Maintenance, Reliability and Condition Monitoring
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Criticality mapping of a system in the mining industry using Bayesian network
Full text linkEffective evaluation of equipment criticality is a key concern in Engineering Asset Management, particularly in operationally intensive industries such as mining. While the concept of criticality is often subjective, it can be assessed more objectively using quantifiable indicators such as cost, downtime, and failure rate. This paper presents a data-driven approach to assess equipment-level criticality by analysing the impact of individual equipment downtimes on overall system performance. Focusing on a case study from a gold mining operation in Australia, the study demonstrates how equipment-level performance can be used to prioritise maintenance efforts and support more informed decision-making. One of the key contributions of this work lies in its integration of statistical modelling and probabilistic analysis to identify critical equipment within a system. Unlike conventional methods that often overlook uncertainty or assume uniform equipment influence, this approach quantifies the impact of individual equipment failures on system-level outcomes. The analysis treats subsystems independently, acknowledging the absence of interdependency data while still capturing meaningful insights about their relative importance. By leveraging a combination of platforms – Excel for data preprocessing, R for simulation, and Netica for network-based evaluation – the study offers a replicable and scalable methodology for criticality assessment. Sensitivity analysis within the Bayesian Network model further enhances the framework by highlighting components with the highest influence on system reliability. The outcome is a transparent, objective, and practically applicable tool for maintenance prioritisation, offering significant value in data-intensive and reliability-critical environments like mining. This paper contributes to the growing body of research focused on integrating operational data with advanced modelling techniques to improve asset performance management
Experimental diagnostics of the condition and behavior of an excavation machine: a review of the most important methods
Full text linkThe paper presents an integral procedure for conducting experimental measurements on excavation machines. Excavators have a complex structure with pronounced dynamic behavior. The identification of exploitation behavior is observed through experimental measurement of stress and acceleration, drive load, and vibrations. Electro-resistive measuring tapes were used to observe the steel structure, devices for measuring current, i.e. engaged power on the drives, as well as devices for measuring vibrations at characteristic points of the drive. The results obtained realistically reflect the condition and behavior of the structure and drive equipment. The goal is to introduce systematic research to monitor the condition and behavior of the equipment on the excavator. This approach forms the backbone of predictive observation, influencing the proper management of the excavator. Experimental measurements are performed to prove the correctness of the numerical model and to diagnose the condition and behavior of the structure and power units. By monitoring the condition and behavior of the equipment, we can optimally influence the process of maintenance of the equipment as well as the lifespan of the mining machine. This work includes the most important experimental measurements to carry out reconstructions, revitalizations, and modernizations on mining machines
Numerical study of heat and mass transfer in a relief pipeline with variable diameter
Full text linkIn this work, a comprehensive numerical and analytical investigation of hydrodynamic and thermal processes in closed pipelines of variable diameter is carried out, taking into account the terrain profile and external temperature effects. The scientific novelty of the study lies in the development of an improved quasi-one-dimensional model that allows for an integral assessment of the influence of geometric parameters (diameter, inclination angle, internal surface roughness) and thermophysical properties (material thermal conductivity, ambient temperature) on the distribution of pressure and temperature along the pipeline. The developed model was validated by comparing it with numerical simulations based on the SST turbulence model and calculations using the Darcy-Weisbach equation. The maximum deviation between the results did not exceed 2 %, confirming the high accuracy of the proposed method. It was established that hydraulic losses are predominantly determined by the pipe diameter and flow velocity, whereas thermal losses depend mainly on the thermal conductivity of the material and the spatial orientation of the pipeline. The proposed approach provides a reliable and computationally efficient foundation for predicting the energy characteristics of liquid transportation systems, optimizing structural parameters, and improving the energy efficiency of industrial, municipal, and thermal networks
Analysis of modal and vibration response characteristics of high-pressure storage tanks
