Machine Operating Conditions Research Articles

Unsupervised anomaly detection in shearers via autoencoder networks and multi-scale correlation matrix reconstruction

As the main equipment of coal mining production, the anomaly detection of shearer is important to ensure production efficiency and coal mine safety. One key challenge lies in the limited or even absence of labeled monitoring data for the equipment, coupled with the high costs associated with manual annotation. Another challenge stems from the complex structure of the mining machines, making it difficult to reflect the overall operational state through local anomaly detection. Consequently, the application of decoupled local anomaly detection for mining machines in practical production remains challenging. This paper presents an unsupervised learning-based method for detecting anomalies in shearer. The method includes a module for constructing a Multi-scale Correlation Matrix (MSCM) of mining machine operating conditions, as well as the CNN-ConvLSTM Autoencoder (C-CLA) network. The module for constructing an MSCM enhances the representation of interrelationships between various features of the equipment from different perspectives using multiple correlation analysis methods. The C-CLA network integrates convolutional and convolutional recurrent neural networks, with the convolutional structure extracting local spatial features and the ConvLSTM structure further capturing information from different time scales and feature scales, thereby enhancing the model’s perceptual capabilities towards changes in equipment status. Finally, shearer anomaly detection is achieved through the analysis of reconstructed residual matrices. The rationality and practicality of the proposed method have been validated on our dataset, and the model’s generalization capability has been verified through repeated experiments in similar scenarios. However, due to variations in the working environment of different mining faces and differences in equipment models, implementing detection on other mining faces often requires retraining the model with new data. Furthermore, we compared our method with other anomaly detection techniques, and our detection efficiency was superior by approximately 3%. This method effectively detects anomalies in the shearer.

Experimental Study of Natural Gas and Hydrogen Co-Firing Characteristics Using Different Types of Single Nozzles of F-Class Practical Gas Turbine Combustors

Abstract Recent research on co-firing natural gas and hydrogen, a carbon-free clean fuel, aims to reduce greenhouse gas emissions from aging gas turbine power generation, a key energy issue. This approach can enhance old gas turbines and increase the proportion of combined cycle power plant utilization as coal-fired power plants in Korea gradually shut down. This study seeks optimal operating conditions for mixed fuels without modifying the F-class gas turbine combustor. Experiments were conducted using four different types of fuel nozzles (F-Class DLN combustors) under varying loads and co-firing rates. The test used actual machine operating conditions from 30% to 100% thermal load, with hydrogen co-fired with natural gas up to 70% at each load. OH high-speed imaging and an OH-PLIF technique analyzed flame structure and characteristics. Dynamic pressure was measured to check combustion instability, and exhaust gas emissions were evaluated for combustion characteristics. Key findings include critical co-firing rates for each nozzle based on NOx emission levels and combustion dynamics. As the hydrogen co-firing rate increased, flame length decreased, and NOx levels rose rapidly beyond 30%vol. Dynamic pressure oscillations showed no significant variations compared to natural gas combustion. This study successfully derived a characteristic operation map for a single nozzle based on the hydrogen co-firing rate.

Сравнительный прочностной анализ алмазных сегментов канатных камнерезных станков

The paper discusses the different shapes of diamond segments for stone cutting machines. The purpose of this research is to find ways to improve the abrasion resistance of the diamond segments, reduce the vibration load and increase the cutting efficiency. The operating conditions of stone-cutting machines are analysed, as well as the ways of increasing the efficiency of diamond segments by changing their shape. A calculation of the loads arising during operation of the diamond segments is made, and the results are verified using an experimental setup. The initial shape is analysed and the stresses that exceed the material parameters are found. In order to reduce the stresses, a diamond segment with rounded edges is simulated. Segments with bevelled edges and conical shape are also considered. Based on the experimental results, it is found that one of the promising shapes is that with rounded edges.

