Complex Operators Research Articles

Development of a methodology for diagnosing faults in bearings operating under variable operating conditions based on self-supervised learning

Predictive maintenance plays a crucial role in ensuring the efficiency and availability of industrial assets. Bearings, essential components in rotating machinery, are subject to diverse and complex operating conditions, necessitating advanced fault diagnosis methods. Traditional diagnostic approaches often rely on supervised learning, which requires extensive labeled datasets, a process that is both costly and impractical under varying conditions. This work proposes a novel methodology for diagnosing bearing faults using self-supervised learning, which leverages unlabeled data to generate useful representations for fault detection. The proposed approach aims to develop an end-to-end system that processes raw vibration signals to accurately diagnose the current state of bearings, including fault detection, localization, and severity assessment. The methodology is validated using experimental data from a test rig simulating various fault conditions and will be further tested on real industrial machinery. This research contributes to the development of more efficient and generalizable diagnostic tools for rotating machinery, particularly under variable operational conditions.

Supersymmetric Quesne-Dunkl Quantum Mechanics on Radial Lines

Quantum deformations offer valuable perspectives into quantum mechanics, particularly by advancing our understanding of symmetry and algebraic structures.The Dunkl oscillator, which integrates Dunkl operators into the harmonic oscillator framework, advances the system’s algebraic properties and opens new approaches for exploring quantum phenomena. Supersymmetric quantum mechanics (SSQM) unifies bosonic and fermionic aspects and facilitates the construction of solvable models using generalized Dunkl operators. This paper introduces a new approach to the Dunkl oscillator, employing a complex reflection operator to deepen the understanding of its connection to Hermite polynomials on radial lines. The results offer new perspectives on the Dunkl oscillator’s algebraic structure and its relevance to SSQM and quantum deformation theory, expanding the potential for discovering solvable quantum models.

Commutator technique for the heat kernel of minimal higher derivative operators

We suggest a new technique of the asymptotic heat kernel expansion for minimal higher derivative operators of a generic 2Mth order, F(∇)=(−□)M+⋯, in the background field formalism of gauge theories and quantum gravity. This technique represents the conversion of the recently suggested Fourier integral method of generalized exponential functions [A. O. Barvinsky and W. Wachowski, Heat kernel expansion for higher order minimal and nonminimal operators, ] into the commutator algebra of special differential operators, which allows one to express expansion coefficients for F(∇) in terms of the Schwinger-DeWitt coefficients of a minimal second-order operator H(∇). This procedure is based on special functorial properties of the formalism including the Mellin-Barnes representation of the complex operator power HM(∇) and naturally leads to the origin of generalized exponential functions without directly appealing to the Fourier integral method. The algorithm is essentially more straightforward than the Fourier method and consists of three steps ready for a computer codification by symbolic manipulation programs. They begin with the decomposition of the operator into a power of some minimal second-order operator H(∇) and its lower derivative perturbation part W(∇), F(∇)=HM(∇)+W(∇), followed by considering their multiple nested commutators. The second step is the construction of special local differential operators—the perturbation theory in powers of the lower derivative part W(∇). The final step is the so-called procedure of their “Syngification,” consisting of a special modification of the covariant derivative monomials in these operators by the Synge world function σ(x,x′) with their subsequent action on the Hadamard-Minakshisundaram-DeWitt coefficients of H(∇). Published by the American Physical Society 2024

Advanced Hybrid Optimization Algorithms for Energy Efficient Cloud Resource Allocation

AbstractCloud computing is highly sought after for its dynamic resource allocation capabilities and pay‐per‐use model. However, previous research has identified several challenges, such as lower coverage, high integration rates, longer computation times, and complex operators, all of which are associated with NP‐hard problems. These issues negatively impact the efficiency of resource allocation and scheduling, leading to slower processes, inefficiencies in multi‐objective optimization, lower throughput, and higher power consumption. To address these challenges, we propose a unique Hybridized Optimization Algorithm that integrates Crow Swarm Optimization (CSO), Cuckoo Search Optimization (CSO), and Cat Hunting Optimization (CHO). Initially, Particle Swarm Optimization (PSO) and the Crow Search Algorithm (CSA) handle the exploitation and exploration phases to balance task loads. Subsequently, Cuckoo Search Optimization (CSO) enhances resource utilization and addresses NP‐hard issues, while Cat Hunting Optimization (CHO) refines the search from global to local optimal spaces to achieve the best values. The results demonstrate that the proposed hybrid technique effectively reduces user request waiting times, lowers energy consumption, and decreases execution times on cloud servers compared to baseline approaches, thereby significantly improving overall system performance.