Full text linkAnalysis of dynamic characteristic was conducted focusing on the transportation of high-pressure storage tanks, covering two scenarios: independent transportation and mixed transportation. For independent transportation, analysis of free modal was carried out to obtain the first four orders of modal shapes. Additionally, the influence of two constraint methods on the modal characteristics and stress distribution was studied, including fixed at both ends and fixed at the cylinder body. Results show that when the tank was fixed at the cylinder body, it had a higher natural frequency and a lower stress level, making it safer. For mixed transportation, a finite element model was built for 6 high-pressure storage tanks, and analysis of random vibration was performed. The results showed that stress was mainly concentrated on the crossbeams and connection nodes, while the stress on the main body of the storage tanks was relatively low. The overall structure exhibited excellent fatigue performance and met the mechanical and safety requirements under random vibration conditions
Experimental analysis of running wheel for a straddle monorail vehicle
Full text linkThis article conducts in-depth research on the force analysis of the test running wheel of a certain type of straddle monorail vehicle, based on the tire six-component force test and wheel dynamic stress test. The main research objective is to accurately identify the factors affecting the wheel strength, thereby providing a solid foundation for subsequent design optimization and safety enhancement. The research commences with a meticulous calibration of the vehicle connecting rod in the laboratory, aiming to acquire the “force-strain” coefficients under both tension and compression conditions. A novel approach lies in the verification of calibration accuracy through a detailed comparison with experimental results, ensuring the reliability of subsequent data acquisition. By strategically installing displacement sensors at various positions to measure the vehicle's dynamic displacement and detecting the strain of the connecting rod, the study innovatively calculates the six-component force data of the tire, which provides a comprehensive data basis for analyzing the forces acting on the wheel hub. Then evaluating the fatigue strength of the wheel hub under AW0 and AW3 operating conditions based on the IIW standard, the research uncovers unique findings. It is revealed that, although the maximum dynamic loads of the vertical force of the running wheel, the lateral force of the guide wheel, and the lateral force of the stabilizing wheel are within the limit load range with a certain safety margin, there are 1 point and 3 points on the wheel hub under AW0 and AW3 working conditions, respectively, that fail to meet the fatigue strength criterion requirements. The maximum equivalent force amplitude at Measurement Point 3 of the inner hub reaches 51.4 MPa, while the calculated service mileage is only 31,000 kilometers. This discovery is of great significance as it precisely pinpoints the weak points of the wheel hub, which is a major contribution to the field. Moreover, during the analysis of the wheel hub's dynamic stress during emergency braking and the influence of polygonal wear on it, the research confirms that there is no abnormal change in the wheel hub’s dynamic stress during emergency braking, and the polygonal wear of the tire shoulder has a negligible impact on the wheel hub’s dynamic stress. These results not only calculate the six-component force data of the tire but also break new ground in understanding the interaction between different factors and the wheel hub’s performance
Effect of Si addition on phase structure and wear resistance of CoCrFeMoNi alloy coatings
Full text linkCoCrFeMoNi high entropy alloy coating was prepared on Q235 substrate by plasma cladding method. The phase structure, morphology characteristics, element distribution, microhardness, and wear resistance for this alloy without and with Si doping were investigated by XRD, OM, SEM, EDS, microhardness tester, and friction-wear tester, respectively. The results show that CoCrFeMoNi alloy is composed of a single FCC phase, while Si-containing alloy is composed of FCC main phase and HCP phase. Both alloys have a typical dendritic structure. There is a layer of isotropic fine-grained region near the fusion line, and a columnar crystal region away from the fusion line. After adding Si element, the enrichment of Mo element in the interdendrite region and Co element in the dendrite region significantly decreased, which is related to the Si-containing alloy can provide a liquid environment with longer duration, lower viscosity, and greater fluidity. The change of Cr element enrichment from interdendrite region to dendrite region is the result of comprehensive competition of mixing enthalpy, atomic radius difference, electronegativity, density, and melt flowability between alloying elements. The friction coefficients of the two alloys show a rapid increase first and then gradually stabilize with the increase of time. After adding Si element, the hardness and wear resistance of the alloy are greatly improved, which is mainly related to the increase of the lattice distortion of FCC phase, the formation of high-strength HCP phase and the reduction of internal defects
Small targets detection in low-resolution remote sensing images based on super-resolution joint optimization
