Condition Monitoring Technology Research Articles

Fault Simulating Test Bed for Developing Diagnostic Algorithm of the Geared Rotating Machinery of Ships

To prevent critical failure of the functional machinery of a ship, condition monitoring technologies have been much studied in recent times. In this respect, securing a fault database is a top priority in technology development. In this paper, we developed a test bed that simulates the LNG (liquefied natural gas) re-liquefaction system installed on LNG carriers to obtain data in various types of faults of ship machinery. To maintain rotor-dynamics characteristics, the structure was scaled based on the critical speed margin of the dynamic system. The developed test bed includes a gearbox and multiple shafts. It can simulate mass imbalance, misalignment, bearing fault, gear fault and impeller fault. To verify the validity of the vibration data obtained from the developed test bed, experiments were conducted on three fault modes: main shaft imbalance, pinion shaft imbalance, and gear fault. The time series data and FFT results were analyzed, and time domain features were extracted and statistically validated. Additionally, a simple diagnosis model was developed using the acquired data to evaluate its performance. The test data show distinct data with respect to fault conditions, and we can expect that the diagnosis algorithm can be developed using the test data. The developed test bed can provide not only for the fault data of a single component of the rotating machine but also for the combined fault data of the total system. In addition, we expect that it will solve the problem of securing fault data in the development of condition diagnosis technology if reliability is verified by identifying correlations by comparing data from the real system and data from the scaled test bed.

Meta-learning-based approach for tool condition monitoring in multi-condition small sample scenarios

Tool Condition Monitoring (TCM) technology in machining is crucial for maintaining safety and optimizing costs. However, its practical application faces two significant challenges: difficulties in data collection and a decline in generalization performance across different monitoring tasks. To this end, a hybrid feature boundary-enhanced meta-learning network with adaptive gradients (HFBEAML) is proposed. This method combines cutting process parameters with time-series signal features, employing a multimodal one-dimensional Convolutional Neural Network (1D-CNN) based on the DenseNet architecture and a multi-head self-attention mechanism (MHSA) to mine multi-dimensional features sensitive to tool wear. To further enhance the model’s feature discrimination capability, a multi-loss joint optimization strategy is introduced, combining task-level loss with an improved triplet loss. This approach incorporates strategies for adaptive boundaries and handling the hardest positive and negative sample sets, thereby enhancing the robustness of feature metrics. Additionally, this research innovatively proposes an adaptive meta-level gradient update mechanism, dynamically adjusting gradient weights according to the characteristics of multiple tasks, aiming to improve the model’s generalization ability in multi-task learning environments. The effectiveness of the proposed method is demonstrated through experiments in two different scenarios, comparing its results with five other models showcasing its significant advantages in multi-task, small-sample data monitoring environments.

An Improved Analytical Thermal Rating Method for Cable Joints

To improve the utilization rate of cable lines while retaining sufficient security, the accurate thermal assessment of cable is significant for cable operation condition evaluation. The thermal rating for a cable joint, which is regarded as a hot spot of cable lines, is not covered by the scope of IEC 60287. While the existing publications for cable joint thermal evaluation also have some limitations. In this paper, the quasi-three-dimensional thermal model of the cable joint was established and the iterative solution method for the model is presented. Based on the model, an improved thermal rating method for the cable joint was proposed, which was implemented with monitored surface temperature and load data. The improved method was verified by the finite element method and the results showed an error of less than 5%. The superiority of the improved method was conducted by the comparison between the previously published method and the improved method. The improved method showed a better accuracy than the previously published method. The proposed method in this paper can be complementary to the IEC method, and is easy to use for the operating evaluation of cable joints in the field with the on-line condition monitoring technology.

Analysis on the Application of Artificial Intelligence in the Field of Logistics

This paper discusses the wide application of artificial intelligence in the logistics industry, from intelligent distribution to intelligent transportation, through route optimization, automated warehouse management, demand forecasting, inventory management and other means, greatly improve logistics efficiency and reduce costs. Intelligent transport enhances traffic management, ensures road safety and optimizes resource utilization with road condition monitoring, autonomous driving and freight optimization technologies. In the face of challenges such as data security and privacy protection, artificial intelligence continues to promote the transformation of the logistics industry to a smarter and more efficient form. In the future, artificial intelligence will be further integrated into the logistics field, and through data-driven intelligent logistics management and applications in international logistics, more refined supply chain management, better resource scheduling and higher customer service standards will be achieved.