Weak Compound Fault Identification of Gearboxes Based on Improved Symplectic Geometric Mode Decomposition and Optimized Cyclic Kurtosis Deconvolution

Abstract Given the complex and harsh operating conditions of gear transmission systems, gearboxes are prone to compound faults. These faults, involving multiple types, often couple together, causing the weak fault pulse characteristics to be entirely masked by strong environmental noise. This significantly complicates the extraction of relevant fault information from the gearbox. To address these issues, this paper proposes a method for extracting compound fault features based on improved symplectic geometric mode decomposition (SGMD) and optimized cyclic bispectrum deconvolution (CYCBD). Firstly, considering the periodic impact characteristics of different fault types, morphological envelope cyclic bispectrum is proposed to cluster the initial components obtained from SGMD decomposition, thereby adaptively separating different fault features contained in the compound fault signals. An adaptive filter length search strategy is subsequently introduced to optimize the CYCBD by deconvolving each initial fault component, thereby eliminating the interference caused by complex transmission paths and substantial environmental noise, which, in turn, enhances weak periodic fault pulses. Following this, the enhanced signals are subjected to envelope demodulation to extract fault characteristic frequencies, enabling the identification of various types of faults. The effectiveness and feasibility of the proposed method are demonstrated through both simulation signals and actual experimental data related to gearbox compound faults. Compared with existing methods, the proposed method demonstrates superior performance in identifying weak compound faults under strong environmental noise.

A mMSA-FOFPID controller for AGC of multi-area power system with multi-type generations

The exceptional growth in the penetration of renewable sources as well as complex and variable operating conditions of load demand in power system may jeopardize its operation without an appropriate automatic generation control (AGC) methodology. Hence, an intelligent resilient fractional order fuzzy PID (FOFPID) controlled AGC system is presented in this study. The parameters of controller are tuned utilizing a modified moth swarm algorithm (mMSA) inspired by the movement of moth towards moon light. At first, the effectiveness of the controller is verified on a nonlinear 5-area thermal power system. The simulation outcomes bring out that the suggested controller provides the best performance over the lately published strategies. In the subsequent step, the methodology is extended to a 5-area system having 10-units of power generations, namely thermal, hydro, wind, diesel, gas turbine with 2-units in each area. It is observed that mMSA based FOFPID is more effective related to other approaches. In order to establish the robustness of the controller, a sensitivity examination is executed. Then, experiments are conducted on OPAL-RT based real-time simulation to confirm the feasibility of the method. Finally, mMSA based FOFPID controller is observed superior than the recently published approaches for standard 2-area thermal and IEEE 10 generator 39 bus systems.

Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation

The battery systems of electric vehicles (EVs) are directly impacted by battery temperature in terms of thermal runaway and failure. However, uncertainty about thermal runaway, dynamic conditions, and a dearth of high-quality data sets make modeling and predicting nonlinear multiscale electrochemical systems challenging. In this work, a novel Mamba network architecture called BMPTtery (Bidirectional Mamba Predictive Battery Temperature Representation) is proposed to overcome these challenges. First, a two-step hybrid model of trajectory piecewise–polynomial regression and exponentially weighted moving average is created and used to an operational dataset of EVs in order to handle the problem of noisy and incomplete time-series data. Each time series is then individually labeled to learn the representation and adaptive correlation of the multivariate series to capture battery performance variations in complex dynamic operating environments. Next, a prediction method with multiple steps based on the bidirectional Mamba is suggested. When combined with a failure diagnosis approach, this scheme can accurately detect heat failures due to excessive temperature, rapid temperature rise, and significant temperature differences. The experimental results demonstrate that the technique can accurately detect battery failures on a dataset of real operational EVs and predict the battery temperature one minute ahead of time with an MRE of 0.273%.

Quaternionic essential numerical range of complex operators

We study the essential numerical range of complex operators on a quaternionic Hilbert space and its relation with the essential S-spectrum. We give a new characterization of the essential numerical range relating it to the complex essential numerical range. Moreover, we show that the quaternionic essential numerical range of a normal operator is the convex hull of the essential S-spectrum.