Pourbaix diagrams for iron-chromium alloys in lithium bromide absorption machines

The influence of lithium bromide concentration on the Pourbaix diagrams for iron-chromium alloys is evaluated in this work, which allows for predicting the corrosion to stainless steels exposed to the aggressive operating conditions of lithium bromide absorption machines (concentrations ranging from: 400–992 g/L). The novelty of this work is found in the development of Pourbaix diagrams for the Fe-Cr-Br–-H2O quaternary system in aqueous solutions with a high concentration of lithium bromide, a topic which has not yet been studied. Results show that the corrosion region at acid pH corresponding to the aqueous species FeBr2(aq), FeBr3(aq), Cr+2, CrOH+2, Cr+3 or CrBr+2 shifts to higher pHs with increasing lithium bromide concentration, which decreases the immunity region of iron and chromium and decreases the passivation region of the solid species FeCr2O4, Fe2O3 and Cr2O3.

A reinforcement learning enhanced memetic algorithm for multi-objective flexible job shop scheduling toward Industry 5.0

Flexible job shop scheduling problem (FJSP) with worker flexibility has gained significant attention in the upcoming Industry 5.0 era because of its computational complexity and its importance in production processes. It is normally assumed that each machine is typically operated by one worker at any time; therefore, shop-floor managers need to decide on the most efficient assignments for machines and workers. However, the processing time is variable and uncertain due to the fluctuating production environment caused by unsteady operating conditions of machines and learning effect of workers. Meanwhile, they also need to balance the worker workload while meeting production efficiency. Thus a dual resource-constrained FJSP with worker’s learning effect and fuzzy processing time (F-DRCFJSP-WL) is investigated to simultaneously minimise makespan, total machine workloads and maximum worker workload. Subsequently, the reinforcement learning enhanced multi-objective memetic algorithm based on decomposition (RL-MOMA/D) is proposed for solving F-DRCFJSP-WL. For RL-MOMA/D, the Q-learning is incorporated into memetic algorithm to perform variable neighbourhood search and further strengthen the exploitation capability for the algorithm. Finally, comprehensive experiments on extensive test instances and a case study of aircraft overhaul shop-floor are conducted to demonstrate effectiveness and superiority of the proposed method.

Tribo-Dynamic analysis of nano-enhanced palm blend lubricant for roller bearing application

Abstract Environmental concerns have led to an increase in the development of bio-lubricants during the last ten years. Many tribological studies address the findings of various types of bio-based lubricant performance derived from plant-based edible and non-edible oils in different operating conditions. However, those tribological tests were limited to workbench tribometers. In the present work, experimental investigations were carried out to evaluate the lubrication performance of functionalized multiwall carbon nanotubes (FMWCNTs) blended palm oil compared to mineral oil. The steel bearings are commonly used to minimize the effect of dynamic loads coming from the driven end in sliding / rolling conditions of the bearing. The wear severity on the bearing surfaces was examined using a Scanning electron microscope coupled with energy-dispersive X-ray spectroscopy (EDX), 3D-optical profilometer results provided detailed information on surface profiles and roughness created on bearing surfaces under machine operating conditions. FFT-bearing vibration results were correlated with the surface degradation study of SEM analysis. Zeta-potential values reveal that the nanomaterial showed stable performance in the lubricant for 80 % of the total machine operating duration. Overall, experimental studies indicate that the tribological and vibration response of nanomaterial blended bio-oil lubricated bearing exhibited better results than that of mineral oil-lubricated bearing results.

Compensating thermal bending of the runner disk in hydrodynamic thrust bearings: Simple approach for passively improving the performance of gas bearings

This paper presents a method for passively compensating thermal bending distortions of the thrust disk in gas thrust bearings by passive approaches. Thermal bending displays a prominent problem in hydrodynamic thrust bearings, especially in connection with air bearings. Axial temperature gradients cause the rotor disk to dish which in turn diminishes the lubricating gap topology and reduces the load capacity of the bearing. Consequently, the bearing performance is decreased and the bearing may even fail. The here proposed straightforward solutions are small changes in the rotor and runner disk design and are therefore rather simple and inexpensive to adopt. They comprise asymmetric mass distributions, recesses, disk cuts, and dual-material rotor disks. In order to show the benefit for the thrust bearing performance, detailed multiphysical numerical simulations of a representative rotor assembly with a foil thrust bearing are presented. The fully coupled finite element model calculates the deformations of the foil package (2D Reissner–Mindlin type shell equations), lubricating air film pressure (2D compressible Reynolds equation), air film temperatures (3D energy equation), and thermo-elastic deformations of the rotor and rotor disk (2D Navier-Lamé equations including centrifugal forces and thermal stresses). When the proposed design changes are applied, the model predicts reduced thermal bending, marked improvements in load capacity as well as decreased temperatures. The load capacity increase ranges up to 43% for an optimized design when compared against a standard symmetric design. Also, the sensitivity of different design parameters is discussed in detail. The suggested disk design changes may be applied to a broad variety of rotor-bearing systems. However, the design has to be tailored to the specific and individual machine operating conditions.

