Journal of Mechatronics and Artificial Intelligence in Engineering
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Fabrication and characterization of interdigital transducer structures as temperature sensors by two-photon lithography
Get PDFContinuous developments in the field of 3D printing techniques and equipment have enabled their usage in the field of electronics structures, circuits, and device fabrication in addition to many other fields. This advancement has enabled the potential fabrication of sensors using silicon-based micro or even Nanoelectronics. Currently, the manufacturing and packaging of such devices and structures are heavily reliant on lithography, which can be slow and can involve substantial processing requirements. In this paper, a temperature-sensing Interdigital Transducer (IDT) structure was designed and fabricated using Direct Laser Writing (DLW) based on Two-Photon Lithography (TPL), which is a high-resolution 3D printing technology. The TPL in a positive photoresist was combined with the physical vapor deposition method and the lift-off process to create gold IDT microstructures. The developed sensing structures were characterized using a network analyzer to determine the resonance frequency and its dependence on the temperature changes. The results showed that the IDT structures exhibit a linear response toward the changes in temperature with an average sensitivity of 0.123 MHz/°C. The most important advantage in producing the IDT structure with the additive manufacturing technique is that a very small-sized structure is produced error-free and efficiently
A k-kNN miscalibrated current transformer identification method based on line topology for distribution networks
Get PDFThe operational duration and environmental factors associated with current transformers (CTs) in distribution networks makes them prone to measurement miscalibration during their operation. To address this, a kernel k-nearest neighbor (k-kNN) miscalibrated CT identification method based on line topology is proposed. This method relies on the composite characteristics of load currents specific to certain line topologies. High-precision secondary-side CT current data provided by the current acquisition devices in the feeder area are used to construct a multiple linear regression model. The multiple linear regression model is established in the complex domain, and indirectly assesses the measurement status of the current transformers by analyzing the complex coefficients. Building upon the kNN identification algorithm, a kernel function is introduced to map low-dimensional distance feature vectors into a higher-dimensional feature space where linear separability is significantly enhanced, thus improving the accuracy with which abnormal coefficients can be detected in the multiple linear regression model. Experimental simulations and field application scenarios demonstrate that the proposed method significantly outperforms traditional kNN algorithms in terms of classification performance. Specifically, there is an increase of 12.0 % in the F1 score, a rise of 13.3 % in accuracy, and an improvement of 12.0 % in recall. Moreover, in practical engineering applications, the recognition metrics consistently exceed 93 %, which substantiates the effectiveness of the proposed miscalibrated CT identification method
Analysis and synthesis of a controllable crank-slider mechanism with parallel springs for frame saws
Get PDFFrame saws suffer from large unbalanced inertia forces, limiting operating speed and requiring heavy construction. This study aims to overcome these limitations by synthesizing a dynamically balanced main drive mechanism using a novel approach based on prescribed motion laws. The methodology involves proposing a crank-slider mechanism featuring a cam-actuated variable-length crank. The mechanism configuration with parallel spring is analyzed allowing for balancing inertia forces, achieved using a prescribed cosine slider motion law. For the considered configuration, the required variable crank length function (cam profile) and associated mechanism parameters (connecting rod length, spring stiffness) are analytically synthesized. The results of the carried-out numerical modeling demonstrate successful synthesis of a near-circular cam profile and very low pressure angles for the case studied. These findings show that synthesizing the saw drive kinematics based on force balancing requirements can theoretically eliminate inertial loads, offering the potential for higher speeds of saw frames and reduced loads. The synthesized near-circular cam profile suggests a pathway towards simpler manufacturing. The implications of successfully implementing such dynamically balanced frame saw mechanisms are potentially transformative for the sawmilling industry. Eliminating the primary inertial forces removes the major obstacle to increasing operating speeds. This could allow frame saws to operate closer to the optimal cutting speeds for wood (e.g., 40-50 m/s), leading to significant gains in productivity
Pattern recognition of acoustic emission signals by Q235 steel corrosion in marine environment
Get PDFTo overcome the limitations of traditional monitoring methods, which are restricted to periodic inspections, this study proposes a real-time method for identifying metal corrosion damage patterns to monitor the condition of Q235 steel corrosion based on acoustic emission (AE). Firstly, AE technology was utilized to monitor the corrosion process of Q235 steel plates in simulated industrial marine environment in real-time. Wavelet packet energy spectrum coefficients, closely related to the damage mechanism, were extracted from the acquired signals. A feature matrix was then constructed using principal component analysis (PCA) to eliminate redundant information and enhance computational efficiency. The K-means clustering algorithm was then applied to classify the AE signals into three classifications: the signals of mode 1 correspond to bubble rupture, the signals of mode 2 to pit growth and expansion, and the signals of mode 3 to the detachment of corrosion products and oxide film rupture. A damage pattern recognition model based on a convolutional neural network (CNN) was developed, enabling the real-time recognition of other unknown AE signals generating during the corrosion process of Q235 steel, and it exhibited satisfactory performance in accurately identifying corrosion-related acoustic emission patterns
Development of a flexible piezoresistive sensor prototype using resin doped with magnetically oriented nanoparticles