Full text linkWhile convolutional neural networks have driven remarkable progress in remote sensing object detection, persistent challenges remain in detecting small targets within low-resolution imagery due to their limited pixel representation and feature degradation during hierarchical downsampling. To address this, this study proposed the joint super-resolution and detection network (JSRDN), which synergistically optimizes SR reconstruction through task-specific detection feedback, significantly enhancing small target recognition in LR remote sensing imagery. Firstly, generator in generative adversarial network incorporates improved residual blocks, enabling enhanced perception of complex deep-level features in the SR reconstruction process. Then, a perceptual loss function is introduced into the adversarial training process, which captures perceptual discrepancies in high-level features between reconstructed images and original HR references. After that, an edge-enhancement network is designed to dynamically detect edges in intermediate features restored by the generator, prioritizing edge influence across network layers to generate discriminative features for target recognition. Furthermore, the JSRDN implements detection-driven feedback by backpropagating object recognition loss through the generator, enforcing the super-resolution process to prioritize detection-salient feature recovery. Evaluated on 64×64 low-resolution COWC datasets, JSRDN achieves 0.1819 dB peak signal-to-noise ratio (PSNR) and 7.18 % average precision (AP) improvements over the deep residual dual-attention network (DRDAN), with ablation studies and visualizations confirming its balanced optimization of reconstruction fidelity and detection-oriented feature learning. This technology can provides valuable support for small target measurement and opens new opportunities in the field
Research on bearing equipment fault diagnoses via SAWOA-LSTM
Full text linkTo address the current low fault diagnosis accuracy problem for bearing equipment, and improve the detection methods, in this paper a sine-adapted whale optimization algorithm (SAWOA)-based optimization of a long short-term memory (LSTM) network is proposed as the equipment fault diagnosis method (SAWOA-LSTM). First, an optimization strategy based on sinusoidal population initialization and adaptive optimization is proposed for the whale optimization algorithm, which has the two drawbacks of slow convergence and easily falling into a local optimum. Second, to improve the accuracy and efficiency of fault diagnoses, the SAWOA is used to optimize the number of hidden units and the learning rate parameter of the LSTM. Compared with ACO-, PSO-, and WOA-based LSTM models, the proposed method improves diagnostic accuracy by 14.17 %, 15.03 %, and 4.32 %, respectively. In tests on 50 bearing samples, SAWOA-LSTM further improves accuracy for RBD, IRA, and ORD by 1.08 %, 1.62 %, and 1.10 %, respectively. Our algorithm provides an innovative solution for the health management of complex industrial bearing equipment
Improved CEEMD-based correction method for low-frequency shock response spectrum in large dual-wave shock tester devices
Full text linkThe shock response spectrum (SRS), calculated from a shock acceleration signal, is a critical indicator of shock environments. However, under intense loads, acceleration sensors are prone to trend term errors that can cause significant drift in the low-frequency spectral lines of large dual-wave shock tester devices. To address this issue, the complementary ensemble empirical mode decomposition (CEEMD) method was employed to decompose acceleration signals and restore the actual shock environment. Intrinsic mode functions (IMFs) were cross-correlated and compared to a predefined threshold to identify the effective IMF components required to reconstruct the signal. K-means clustering was employed to further validate the effectiveness of the IMFs for enhanced selection accuracy. Finally, the reconstructed acceleration signal was used to calculate a corrected SRS. The proposed approach demonstrated significant improvements over the traditional CEEMD algorithm. The corrected SRS exhibits a 5.6316 dB/oct slope in the low-frequency band, reflecting an equal displacement trend. The maximum error at the corresponding frequency was less than 6 % in comparison to the relative displacement response measured by low-frequency spring oscillators. This improved CEEMD correction method can effectively restore the actual shock environment of a dual-wave shock tester device, offering a valuable reference for evaluating shock resistance in onboard equipment
Acoustic detection of fan blade faults based on dynamic Cauchy swarm algorithm to optimize support vector machine
Full text linkFan blades operate in outdoor environments, where the detection of sound signals is susceptible to interference from background noise such as random loads, wind speed, rainwater, and other ambient noise. Therefore, this article proposes an acoustic detection method for wind turbine blade faults based on a dynamic Cauchy bee colony algorithm-optimized support vector machine. First, the signal is preprocessed using a Butterworth bandpass filter, and the full frequency band is divided into sub-bands using the octave band feature extraction method. Based on frequency domain analysis, the natural frequency offset of the blade is determined. Next, the dynamic Cauchy bee colony algorithm is applied to optimize support vector machine parameters, while moving average and bandpass filtering are used to smooth the noise power curve and extract impeller speed information. The experimental results show that the proposed method converges in fitness value after 22 iterations, with a detection time of only 6.8 seconds and small fluctuations in impeller speed amplitude. In terms of classification performance, the accuracy of detecting normal samples is 0.95, the recall rate is 0.96, and the F1 score is 0.95. The method demonstrates high prediction accuracy and stability for various types of fault samples and can be reliably applied to the acoustic detection of wind turbine blade faults