A novel rolling bearing fault diagnosis method for limited data

The ability of deep learning based bearing fault diagnosis methods is developing rapidly. However, it is difficult to obtain sufficient and comprehensive fault data in industrial applications, and changes in vibration signals caused by machine operating conditions can also hinder the accuracy of the model. The problem of limited data and frequent changes in operating conditions can seriously affect the effectiveness of deep learning methods. To tackle these challenges, a novel transformer model named the Differential Window Transformer (Dwin Transformer), which employs a new differential window self-attention mechanism, is presented in this paper. Meanwhile, the model introduces a hierarchical structure and a new patch merging to further improve performance. Furthermore, a new fault diagnosis model dealing with limited training data is proposed, which combines the Auxiliary Classifier Generative Adversarial Network with the Dwin Transformer(DT-ACGAN). The DT-ACGAN model can generate high-quality fake samples to facilitate training with limited data, significantly improving diagnostic capabilities. The proposed model can achieve excellent results under the dual challenges of limited data and variable working conditions by combining Dwin Transformer with GAN. The DT-ACGAN owns superior diagnostic accuracy and generalization performance under limited sample data and varying working environments when compared with other existing models. A comparative test about cross-domain ability is conducted on the Case Western Reserve University dataset and Jiang Nan University dataset. The results show that the proposed method achieves an average accuracy of 11.3% and 3.76% higher than other existing methods with limited data respectively.

Intelligent process control system for predicting operating conditions of slag grinding machines: a data mining approach for improved efficiency and energy savings

This study develops an intelligent data-driven approach for optimising slag grinding systems. Slag grinding exhibits complex nonlinear dynamics that challenge control. The proposed system monitors key operating parameters to assess machine health and automate control adjustments. Operating data are collected, and features linked to health status are identified using data mining techniques. Cluster analysis categorises historical data into healthy/unhealthy modes to build a condition library. Real-time data are then evaluated against this library. A predictive model forecasts future trend. The system was implemented in an industrial slag mill. Results demonstrated reduced vibration and energy use versus manual control. Validation confirmed improved accuracy in predicting mill responses. Significant energy savings were achieved annually through optimised control. The system enhances safety by automating adjustments while minimising costs and environmental impacts. Data-driven strategies overcome the limitations of traditional methods, representing an advance for intelligent management of industrial processes. Benefits were confirmed through rigorous factory implementation and performance monitoring.

Theoretical basis and practical implementation of “bi-refrigerant” and “two-compressor” refrigeration machine schemes

