Condition Monitoring System Research Articles

Design and Analysis of Universal Joint to Enhanced Performance in Electric Vehicles - A Review

The rapid evolution of electric vehicles (EVs) has necessitated a comprehensive reevaluation of critical components, particularly the universal joint (U-joint), which plays a pivotal role in transmitting power from electric motors to the wheels. This review meticulously examines the existing literature on the design and analysis of U-joints, with a primary focus on enhancing performance in the context of electric vehicles. Challenges unique to EVs, including higher torque demands and variable speeds, underscore the urgency for optimized U-joint designs. Material advancements and manufacturing techniques, analytical tools, and innovative designs are explored, considering their potential to address these challenges. Smart technologies, such as sensors and condition monitoring systems, are integrated into the discussion, offering insights into real-time monitoring and predictive maintenance strategies. Environmental considerations, case studies, and practical implementations are also deliberated upon. The literature review concludes with a forward-looking perspective, identifying future trends and recommending areas for continued research and development in the pursuit of achieving enhanced U-joint performance for electric vehicles. This synthesis of research findings serves as a valuable resource for engineers, researchers, and industry professionals engaged in advancing the efficiency and reliability of electric propulsion systems.

Interface Control and Status Monitoring of Electronic Information Equipment based on Nonlinear Data Encryption

An advanced electronic information equipment interface control and status monitoring system is proposed to ensure the fairness, objectivity, and security of information while identifying responsibility for traffic accidents. Through an in-depth analysis of the system's security requirements and the current landscape of information security technology, a robust security strategy is developed for each crucial system stage. A PC-based platform is developed for efficient data acquisition, secure processing, reliable transmission, and fortified storage, focusing on implementing nonlinear data encryption methods. Performance evaluation of the system involved rigorous testing using files ranging from 3MB to 10MB. The results of the proposed system revealed a significant improvement in the system's overall speed and efficiency, showcasing an average performance enhancement of one quarter compared to the original platform. The proposed system demonstrated an impressive 15% to 30% increase in processing speed, establishing its capability to ensure data integrity protection during information transmission, facilitate accurate identification of data recording equipment post-accident, and safeguard the security of stored data. The developed electronic information equipment interface control and status monitoring system effectively addresses critical challenges associated with ensuring data integrity and security in traffic accident investigations.

Driver body condition monitoring system based on human-computer interaction

Tired driving is still a significant cause of traffic accidents, often causing huge economic losses to society.Therefore, how to avoid driver fatigue has become a meaningful problem. The purpose of this paper is to review and summarize various research methods of driver fatigue monitoring system at home and abroad, so as to reduce the probability of traffic accidents caused by fatigue driving. The main content of this paper is to review the existing fatigue driving monitoring technologies such as electrocardiogram (ECG), electroencephalogram (EEG), HRV, eye tracking system, CNN, facial state analysis, emotion recognition, 5G and brain wave recognition, and infer the fatigue level of drivers by using these technologies to extract various clues that usually represent the level of human vigilance. Assess cognitive load and identify emotional states to monitor tired driving. By discussing the advantages and disadvantages of various fatigue monitoring systems at home and abroad, this paper proposes an innovative method, which transforms the previous single-mode monitoring system into a multi-mode monitoring system, integrates multiple biosensors, extracts relevant features from the biosensor data and processes the data reasonably in order to understand the drivers physiological state more comprehensively. This method is expected to improve the accuracy of fatigue monitoring system.

The future of tire energy: a novel one-end cap structure for sustainable energy harvesting

Piezoelectric energy harvesting is gaining popularity as an eco-friendly solution to harvest energy from tire deformation for tire condition monitoring systems in vehicles. Traditional piezoelectric harvesters, such as cymbal and bridge structures, cannot be used inside tires due to their design limitations. The wider adoption of renewable energy sources into the energy system is increasing rapidly, reflecting a global attraction toward the utilization of sustainable power sources (Aljendy et al. in Int J Power Energy Convers 12(4): 314–337, 2021; Yesner et al. in Evaluation of a novel piezoelectric bridge transducer. In: 2017 Joint IEEE International Symposium on the Applications of Ferroelectric (ISAF)/International Workshop on Acoustic Transduction Materials and Devices (IWATMD)/Piezoresponse Force Microscopy (PFM). IEEE, 2017). The growing interest in capturing energy from tire deformation for Tire Pressure Monitoring Systems (TPMS) aligns with this trend, providing a promising and self-sustaining alternative to traditional battery-powered systems. This study presents a novel one-end cap tire strain piezoelectric energy harvester (TSPEH) that can be used efficiently and reliably inside a tire. The interaction between the tire and energy harvester was analyzed using a decoupled modeling approach, which showed that stress concentration occurred along the edge of the end cap. The TSPEH generated a maximum voltage of 768 V under 2 MPa of load, resulting in an energy output of 32.645 J/rev under 1 MPa. The computational findings of this study were consistent with previous experimental investigations, confirming the reliability of the numerical simulations. The results suggest that the one-end cap structure can be an effective energy harvester inside vehicle tires, providing a valuable solution for utilizing one-end cap structures in high-deformation environments such as vehicle tires.