High Stability Control of a Magnetic Suspension Flywheel Based on SA-BPNN and CNN+LSTM+ATTENTION

Compared to traditional, static-based flywheel systems, vehicle-mounted magnetic suspension flywheels face more complex operating conditions, and existing control strategies usually regard disturbances in vehicles under different operating conditions to be the same problem. Therefore, it is necessary to determine the interference from complex operating conditions and reasonably distinguish among them under different operating conditions to provide flywheel systems with strong stability (the rotor offset was less than 0.025 mm). Thus, this paper proposes a high-stability control strategy for flywheels based on the classification of vehicle-driving conditions and designs its control strategy by taking the vehicle-mounted magnetic suspension flywheel with a virtual inertia spindle as an example. First, according to the different vehicle working conditions and the varying interference intensities affecting the flywheel system, the working mode is divided into four modes. Considering the obvious differences in each working mode, it is proposed to use BP neural network optimization based on the simulated annealing algorithm (SA-BPNN) to determine the flywheel’s working condition. A relatively simple neural network can improve the response speed of the whole system. It also has a good effect. Secondly, it is proposed to use deep learning models based on convolutional neural networks, long short-term memory networks and attention mechanisms (CNN+LSTM+ATTENTION) to train the corresponding control parameters under each working condition to judge and predict the control parameters under different working conditions. Three evaluation parameters are used to evaluate the training results, and all achieved good results. Finally, the classification of working conditions and performance tests are carried out. The experimental results show the effectiveness and superiority of the proposed control strategy.

Effect of radiation on lubrication performance and the consequences for mechanical equipment at CERN

Successful operation of the accelerator complex at CERN relies on Beam Intercepting Devices (BIDs) which are exposed to high doses of radiation and severely restricted maintenance access over their operating life. Instances of lubrication problems have been observed, such as high friction, overheating, corrosion and, in extreme cases, seizure. Understanding the root causes and the mechanisms controlling such failures is key to avoid downtime and improve maintenance schedules and processes. The paper reviews radiation degradation of lubricants and examines their tribological implications. Several examples of lubrication problems observed at CERN are investigated, and preliminary explanations are provided in the light of tests performed on irradiated samples. This highlights the need for better testing protocols and a thorough assessment of the time-varying performance of lubricants operating in the presence of radiation.

The physical information LSTM surrogate model for establishing a digital twin model of reciprocating air compressors

Reciprocating air compressors play a crucial role in industrial production processes. However, due to the complex structure and long operating time of reciprocating air compressors, real-time monitoring to grasp the operating status of reciprocating air compressors has become particularly important. Digital twin is a technology that can reflect the behavior of physical entities in real time, accurately predicting and evaluating the operation status of reciprocating air compressors. However, the establishment of a digital twin model for reciprocating air compressors requires a significant amount of computational resources, which can result in the inability to meet the requirements of real-time performance evaluation. To overcome this limitation, this paper proposes a method for constructing a digital twin model of reciprocating air compressors based on a surrogate model. The surrogate model is constructed based on a long short-term memory neural network with physical information(PILSTM). This model can accurately describe the changes in cylinder pressure by combining physical information. According to the characteristics of cylinder pressure changes, regularization formulas are added to ensure the smoothness of the predicted pressure. The experimental results show that the digital twin model based on the surrogate model has high prediction accuracy and real-time performance. Therefore, this model provides a new method for monitoring the operating status of reciprocating air compressors.

Fault location method for new distribution networks based on waveform matching of time–frequency travelling waves

AbstractSome existing fault location methods for distribution networks rely too much on the local wave head information of the time or frequency domain signals, making it difficult to adapt to the increasingly complex structure and operating conditions of the distribution network after the new energy access. For improvement, the travelling wave (TW) time and frequency ranges that can effectively avoid waveform distortion caused by new energy access are analysed. The one‐to‐one matching relationship between the TW waveforms in these ranges and the fault positions is revealed. A fault TW time–frequency matrix with specific time and frequency windows is constructed, and a new distribution network fault location method is proposed based on the matching technique of waveform features, which realises the accurate fault location by exploring the proportionality between the cumulative trend of the matrix energy amplitude deviation and the fault point position. Simulation test results show that the proposed method is not affected by the complex structure of distribution networks such as new energy access and overhead‐cable line mixing on fault location and flexibly transforms the fault location problem into a time–frequency TW waveform matching problem, which improves the accuracy and robustness of the fault location for new distribution networks to a degree.