The operating conditions of a refrigeration machine determine its energy efficiency. Some industries, particularly the food industry, are characterized by seasonal (short-term) consumption of cold in the low-temperature range (−40℃ and below) when rapid freezing of the product is required to achieve high quality because of finely dispersed crystallization of moisture and a “shock” temperature for psychrophilic microorganisms. To solve this problem, the periodic use of a low-temperature refrigerant, which, from a rational technical point of view, is not always combined with the constant required temperature of refrigerated storage because of possible operation under vacuum conditions and other difficulties, is advised. The difference between the temperature regime of the required short-term (seasonal or periodic) processing and the constant (main) temperature regime (e.g., refrigerated storage of products) often reaches 20℃ or more, which imposes certain restrictions on the design or operation of the refrigeration machine. In this regard, small and medium-sized refrigeration machines with the capability to operate on two refrigerants (e.g., low and medium temperatures, with periodic automatic mutual replacement) are promising. Such a refrigeration machine is referred to as “bi-refrigerant” in this study. The aforementioned difficulties are also solved by the proposed scheme of a “two-compressor” refrigeration machine, in which the possibility of its operation is realized during periodic change of modes from one stage to two stages, and vice versa, according to the need for the corresponding differences in boiling and condensation pressures.
 BACKGROUND: Environmental conditions and boiling point impose certain requirements on the selection of refrigerant and the design of the refrigeration machine, which often necessitates the division of the design period of operation of the refrigeration machine into two seasons, i.e., summer and winter, or the main operating mode (e.g., refrigerated storage) and nonessential (e.g., freezing seasonal or periodically supplied products to the refrigerator). This need may be especially acute in seasonal refrigerators and refrigerators operating under the conditions of large differences in ambient temperatures that periodically occur in some geographic areas. The need for a two-season (“bi-refrigerant”) refrigeration machine arises not due to fluctuations in the conversion coefficient under different environmental conditions (although this does occur) or a change in operating mode from the refrigeration cycle to the heat cycle (heat pump) but due to the seasonality of the supply of products subject to primary processing (quick freezing).
 AIMS: This work aims to provide theoretical justification for the practical implementation of the “bi-refrigerant” and “two-compressor” refrigeration machine schemes.
 MATERIALS AND METHODS: A theoretical investigation method was used to propose schemes for “bi-refrigerant” and “two-compressor” refrigeration machines, was used. The objects of the study were the diagrams of vapor compression refrigeration machines.
 RESULTS: A schematic diagram of a “bi-refrigerant” refrigeration machine operating by periodically replacing “seasonal” refrigerants has been developed. A schematic diagram of a “two-compressor” refrigeration machine, which provides the possibility of periodically changing a one-stage to a two-stage cycle, has been presented. A diagram of the automatic control of the proposed solutions is presented using the example of a “bi-refrigerant” refrigeration machine. Mathematical support and justification of the developed schemes are also presented.
 CONCLUSIONS: Our investigation revealed the theoretically justified possibility of using “bi-refrigerant” and “two-compressor” refrigeration machine schemes, e.g., in industries with seasonal short-term supply of short-term products subject to rapid (low-temperature) freezing or in other industries that require periodic changes in the boiling point of the refrigerant. Delaying factors in the application of the proposed “bi-refrigerant” refrigeration machine design may be the selection of compressor oil capable of operating on two refrigerants and the need to carefully solve the problem of circulation and its return to the compressor. However, this problem can be solved.

Machinery Prognostics and High-Dimensional Data Feature Extraction Based on a Transformer Self-Attention Transfer Network.

Machinery degradation assessment can offer meaningful prognosis and health management information. Although numerous machine prediction models based on artificial intelligence have emerged in recent years, they still face a series of challenges: (1) Many models continue to rely on manual feature extraction. (2) Deep learning models still struggle with long sequence prediction tasks. (3) Health indicators are inefficient for remaining useful life (RUL) prediction with cross-operational environments when dealing with high-dimensional datasets as inputs. This research proposes a health indicator construction methodology based on a transformer self-attention transfer network (TSTN). This methodology can directly deal with the high-dimensional raw dataset and keep all the information without missing when the signals are taken as the input of the diagnosis and prognosis model. First, we design an encoder with a long-term and short-term self-attention mechanism to capture crucial time-varying information from a high-dimensional dataset. Second, we propose an estimator that can map the embedding from the encoder output to the estimated degradation trends. Then, we present a domain discriminator to extract invariant features from different machine operating conditions. Case studies were carried out using the FEMTO-ST bearing dataset, and the Monte Carlo method was employed for RUL prediction during the degradation process. When compared to other established techniques such as the RNN-based RUL prediction method, convolutional LSTM network, Bi-directional LSTM network with attention mechanism, and the traditional RUL prediction method based on vibration frequency anomaly detection and survival time ratio, our proposed TSTN method demonstrates superior RUL prediction accuracy with a notable SCORE of 0.4017. These results underscore the significant advantages and potential of the TSTN approach over other state-of-the-art techniques.