Design and Implementation of Smart Petrol Station

The problem that still exists nowadays with the petrol station is the method of operation because the petrol station is currently operated manually. As it is a time-consuming process that increases manpower, other problems are related to accuracy, gasoline smuggling, fluctuations in global oil prices, sales, database management, environmental pollution and others. Traditional methods of monitoring fuel in petrol station by humans on site are unable to meet the expectations for efficiency, accuracy and cost. Therefore, this paper designs an intelligent system of three filling stations, where the three stations are simultaneously displayed on a single web application, and this IoT-based system is implemented to address all the problems. Therefore, this paper presents the design and implementation of three petrol stations in which we are going to measure the level of fuel and show it to central server. internet of things (IoT) based petrol station monitoring system is a good approach to improve monitoring efficiency and to improve management efficiency in stations remotely. simulation results presented in LabVIEW software showed the ability of the system to monitor levels of petrol, detect fire, evaporation and etc.

The development of the soderberg electrolyzer electromagnetic field’s state monitoring system

This study is devoted to improving the economic efficiency of the cell, due to the field of the generated electromagnetic field’s accurate diagnostics. To solve this problem, the authors had developed a hardware-software complex for electromagnetic field diagnostics. This complex includes a measurement device and a software package for data collection and analysis. On the laboratory prototype of the aluminum electrolysis complex, a study was carried out on the formation and structure of the electromagnetic field. A number of experiments have been carried out showing the degree of formation of the electromagnetic field by the anode, the relationship of electromagnetic fields in the inter-anode space has been shown. Based on the results of the studies, conclusions were drawn about the possibility of diagnosing the current state of the anode, determining the direction of rotation of aluminum in the electrolytic cell and estimating the life of the anode and its burnout time.

Condition Monitoring of Wind Turbine Drivetrain Bearings

Abstract As an extremely valuable and reliable energy source, the renewable energy is developing around the world at an unprecedented pace. By the end of 2022, the global installed capacity of offshore wind turbines is expected to reach 46.4 GW, among which 33.9 GW in Europe. Efficiencies in Operations and Maintenance (O&M) offer potential to achieve significant cost savings as it accounts for around 20%–30% of overall offshore wind farm costs. A recent study of approximately 350 offshore wind turbines indicates that gearboxes might have to be replaced as early as 6.5 years. Therefore sensing and condition monitoring (CM) systems are needed in order to obtain reliable information on the state and condition of different critical parts. The development and use of such technologies will allow companies to schedule actions at the right time. The reduced costs of O&M enable the wind energy at a competitive price thus strengthening productivity of the wind energy sector. At the academic level, a plethora of methodologies have been proposed during the last decades focusing toward early and accurate fault detection. Among others, Envelope Analysis is one of the most important methodologies, where the envelope of the vibration signal is estimated, usually after filtering around a selected frequency band excited by impacts due to the faults. Different tools, such as Kurtogram, have been proposed in order to accurately select the optimum filter parameters (center frequency and bandwidth). Cyclostationary analysis and corresponding methodologies, i.e., the cyclic spectral correlation and the cyclic spectral coherence, have been proved as powerful tools for CM. On the other hand the application, test, and evaluation of such tools in general industrial cases is still rather limited. Therefore the main aim of this paper is the application and evaluation of advanced diagnostic techniques and diagnostic indicators, including the Enhanced Envelope Spectrum and the Spectral Flatness on real-world vibration data collected from vibration sensors on gearboxes in multiple wind turbines over an extended period of time of nearly four years. The diagnostic indicators are compared with classical statistic time and frequency indicators, i.e., Kurtosis, Crest Factor, etc. and their effectiveness is evaluated based on the successful detection of two failure events.

On-line condition monitoring for rotor systems based on nonlinear data-driven modelling and model frequency analysis

This paper proposes a novel on-line rotor system condition monitoring approach using nonlinear data-driven modelling and model frequency analysis. First, the dynamic process model of the vibration transmission path between the vibration measurement points of two fulcrum structures is established by utilizing nonlinear data-driven modelling. Then, the unique frequency properties are extracted from the established model to reveal, in real time, the health condition of the rotor system. Finally, using the frequency properties as features, the unsupervised learning technology is applied to the on-line monitoring of the rotor system. Compared to conventional condition monitoring methods, the proposed approach can output an early warning 26 min before a shaft fracture occurs, without generating false alarms. Consequently, this approach can greatly enhance diagnostic accuracy, demonstrating its potential to contribute to the advancement of rotor system condition monitoring techniques.