Adaptive fuzzy PI control for the dynamic operation of the transcritical CO2 thermal system in electric vehicles

Process control is essential for ensuring the stable and efficient operation of EVs’ transcritical CO2 thermal management systems. Traditional PI control generally shows an insufficient dynamic response for the variable and complex operating conditions and then causes fluctuant process control. This study proposed an adaptive fuzzy PI control strategy to provide real-time varying PI gains for fast-tracking response and fluctuation suppression, considering the compressor discharge pressure, coolant, and air outlet temperature. A high-fidelity transcritical CO2 thermal model in EVs was first built in the Amesim platform to simulate real-time operation, and the adaptive fuzzy PI control algorithm was then developed to optimize the process control. The co-simulation is conducted to validate the effectiveness and adaptability of the proposed control methodology by comparing it with a benchmark traditional PI control strategy. The results indicated that the proposed adaptive fuzzy PI could provide a 9.22 % overshoot suppression for discharge pressure, a maximal 22.29 % overshoot inhibition and 86 s settling time reduction for the air outlet temperature, and a 25 % settling time reduction for the coolant temperature control. In addition, the fuzzy PI improved the start-up characteristics by avoiding excessive response. Such findings demonstrate that the proposed fuzzy PI control algorithm can effectively optimize the dynamic operation of the transcritical CO2 thermal system in EVs.

Stakeholder Opinion Survey and Analysis to Establish Priorities for the Eco-Friendly Design and Operation of Healthcare Plant Production Complexes

Stakeholder Opinion Survey and Analysis to Establish Priorities for the Eco-Friendly Design and Operation of Healthcare Plant Production Complexes

Fault identification of rolling bearing based on improved salp swarm algorithm

Due to the rapid development of industrial manufacturing technology, modern mechanical equipment involves complex operating conditions and structural characteristics of hardware systems. Therefore, the state of components directly affects the stable operation of mechanical parts. To ensure engineering reliability improvement and economic benefits, bearing diagnosis has always been a concern in the field of mechanical engineering. Therefore, this article studies an effective machine learning method to extract useful fault feature information from actual bearing vibration signals and identify bearing faults. Firstly, variational mode decomposition decomposes the source signal into several intrinsic mode functions according to the actual situation. The vibration signal of the bearing is decomposed and reconstructed. By iteratively solving the variational model, the optimal modulus function can be obtained, which can better describe the characteristics of the original signal. Then, the feature subset is efficiently searched using the wrapper method of feature selection and the improved binary salp swarm algorithm (IBSSA) to effectively reduce redundant feature vectors, thereby accurately extracting fault feature frequency signals. Finally, support vector machines are used to classify and identify fault types, and the advantages of support vector machines are verified through extensive experiments, improving the ability of global search potential solutions. The experimental findings demonstrate the superior fault recognition performance of the IBSSA algorithm, with a highest recognition accuracy of 97.5%. By comparing different recognition methods, it is concluded that this method can accurately identify bearing failure.

Анализ технологических процессов погрузочно-разгрузочных, транспортных, перегрузочных и складских операций на основе контейнерных перевозок

The article discusses key technological operations, including loading and unloading, transportation, transshipment and storage of goods in containers, as well as analyzes modern approaches to managing and optimizing these processes. The main attention is paid to the automation and mechanization of loading and unloading operations, the use of multimodal transportation to improve the efficiency of transport processes, the development of strategies for optimizing routes and logistics schemes, as well as the use of modern information technologies for cargo tracking and inventory management. The authors of the article emphasize the importance of integrating logistics operations and creating a unified information system for coordinating all stages of container transportation. As a result of the analysis, ways to improve technological processes aimed at reducing costs, increasing the speed of cargo delivery and increasing the overall efficiency of logistics chains are proposed. The article contains recommendations on the introduction of innovative solutions and technologies that can help optimize the work of enterprises engaged in container transportation. The research is based on the analysis of real data, including statistics on the operation of ports, transport companies and warehouse complexes, as well as on the study of best practices in this area. The article provides examples of successful implementation of the proposed approaches in practice, which makes it useful for specialists in the field of logistics, supply chain management and transportation.

On logic gates with complex numbers

Logic gates can be written in terms of complex differential operators, where the inputs and outputs are holomorphic functions with several variables. Using the polar representation of complex numbers, we arrive at an immediate connection between the oscillatory behavior of the system and logic gates. We discuss the universality of this formalism in a variety of computing systems.