Active Vibration Control of a Rotating Machine with Voltage-Driven Electrodynamic Actuators Between Machine Feet and Steel Frame Foundation Under Multiple Base Excitations

PurposeIn the paper, a theoretical analysis—based on a 2D multibody model—is presented regarding active vibration control of a rotating machine with voltage-driven electrodynamic actuators between machine feet and steel frame foundation under multiple base excitations.MethodsMathematical formulations are derived in the time domain and then transferred into the Laplace domain, where a state space formulation is used to describe the controlled vibration system. With this mathematical formulations, it is possible to analyze separately the two different kinds of actuator forces—the actuator forces, caused by the motion-induced voltage and the actuator forces caused by the control system—and their influence on the vibration behavior. Afterward, the mathematical formulations are transferred into the Fourier domain, for considering harmonic excitations of the base. Also a numerical example is presented, where different cases regarding machine mounting and operation conditions are investigated and compared to each other.ResultsIt could be clearly demonstrated that with the presented control system, most of the resonance peaks in the frequency responses could be strongly damped.ConclusionsWith the presented mathematical formulations, the controlled vibration system can be well described, considering separately the two different kinds of electrodynamic actuator forces and their influence.

Cross-domain privacy-preserving broad network for fault diagnosis of rotating machinery

The broad learning system-based data processing method has been widely used in the field of industrial intelligent operation and maintenance, and has achieved impressive results. However, data island created by privacy concerns in real industrial scenarios makes centralized deep training extremely difficult. In addition, the fluctuating operating conditions of machines including speed and load significantly affect the distribution of monitoring data representing the operating state of the device. These two problems make it difficult to apply broad learning system to practical application scenarios. Therefore, in view of the above problems, a novel cross-domain privacy-preserving broad network (CDPPBN) for cross-domain fault classification of rotating machinery is presented. Specifically, a cross-domain interactive data encryption protocol feature extractor is designed firstly to ensure the privacy and security of both source and target domain data during feature extraction process. Then, a domain adaptation target classifier is constructed to achieve the fault classification task in the target domain without labels. In this classifier, a maximum mean discrepancy regularization term is constructed to match the mean projections of the source and target domains to adapt the source classifier to the target domain, and a semi-supervised local graph embedding regularization term is constructed to mine the structural features of the target domain data. CDPPBN not only has high performance in terms of data privacy protection, but also makes full use of both labeled source data and unlabeled target data to generate a domain adaptation target classifier. The effectiveness of the presented method is verified through bearing monitoring signal. Experimental analysis shows that the proposed CDPPBN has better data privacy protection capability and data cross-domain diagnosis ability compared with other methods.

Robust deep learning-based fault detection of planetary gearbox using enhanced health data map under domain shift problem

Abstract The conventional deep learning-based fault diagnosis approach faces challenges under the domain shift problem, where the model encounters different working conditions from the ones it was trained on. This challenge is particularly pronounced in the diagnosis of planetary gearboxes due to the complicated vibrations they generate, which can vary significantly based on the system characteristics of the gearbox. To solve this challenge, this paper proposes a robust deep learning-based fault-detection approach for planetary gearboxes by utilizing an enhanced health data map (HDMap). Although there is an HDMap method that visually expresses the vibration signal of the planetary gearbox according to the gear meshing position, it is greatly influenced by machine operating conditions. In this study, domain-specific features from the HDMap are further removed, while the fault-related features are enhanced. Autoencoder-based residual analysis and digital image-processing techniques are employed to address the domain-shift problem. The performance of the proposed method was validated under significant domain-shift problem conditions, as demonstrated by studying two gearbox test rigs with different configurations operated under stationary and non-stationary operating conditions. Validation accuracy was measured in all 12 possible domain-shift scenarios. The proposed method achieved robust fault detection accuracy, outperforming prior methods in most cases.

