failure modes under different severity levels: broken tooth, pitting and crack. The performance of 64 statistical condition indicators (SCI) extracted from vibration signals under the three failure modes were analyzed by two AutoML systems, namely the H2O Driverless AI platform and TPOT, both of which include feature engineering and feature selection mechanisms. In both cases, the systems converged to different types of decision tree methods, with ensembles of XGBoost models preferred by H2O while TPOT generated different types of stacked models. The models produced by both systems achieved very high, and practically equivalent, performances on all problems. Both AutoML systems converged to pipelines that focus on very similar subsets of features across all problems, indicating that several problems in this domain can be solved by a rather small set of 10 common features, with accuracy up to 90%. This latter result is important in the research of useful feature selection for gearbox fault diagnosis. Full article

This study investigates the effects of laser powder bed fusion (LPBF) on the hydrogen uptake of the face-centered cubic (FCC) equiatomic CrFeNiMn multicomponent alloy after cathodic hydrogen charging (HC). Hydrogen desorption was evaluated using thermal desorption spectroscopy (TDS), and microstructural changes after the TDS test were examined. Results reveal that the amount of hydrogen absorbed by LPBF CrFeNiMn alloy was significantly higher than that in pulsed electric current sintered (PECS) CrFeNiMn alloy or in conventional 316L austenitic stainless steel. The observations are ascribed to the differences in the amount of hydrogen absorbed by the multicomponent lattice, dislocation densities, width of segregation range at cell walls created by the rapid cooling in LBPF, and vacancies remaining after cooling to room temperature. A hydrogen-charged LBPF transmission electron microscope (TEM) specimen was also characterized. Stacking faults and cracks along the (111)-planes of austenite were observed. Scanning electron microscopy (SEM) of the surface of the TDS-tested samples also indicated hydrogen-induced cracks and hydrogen-induced submicron pits at the grain boundary inclusions. Full article

Wind turbine gearboxes are known to be among the weakest components in the system and the possibility to study and understand the behavior of geared transmissions when subject to several types of faults might be useful to plan maintenance and eventually reduce the costs by preventing further damage. The aim of this work is to develop a high-fidelity numerical model of a single-stage planetary gearbox selected as representative and to evaluate its behavior in the presence of surface fatigue and tooth-root bending damage, i.e., pits and cracks. The planetary gearbox is almost entirely modelled, including shafts, gears as well as bearings with all the rolling elements. Stresses and strains in the most critical areas are analyzed to better evaluate if the presence of such damage can be somehow detected using strain gauges and where to place them to maximize the sensitivity of the measures to the damage. Several simulations with different levels, types and positions of the damage were performed to better understand the mutual relations between the damaged and the stress state. The ability to introduce the effect of the damage in the model of a gearbox represents the first indispensable step of a Structural Health Monitoring (SHM) strategy. The numerical activity was performed taking advantage of an innovative hybrid numerical–analytical approach that ensures a significant reduction of the computational effort. The developed model shows good sensitivity to the presence, type and position of the defects. For the studied configuration, the numerical results show clearly show a relation between the averaged rim stress and the presence of root cracks. Moreover, the presence of surface defects seems to produce local stress peaks (when the defects pass through the contact) in the instantaneous rim stress. Full article

As an important machine component, the gearbox is widely used in industry for power transmission. Condition monitoring (CM) of a gearbox is critical to provide timely information for undertaking necessary maintenance actions. Massive research efforts have been made in the last two decades to develop vibration-based techniques. However, vibration-based methods usually include several inherent shortages including contact measurement, localized information, noise contamination, and high computation costs, making it difficult to be a cost-effective CM technique. In this paper, infrared thermal (IRT) images, which can contain information covering a large area and acquired remotely, are based on developing a cost-effective CM method. Moreover, a convolutional neural network (CNN) is employed to automatically process the raw IRT images for attaining more comprehensive feature parameters, which avoids the deficiency of incomplete information caused by various feature-extraction methods in vibration analysis. Thus, an IRT–CNN method is developed to achieve online remote monitoring of a gearbox. The performance evaluation based on a bevel gearbox shows that the proposed method can achieve nearly 100% correctness in identifying several common gear faults such as tooth pitting, cracks, and breakages and their compounds. It is also especially robust to ambient temperature changes. In addition, IRT also significantly outperforms its vibration-based counterparts. Full article