Adsorption Properties of Dissolved Gas Molecules in Transformer Oil on the ReSe2 Monolayer: A DFT Study.

Based on density functional theory, the adsorption behavior of seven typical dissolved gas molecules (CO, CO2, H2, CH4, C2H2, C2H4, and C2H6) and H2O molecule on the ReSe2 monolayer was systematically investigated. The interactions between the ReSe2 monolayer and eight gas molecules were investigated by calculating the adsorption energies, charge transfer, density of states (DOS), and deformation charge density (DCD) for eight different adsorption systems. The gas sensitivity of the ReSe2 monolayer toward these gases was studied using frontier molecular orbital theory and work function analysis. The results demonstrate that compared to other gas molecules, the ReSe2 monolayer exhibits a stronger interaction with CO with an adsorption energy of -1.49 eV. It also displays excellent sensitivity and selectivity toward CO making it a promising candidate for CO gas sensing applications. We aspire that this research will offer theoretical guidance for the development of ReSe2-based gas sensors and contribute to state monitoring technology in oil-immersed power equipment.

A Review of Capacity Decay Studies of All-vanadium Redox Flow Batteries: Mechanism and State Estimation.

As a promising large-scale energy storage technology, all-vanadium redox flow battery has garnered considerable attention. However, the issue of capacity decay significantly hinders its further development, and thus the problem remains to be systematically sorted out and further explored. This review provides comprehensive insights into the multiple factors contributing to capacity decay, encompassing vanadium cross-over, self-discharge reactions, water molecules migration, gas evolution reactions, and vanadium precipitation. Subsequently, it analyzes the impact of various battery parameters on capacity. Based on this foundation, the article expounds upon the significance of battery internal state estimation technology. Additionally, the review also summarizes domestic and international mathematical models utilized for simulating capacity decay, serving as a valuable reference for future research endeavors. Finally, through the comparison of traditional experimental methods and mathematical modeling methods, this article offers effective guidance for the future development direction of battery state monitoring. This review generally overview the problems related to the capacity attenuation of all-vanadium flow batteries, which is of great significance for understanding the mechanism behind capacity decay and state monitoring technology of all-vanadium redox flow battery.

Research on tool wear state identification method driven by multi-source information fusion and multi-dimension attention mechanism

Cutting tool wear condition monitoring technology is the key technology of advanced manufacturing system and a crucial component of machining. The stage of the tool wear has a direct impact on performance of the workpiece and efficacy of machine tool. However, the duplication and lack of wear information and the fuzzy area of the tool wear transition stage are the key factors contributing to the incorrect estimation of the tool wear state when extracting cutting tool wear feature information. As a consequence, this study presents a data fusion from several sources-based intelligent tool wear state detection technique. The fusion of data from several sources can effectively realize the complementarity of machining information. This provides the model with more comprehensive identification data. The mapping between wear state and wear characteristics is precisely established. To address these issues, the attention mechanism of channel and spatial latitude is integrated into the feature extraction. The model that was constructed in the present investigation has a comprehensive identification accuracy of 0.982. The F1 score of initial, normal and severe wear stage of tool wear are 0.977, 0.968 and 0.993, which are better than other models. Experiments show that the identification method proposed in this study may provide accurate tool wear condition identification based on machining process data, allowing for more flexible and precise cutting tool change decisions in machining.

Design and use of a wireless temperature measurement network system integrating artificial intelligence and blockchain in electrical power engineering.