Fault diagnosis of machines operating in variable conditions using artificial neural network not requiring training data from a faulty machine

The fault diagnosis for maintenance of machines operating in variable conditions requires special dedicated methods. Variable load or temperature conditions affect the vibration signal values. The article presents a new approach to diagnosing rotating machines using an artificial neural network, the training of which does not require data from the damaged machine. This is a new approach not previously found in the literature. Until now, neural networks have been used for machine diagnosis in the form of classifiers, where data from individual faults were required. A new diagnostic parameter rDPNS (Relative Differences Product of Network Statistics) as a function of the machine's shaft order was proposed as a kind of new order spectrum independent of the machine's operating conditions. The presented work analyses the use of the proposed method to diagnose misalignment and unbalance. The results of an experiment carried out in the laboratory demonstrated the effectiveness of the proposed method.

Experimental Study of the Thermal and Wear Characteristics of a Foil Bearing Lubricated with a Low-Boiling Liquid

Developing high-speed rotating machines, such as microturbines, requires new solutions for bearing systems. Foil bearings are one of the fastest-growing high-speed bearing technologies. This article presents the results of experimental studies on foil bearings conducted on a test rig reflecting the operating conditions of the target machine. The tested bearings were therefore placed in a chamber filled with a low-boiling medium used as a working fluid in vapour microturbines. The experiments were carried out for several test cycles following a repeatable procedure. To carry out the tests under specific environmental conditions, a measurement cycle according to the developed test procedure and a temperature stabilization system for long-term measurements were proposed. The research involved determining the thermal characteristics of four foil bearing variants and assessing the impact of operating conditions on their wear. Additional research has shown that the operating characteristics are highly dependent not only on the materials and geometry of the bearing but also on the surface roughness of the mating parts. This study is part of ongoing work to select appropriate designs and material packages for foil bearings operating under conditions involving lubricating film formation and heat dissipation.

A federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation for rolling bearing fault diagnosis

Intelligent mechanical fault diagnosis techniques have been extensively developed in recent years. Owing to the advantage of data privacy protection, federated learning has recently received increasing attention; this approach can utilize monitoring data from multiple local clients to train an optimal global diagnosis model. However, low-quality data are often present for some clients, including mislabeled data and incomplete data that lack some health states. Furthermore, the data distributions are usually different across different clients owing to variations in machine operating conditions. Therefore, the performance of the federated diagnostic model may be limited by low-quality data and data distribution discrepancies. To address this issue, a federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation is proposed. First, a dynamic filter is designed by filtering out low label probabilities predicted by low-quality source domain models to construct high-confidence pseudo-labels for the target domain data. Then, the batch normalized maximum mean discrepancy (BN-MMD) distance metric is introduced into the training loss function to reduce the data distribution discrepancy between the source clients and the target client without private data leakage. While building the global model in each training round, a dynamic model aggregation algorithm is proposed to mitigate the influence of low-quality clients. This algorithm evaluates the weight of each client according to its contribution to the total consensus diagnostic knowledge and then aggregates the local models with adaptive weights. Consequently, it can overcome the drawback of the classical federated averaging (FedAvg) algorithm, where all local clients are assigned the same weight when constructing the global model. Experiments are conducted on three bearing datasets under various loads and speeds. Compared with some existing diagnostic methods, the proposed federated transfer learning method can reduce the impact of low-quality data and achieve higher diagnostic accuracy while preserving data privacy.

Study of leakage flow in oil-free positive displacement rotary machines using particle image velocimetry

Rotary positive displacement machines (PDM) are widely used in industry. Oil free PDM’s are used in applications where presence of oil is not allowed, but they still are not much utilised due to challenges with reliability and efficiency related to extensive thermal loads. Their efficiency is important to meet carbon footprint targets while increasing reliability will allow their wider use. The main contributor to losses is the internal leakage through clearance gaps between stationary and rotating parts driven by the differential pressure between chambers. Due to the transient nature of leakage flows in running conditions, the measurement of velocity flow field inside the clearance gap is challenging but can provide significant information about flow physics. Thus, understanding of the velocity flow field is essential for developing methods to increase efficiency without compromising reliability.The flow field in clearance gaps can be captured using Particle Image Velocimetry (PIV) for optically accessible flows. The basic PIV system consists of a light source, camera, tracer particles, and analysis algorithms. In this study the Roots blower is used as a representative of the oil-free PDM for which the velocity field is measured inside the radial clearance gap in the running condition using the PIV technique. An extensive testing campaign has been carried out at various speeds and pressure ratios of the machine. PIV measurements were taken inside, upstream, and downstream of the clearance gap. Post-processing of PIV images allows the generation of velocity fields. These pioneering measurements of the velocity field inside the clearance in the machine operating conditions reveal interesting flow physics. Moreover, the large amount of collected experimental data provides an excellent basis for validation of simulation models and further developments.