Digital Twin-driven Health Operation and Maintenance Strategies for Complex Equipment with Self-healing Capabilities

With the development of the industrial level, the service-oriented transformation of the manufacturing industry has become a new growth point for the interests of the manufacturing industry recognized by all countries, and in this transformation process, the health operation and maintenance (HOM) of equipment has become a crucial link, especially for complex equipment, and its health operation and maintenance level directly affects the overall efficiency of the manufacturing industry. However, with the improvement of intelligence and complexity, a more general and complete set of methods and theories is needed for the healthy operation and maintenance of complex devices with self-healing states. Based on an in-depth analysis of the key issues of complex equipment health operation and maintenance, this paper introduces digital twin technology as a breakthrough. Digital twins provide new ideas for the healthy operation and maintenance of complex equipment. By defining a digital twin-driven PHM (Prediction and Health Management) framework, this paper not only solves the problem of mobility (i.e., virtual and real connection) of the cross-platform health operation and maintenance model, but also focuses on improving the accuracy and evaluation indicators of the model (the consistency between the virtual model and the entity). Addressing issues of strategy development and effectiveness evaluation. Based on the digital twin technology, a general model was established, and the self-healing phenomenon and different maintenance strategies were introduced to explore its impact on reliability. Based on the game idea, the reliability model is used to evaluate different maintenance strategies. So as to develop the optimal operation and maintenance strategy. This not only improves the accuracy and efficiency of O&M, but also provides a solid theoretical foundation and technical support for the intelligent and autonomous health O&M of complex equipment.

ORGANIZATION OF INTERNATIONAL FREIGHT TRANSPORTATION IN EXTREME CONDITIONS OF TODAY: PROBLEMS AND TRENDS

Today, road transport in Ukraine remains among the leaders in terms of the share of cargo transportation both in domestic and international traffic. In the current conditions of globalization, increasing the efficiency of cargo delivery is one of the priority areas for reducing the costs of industrial organizations, trade and services. Stable and efficient operation of the transport complex is an important condition for the life support of the diversified economy and the implementation of the main directions of programs for the socio-economic development of Ukraine. The international transport market is characterized by strict requirements for the quality of transport services, which causes the high complexity of transportation process management. That is why enterprises in the transportation industry must constantly work on improving the international transportation of goods by road. The main aspect of these studies is freight transportation, methods and methods of organizing the integration of the Ukrainian economy into the world economy under modern conditions. The purpose of the study is to study theoretical, scientific and methodological approaches and practical aspects of solving the problems of organizing freight transportation in the context of deepening international cooperation between Ukraine and Poland. Achieving this goal involves solving a set of interrelated tasks:. In particular, to study the experience of Poland in the organization of freight transportation in the context of deepening international cooperation; to study the essence of the organization of freight transportation and to analyze the indicators of production and economic activity and international economic relations of the enterprise and to study the state and quality of the organization of freight transportation in the context of deepening international cooperation, in particular FOP "Alekseevets S.M."; to identify the problems of organizing freight transportation in the context of deepening international cooperation and to provide recommendations for the organization of freight transportation in the context of deepening international cooperation".

A multipoint prediction model for the deformation of concrete dams considering climatic features of high-altitude regions

High-altitude regions are characterized by natural climatic features such as low temperatures and high radiation. Concrete dams built in these regions face more complex operating conditions. Traditional single-output deformation prediction models ignore the correlations between different monitoring points, making it difficult to assess the overall deformation behavior of concrete dams. The black-box nature of some machine learning models can lead to a lack of interpretability, affecting the practical application. Therefore, a multipoint prediction model (MPM) is proposed to analyze the deformation behavior of concrete dams in high-altitude regions. Firstly, the deformation monitoring points are divided based on the climatic features of high-altitude regions. Subsequently, the MPM is constructed using the multi-output CatBoost model and Quasi-Monte Carlo algorithm to achieve deformation prediction across different partitioned monitoring points. Finally, the elucidation of the MPM optimization procedure in a transparent manner alongside the quantification of the influence exerted by input variables on output outcomes notably augments the interpretive capacity of the MPM. The proposed model is validated using monitoring data from a concrete dam in a high-altitude region. The disparities in chaotic and entropy variation features among deformation monitoring points in different zones corroborate the appropriateness of the zoning in this study. Comparisons between the single-output CatBoost model and MPM verify the feasibility and efficiency of multipoint predictions. Comparisons with other multi-output prediction models validate the superior predictive performance of the MPM. Additionally, the proposed model's generalization performance is validated using data from another concrete arch dam in high-altitude regions.