This work aims to investigate the potential fire hazard stemming from the overheating of power equipment. The advent of the artificial intelligence era has facilitated the fusion of blockchain and Internet of Things (IoT) technologies. This work delves into the technical standards for IoT equipment monitoring and smart grid communication, and the IoT environment of power grid equipment. This work introduces a temperature monitoring network tailored for IoT wireless power equipment suitable for the power environment, and conducts system debugging in the power laboratory. The findings affirm that the temperature out-of-limit alarm testing has met the required criteria, confirming the system's ability to issue timely warnings when temperatures breach a predefined threshold, effectively avoiding high-temperature misfires. This work fully harnesses the secure and user-friendly operation of smart blockchain and the wireless sensing technology of the IoT to realize online monitoring and remote temperature measurement of the power system. It can effectively prevent equipment from overheating and damage, and promote the development of equipment condition monitoring technology in electric power engineering.

Condition Monitoring and Fault Diagnosis of Permanent Magnet Synchronous Motor Stator Winding Using the Continuous Wavelet Transform and Machine Learning

Abstract Applying the condition monitoring technology to industrial processes can help detect faults in time, minimise their impact and reduce the cost of unplanned downtime. Since the introduction of the Industry 4.0 paradigm, many companies have been investing in the development of such technology for drive systems. Permanent magnet synchronous motors (PMSMs) have recently been used in many industries. Therefore, the issues of condition monitoring of PMSM drives are important. This study proposes and compares diagnostic schemes based on the stator phase currents (SPCSCs) signal for condition monitoring and fault diagnosis of PMSM stator winding faults. The continuous wavelet transform (CWT) is used for the extraction of the symptoms of interturn short circuits in PMSM stator winding. Machine learning algorithms are applied to automate the detection and classification of the faults. The concept for an original and intelligent PMSM stator winding condition monitoring system is proposed.

Research on Transmission Line Tower Tilting and Foundation State Monitoring Technology Based on Multi-Sensor Cooperative Detection and Correction

Research on Transmission Line Tower Tilting and Foundation State Monitoring Technology Based on Multi-Sensor Cooperative Detection and Correction

Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach

This paper presents a transformative methodology that harnesses the power of digital twin (DT) technology for the advanced condition monitoring of lithium-ion batteries (LIBs) in electric vehicles (EVs). In contrast to conventional solutions, our approach eliminates the need to calibrate sensors or add additional hardware circuits. The digital replica works seamlessly alongside the embedded battery management system (BMS) in an EV, delivering real-time signals for monitoring. Our system is a significant step forward in ensuring the efficiency and sustainability of EVs, which play an essential role in reducing carbon emissions. A core innovation lies in the integration of the digital twin into the battery monitoring process, reshaping the landscape of energy storage and alternative power sources such as lithium-ion batteries. Our comprehensive system leverages a cloud-based IoT network and combines both physical and digital components to provide a holistic solution. The physical side encompasses offline modeling, where a long short-term memory (LSTM) algorithm trained with various learning rates (LRs) and optimized by three types of optimizers ensures precise state-of-charge (SOC) predictions. On the digital side, the digital twin takes center stage, enabling the real-time monitoring and prediction of battery activity. A particularly innovative aspect of our approach is the utilization of a time-series generative adversarial network (TS-GAN) to generate synthetic data that seamlessly complement the monitoring process. This pioneering use of a TS-GAN offers an effective solution to the challenge of limited real-time data availability, thus enhancing the system’s predictive capabilities. By seamlessly integrating these physical and digital elements, our system enables the precise analysis and prediction of battery behavior. This innovation—particularly the application of a TS-GAN for data generation—significantly contributes to optimizing battery performance, enhancing safety, and extending the longevity of lithium-ion batteries in EVs. Furthermore, the model developed in this research serves as a benchmark for future digital energy storage in lithium-ion batteries and comprehensive energy utilization. According to statistical tests, the model has a high level of precision. Its exceptional safety performance and reduced energy consumption offer promising prospects for sustainable and efficient energy solutions. This paper signifies a pivotal step towards realizing a cleaner and more sustainable future through advanced EV battery management.