An Inequality Indicator for High-Resistance Connection Fault Diagnosis in Marine Current Turbine

Marine current energy is an abundant renewable energy resource. Marine current turbines (MCTs) can convert the kinetic energy of marine currents into electrical energy. However, the variations in the marine currents are violent and complex. These characteristics will be reflected in the variation in the operating condition of MCTs, thus interfering with normal diagnosis for the high-resistance connection (HRC). The HRC can be caused by damaged connections between device components that are easily made due to the harsh marine environment. To diagnose HRC in MCT, an inequality indicator is proposed to quantify the current imbalance caused by HRC. The inequality indicator is defined based on the arithmetic–geometric mean inequality and can identify slight current imbalances in the early stages of HRC. The inequality indicator is robust to the variable operating conditions of the permanent magnet synchronous machine (PMSM) in the MCT. Experimental results show that the inequality indicator can be used to effectively diagnose HRC in the MCT with 100% accuracy. This research will help maintain the health condition of the MCTs and provide some ideas for diagnosis in MCTs. Moreover, the inequality indicator may provide a different approach to the analysis for other faults that can lead to a current imbalance.

Kinodynamic Generation of Wafer Scanners Trajectories Used in Semiconductor Manufacturing

The operation time of an ideal reliable wafer scanner model is defined at the die level where the actual exposure process takes place as the time unit per die, or at the wafer substrate level as the time unit per wafer substrate. Therefore, the machine throughput is given as the reciprocal of the operation time. The involved motion profiles of a machine, namely the step-and-scan trajectories, function as the heartbeats that drive its multidisciplinary elements, which suggests that a multidisciplinary design optimization should be involved when such profiles are selected or designed. This is also true when considering the traverse motion profiles among rows and columns within the wafer substrate. The step-and-scan trajectories affect the machine throughput, performance, and die yield. The effects of tracking such profiles appear as structural vibration, tracking errors, and thermal loading at various machine elements such as the actuators, the reticle, the wafer, and the projection elements specifically when the exposure high-energy duration and frequency are not taken into consideration while designing the reference motion. From the dynamics perspective, having a reference motion with nonzero and bounded higher-order derivatives is recommended since it enhances the tracking performance of the machine, however, its ability to increase the operation time is usually overlooked. In an attempt to understand such effects, we present a case study that outlines the aforementioned aspects using three step-and-scan profiles of mainly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3^{rd}$ </tex-math></inline-formula> -order. Taking the dynamics of the driven stage into consideration through input shaping, both the step-and-scan and traverse motion profiles are analyzed. We provide analytical expressions that can be used to generate both types of motion profiles on the fly without additional optimization. A simulation example of a simplified wafer scanner machine shows the usefulness of the proposed framework. Note to Practitioners—Choosing the most suitable operating conditions of a lithography machine is challenging. These conditions affect machine productivity, profit margin, and maintenance. In this paper, we reveal the relation between the selection of operating conditions based on several decision variables- and the kinodynamic step-and-scan trajectory generation based on specific machine parameters and clients’ requirements. Being chart-based, the selection process of an operating point can be less practical at some points. However, using appropriate curve fitting tools, the information provided in the optimal operating charts can be put into suboptimal closed-form expressions that facilitate the selection process. Therefore, the designed trajectories parameters can be easily saved in lookup tables for ease of evaluation and future use. This helps in accommodating changes in the operation plans and flexible manufacturing systems. Also, starting with a given set of machine parameters, it is possible to calculate the optimal machine operating point when the input shaping technique is used, as illustrated in this paper.