Get PDFHigh-performance flexible piezoresistive sensors are highly useful in areas such as biomedicine, soft robotics, and pressure change detection technology. However, they require complex designs and advanced manufacturing methods. In this study, the design and fabrication of a flexible piezoresistive sensor using a flexible resin matrix doped with magnetically oriented iron nanoparticles is presented. The sensor consists of a flexible polymer resin matrix as substrate, reinforced with iron nanoparticles in different concentrations (0.5 %, 0.7 % and 1 % by weight), oriented by a magnetic field during the manufacturing process. The nanoparticles significantly enhance the piezo-resistive properties of the sensor, increasing its sensitivity and electrical conductivity under compressive loads. The sensor demonstrated high sensitivity under loads greater than 100 N in samples with concentrations of 0.7 % and 1 % of nanoparticles, and exhibited stability during cyclic testing, demonstrating durability. Additionally, stability tests showed excellent durability in repeated load cycles. Scanning Electron Microscopy (SEM) and Confocal Laser Scanning Microscopy (CLSM) confirmed the effective alignment and distribution of the nanoparticles within the matrix, enhancing conductivity. This flexible piezoresistive sensor doped with nanoparticles has great potential for future applications in technologies such as soft robotics and electronic skins, where high sensitivity and durability in pressure detection are required
Dynamic detection and evaluation of wheel flats in heavy-haul railway wheelsets using wayside monitoring systems
Get PDFIn recent years, heavy-haul railways have become a critical direction for freight transport in China, with wheel flats in wheelsets posing significant threats to operational safety and infrastructure integrity. Traditional detection methods (e.g., manual inspection, TPDS) suffer from low efficiency or limited accuracy in characterizing flat features. To address this, this study develops a rigid-flexible coupling dynamic model for C80 wagons with K6 bogies, uniquely integrated with field data from the Truck Operation Detection System (TODS) to bridge simulation and engineering application gaps. Focusing on wheel-rail force responses under wheel flat conditions, we establish a quantitative mapping relationship between flat length, vehicle speed, and impact force through polynomial fitting of simulation data (10-80 km/h for empty/loaded vehicles). To validate feasibility, a 56-channel wayside monitoring system (TODS) is installed on a heavy-haul railway, calibrated via hydraulic loading to ensure measurement accuracy. Field tests (80,541 vehicles monitored) confirm that TODS can infer flat length from detected impact forces, with results consistent with TPDS alarms but offering finer characterization of flat dimensions. This work provides a practical solution for real-time wheel flat detection, enhancing maintenance efficiency and safety in heavy-haul operations
Acoustic detection of fan blade faults based on dynamic Cauchy swarm algorithm to optimize support vector machine
Get PDFFan 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
Analysis of the influence of vibration phenomena in pump systems on electrical energy consumption and operational efficiency
Get PDFDespite the long-standing recognition of vibration phenomena as a critical factor affecting both mechanical reliability and energy performance, yet their influence on electrical energy consumption remains insufficiently quantified. Excessive vibration, originating from rotor imbalance, shaft misalignment, bearing wear, and hydraulic instabilities, can result not only in accelerated component degradation but also in significant increases in energy demand and reductions in hydraulic efficiency. Understanding the quantitative relationship between vibration intensity and pump energy performance is therefore essential for both predictive maintenance strategies and energy efficiency improvements in pumping systems. This paper presents an experimental investigation of the effect of vibration on the electrical energy consumption and operational efficiency of centrifugal pumps. Five industrial pump types, with rated powers ranging from 15 to 75 kW and capacities from 100 to 320 m3/h, were tested under controlled conditions. Measurements were carried out using UT310A vibration testers, an ultrasonic flow meter, and a Fluke 1777 Power Quality Analyzer. Vibration signals, volumetric flow rates, pressure heads, and three-phase electrical parameters were simultaneously recorded under partial load, nominal load, and overload conditions. Hydraulic power and efficiency were then calculated, while statistical analyses-including correlation and regression models-were applied to determine the relationship between vibration intensity and electrical performance. The results revealed a strong positive correlation between increasing vibration levels and higher electrical energy demand. In particular, RMS vibration acceleration was found to be a reliable predictor of additional energy losses, while efficiency was observed to decrease as vibration intensity increased. These findings not only confirm the detrimental effect of mechanical instability on energy consumption but also provide a methodological framework for integrating vibration monitoring into energy management practices. By bridging the gap between mechanical diagnostics and energy performance analysis, the study contributes new insights that can support the development of predictive maintenance systems, improve pump reliability, and promote more sustainable operation of pumping stations
Experimental thermal fatigue crack on brake disc of heavy vehicle
Get PDFBrake system reliability is critical for the safety and performance of heavy vehicles, including semi-trailers, passenger buses, and industrial transport units. This study investigates the thermal fatigue failure mechanisms in brake discs (BDs), which are subjected to extreme operational conditions. The primary motivation is to enhance brake disc durability and reduce the risk of catastrophic failures by understanding the interplay between material properties, thermal stress, and fatigue resistance. A comprehensive experimental approach was employed, including visual inspections, chemical composition analysis, metallurgical structure examination, hardness testing, and tensile strength evaluation. The study compares brake discs that have undergone extensive service with those in an undamaged state to identify critical degradation patterns. The results indicate that temperature fluctuations and cyclic thermal stresses induce crack formation and propagation, with rough graphite inclusions significantly reducing fatigue strength. Furthermore, deviations in silicon and carbon content were found to impact material integrity, contributing to premature failure. The findings of this research provide actionable insights for optimizing brake disc design, material composition, and manufacturing processes. By modifying graphite distribution, refining alloy compositions, and improving thermal resistance, future brake systems can achieve greater durability and reliability. These advancements will directly enhance braking efficiency, reduce maintenance costs, and improve overall vehicle safety
Criticality mapping of a system in the mining industry using Bayesian network
Get PDFEffective 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