Feasibility Analysis of Current Pavement Condition Monitoring Technologies: 3D Pavement Profiler and Infographic Pavement Management Program Using the Semi-Circular Bending Fracture Test

Feasibility Analysis of Current Pavement Condition Monitoring Technologies: 3D Pavement Profiler and Infographic Pavement Management Program Using the Semi-Circular Bending Fracture Test

Detection and Diagnostic with Random Forest Classifier (RFC) to Improve the Maintenance Management System in Steam Boiler of Power Plant

Industrial internet of things (IIoT), digital twin, and connected devices can continue to use smart equipment and improve access to data. While the data collected by sensors has been an invaluable asset to companies, the ability to understand and use this data to drive new insights. The development of Condition Monitoring (CM) technology and Computerized Maintenance Management System (CMMS) in power generation systems provides a validated set of operation and maintenance data with abundant event data. Maintenance decision-making is primarily based on equipment reliability and performance-based features for diagnosing equipment failure. The most critical asset and often reduces the reliability and availability of a Coal Fired Steam Power Plant (CFSPP) with the most frequency of disturbances is the steam boiler. As a departure from the idea of creating a digital twin, this article will focus on analyzing equipment health conditions and finding causes of failure of the tools, utilizing data for diagnostic purposes. Real-case used in this research are steam boilers, which are important assets in power plant generation. The online and Failure Mode and Effect Analysis (FMEA) module data will be combined to realize the concept of anomaly diagnosis which is driven by hybrid data. Hoping that accurate diagnosis result with the Random Forest Classifier (RFC) Algorithm can be obtained and be used to analyze the causes of failure and decrease in equipment performance resulting by a decrease of energy efficiency performance. The analytical approaches are carried out to have the goal of generating detection models and diagnostic insights of event data based on operational data and FMEA.

Digital Twin Construction and Application in Blade Measurement Process

In order to solve the problems of visualization and condition monitoring of the blade measurement process, a digital twin construction method of the blade measurement process was proposed. By studying the digital twin construction method based on multi-dimensional data, a digital twin system of blade measuring equipment was established. Through the study of complex system condition monitoring technology, the connection between physical system and virtual system is realized. This method can achieve digital twin matching and synchronous calibration in the coordinate measurement process, solve the problem of difficult integration between information space and physical space, achieve digitalization of the measurement process and state monitoring, and provide a research foundation for subsequent data analysis.

Research on loom on-board condition monitoring technology based on fuzzy rough set and improved DSmT theory1

The foundation of textile machinery digitization and intelligence is condition monitoring and identification. Online condition monitoring of looms is of great significance to ensure their long-term stable operation and improve their digital management level. However, the existing loom condition monitoring methods have problems such as insufficient depth of information mining, low condition recognition rate and poor system versatility. As a result, the loom on-board condition monitoring technology based on fuzzy rough set and improved DSmT theory is studied. To begin, we examine the loom operation mechanism, loom state characterization, and loom state feature data composition. Then, using the fuzzy rough set method, we analyze and make decisions on the loom state feature data, apply the theory of uncertainty and importance improvement DSmT fusion to solve the uncertainty problem of the rough set method’s decision rules, and build the loom state feature decision network on the embedded terminal using the decision rules. Meanwhile, to collect, communicate, display, and alarm loom characteristic data, this paper employs the STM32F407ZET6 microcontroller and designs a loom system status data collection platform with the AD7730 as the core, as well as tests loom status monitoring data collection and loom status data analysis and decision method based on this platform. The experimental findings show the usefulness of attribute data gathering as well as data analysis and decision-making processes. The technology enhances the precision of loom condition identification and decision making, as well as the safety and quality of manufacturing. It is critical for carrying out applications like as problem detection, remote monitoring, efficiency optimization, and intelligent weaving machine management.

Research Progress in Failure Mechanism and Health State Evaluation Index System of Welded IGBT Power Modules

As the performance requirements of insulated gate bipolar transistors (IGBTs) in various fields of power electronic systems are increasing, the IGBT power module package structure, aging mechanism analysis, and selection of health state characteristic parameters are of great significance to ensure the normal operation of power systems. At present, research on the aging failure of welded IGBT modules is relatively comprehensive and can be divided into chip aging failure and package aging failure according to their failure parts. However, there is some research on the relationship between aging failure and the characteristic quantity of power modules, but no definite system has been formed. Therefore, in this paper, the packaging structure of the solder-type IGBT modules and the aging mechanism caused by different aging reasons were summarized. Moreover, the health status monitoring of solder-type IGBT modules from the junction temperature, bonding line, solder layer, and package insulation aging health status monitoring and its quantitative assessment research progress was analyzed in detail. Also, the health state evaluation index system of the solder-type IGBT modules was completed on the basis of voltage, current, thermal, insulation parameters, and other characteristic quantities. Finally, based on the analysis of domestic and international research problems, the development trend and direction of the condition monitoring technology of solder-type IGBT modules is foreseen. The research on condition monitoring for welded IGBT module technology has a certain significance.

Applications of IoT and digital twin in electrical power systems: A comprehensive survey

AbstractThis paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research.

Physics verification and validation for transferring data between bearings

Bearings are critical components for transferring load and motion between subsystems with reduced friction for rotational equipment. Manufacturers implement condition monitoring technology to prevent failures of bearings by using different data acquisition methods, such as vibration, acoustics, temperature, motor current, and ultrasonic sensing, to monitor and predict changes that could indicate early equipment degradation. However, the data quality and availability varies depending on the application, and low data availability from physical environments can lead to poorly trained models. This paper explores how to transfer data and information about failure propensity between bearings of different sizes using a combined physics and data-driven approach. Though the approach is exemplified with roller bearings, the method is extensible to other types of failures in different-sized equipment. Data are generated using an experimental test stand measuring failure of one component, with the intent to scale findings to represent a real-world system; findings are verified with a physics-based model. Data from three different bearing sizes, i.e. 6205, 6206, and 6207, are used to train the algorithms to identify similarity among the sets. Classifiers trained with the raw data provide over 90% accuracy, leading to the conclusion that the data classes are separable based on bearing size. 6205 and 6207 data were scaled to simulate 6206 and tested to see if a classifier could differentiate between the true 6206 bearing data and the simulated bearing data derived from the 6205 and 6207 data. In the simulated data, the classifier accuracy for each algorithm dropped below 90% to as low as 50% (Naive Bayes case). The lower accuracy implied a greater overlap in the vibration features, increasing the data similarity between the different bearing sizes. Future work will further investigate defining dimensionless numbers as scaling parameters for bearing data.

이벤트 센서를 활용한 운전 중 장비 상태 감시 가능성 고찰

Vibration-based condition monitoring technology is regarded as one of the most effective maintenance technologies. Many studies aimed at developing a vision sensor-based condition monitoring system using image processing technologies are being conducted. Event sensors such as dynamic vision sensor have a different operating principle and have many advantages compared to the traditional frame-based sensors; however, for event sensor-based vibration measurement and analysis, the formalized methodology does not exist as an image processing technology. In this study, optical flow and template matching methods were used as tracking methods for event sensor-based condition monitoring. The vibration measurement performance of the representative methods was examined through experiments using a simulator with vertical and horizontal movements. Furthermore, the possibility of extracting changes in the vibration signals according to the state of simulator was investigated.

A hybrid prognostic method for rotating machinery under time-varying operating conditions by fusing direct and indirect degradation characteristics

Condition monitoring technologies can provide sensor data for predicting the remaining useful life (RUL) of rotating machinery. However, it is often difficult to obtain direct degradation characteristics (DCs), which directly reflect the health of a machine, in real-time. Instead, indirect DCs, which are collected under time-varying operating conditions, are often used. Traditional machine learning and model-based prognostic methods may not be effective when handling such data. This paper presents a hybrid Direct-Indirect Fusion (DIF) method that combines direct and indirect DCs to predict the RUL of rotating machinery under time-varying conditions. The framework can account for time-varying covariates, convert indirect DCs to direct DCs under moderate sample sizes with a loop-generative adversarial network (Loop-GAN), and describe gradual degradation and sudden shocks in direct DCs with Lévy processes. The proposed framework outperforms several benchmarks in predicting the degradation path and RUL of rotating machinery as demonstrated in both simulation examples and an industrial application.