Railway Catenary Condition Monitoring: A Systematic Mapping of Recent Research

In this paper, a different approach to the traditional literature review—literature systematic mapping—is adopted to summarize the progress in the recent research on railway catenary system condition monitoring in terms of aspects such as sensor categories, monitoring targets, and so forth. Importantly, the deep interconnections among these aspects are also investigated through systematic mapping. In addition, the authorship and publication trends are also examined. Compared to a traditional literature review, the literature mapping approach focuses less on the technical details of the research but reflects the research trends, and focuses in a specific field by visualizing them with the help of different plots and figures, which makes it more visually direct and comprehensible than the traditional literature review approach.

The optimization, design and performance of the FBCM23 ASIC for the upgraded CMS beam monitoring system

We present the development of the FBCM23 ASIC designed for the Phase-II upgrade of the Fast Beam Condition Monitoring (FBCM) system built at the CMS experiment which will replace the present luminometer based on the BCM1F ASIC [1]. The FBCM system should provide reliable luminosity measurement with 1 ns time resolution enabling the detection of beam-induced background. The FBCM23 ASIC comprises 6 channels of the fast front-end amplifier working in transimpedance configuration, booster amplifier, and leading edge discriminator. The complete processing chain provides an overall shaping function equivalent to the CR-RC3 filter. The paper will show the optimization of the design, overall architecture, and the detailed implementation in a CMOS 65 nm process as well as preliminary electrical performance.

Разработка программного комплекса для испытания цифровых измерительных трансформаторов на вибростенде

The presented work examines a software package for monitoring the condition of a hydraulic servo drive with hydrostatic guides as part of a diagnostic vibration stand for digital transformers. The service life of digital instrument transformers is 25 years, and the calibration interval is 8 years. To achieve such high performance characteristics, it is necessary to conduct a large number of tests for such products. One such test is the vibration test. The uniqueness of the hydraulic drive used in the diagnostic vibration stand lies in its long service life under operational loads (up to 100 million cycles). This reliability is achieved with special hydrostatic guides in the hydraulic drive design and requires special software. The architecture of the software complex for the vibration stand has been created. To test the functionality of the package, simulation modeling of the operation of the components of the software package was performed. Aim. Based on the analysis of the functioning of technological equipment and options for implementing control software, develop the architecture of a software package for implementing a system for condition monitoring of a servo hydraulic drive with hydrostatic guides as part of a diagnostic vibration stand for digital transformers. Materials and methods. The stated scientific problems were solved using methods of system analysis, computer, mathematical and simulation modeling of the interaction of the software complex with the components of the servo hydraulic drive with hydrostatic guides. Results. The main practical result of the research is the development of the architecture of a software package for testing digital instrument transformers on a vibration stand. It is proposed to use cross-platform solutions when implementing the complex software and rely on modern experience in using freely distributed software. Conclusion. The developed architecture of the software package is focused on ensuring the automated operation of servo hydraulic drives, allows you to configure all the necessary equipment parameters, monitor the condition of the equipment using digital data transfer protocols, centrally save and replicate hydraulic drive settings for specific operating conditions on the enterprises. Analysis of implementation options showed that the use of a SCADA system simplifies the process of developing standard projects for dispatch control and data collection and condition monitoring, but at the same time, it has increased complexity at the stage of deployment and operation. The functionality of none of the widespread and reliable SCADA systems covers all the requirements for this project. This particularly applies to USB connectivity and high frequency signal processing required by design requirements. In addition, when using universal SCADA systems, the end user must purchase a license for the RunTime SCADA component.

Efficient implementation of a wavelet neural network model for short-term traffic flow prediction: Sensitivity analysis

The concept of a smart city is emerging to address significant challenges arising from rapid urbanization, economic growth, and climate change. Innovative technologies can be used as a means to promote sustainable and inclusive urban development. These technolgies include the deployment of the internet of things (IoT), artificial intelligence (AI), energy management, and smart transportation. In a smart city, intelligent transportation systems (ITS) play a vital role in efficient traffic management. This paper explores the use of hybrid artificial intelligence techniques for predicting short-term traffic flow data from M25 motorways in the UK. Since volume traffic flow data are non-stationary, wavelet transform (WT) as a powerful signal analyzer is applied for signal decomposition for the elimination of redundant data from input matrices. The feature selection method based on Gram-Schmidt (GS) is used for the selection of more valuable features. The elimination of redundant data can speed up the learning process and improve the generalisation capability of the prediction models. After a pre-processing stage, a wavelet neural network (WNN) with a simple structure is applied as a powerful prediction tool. Two separate structures are considered for the prediction of weekday and weekend traffic volume data. The experiments explore that the debauchies-4 (db4) wavelet function with 7 decomposition levels leads to the best detection accuracy. Moreover, the range of forecasting, the type of the day, the level of decomposition, and other factors all have an impact on prediction stability. Compared with existing prediction methods, the proposed approach produces lower values of root mean square error (RMSE) and mean absolute percentage error (MAPE) for all step-horizons analyzed. These findings provide valuable implications and insights into the development of an efficient and reliable road condition monitoring system for delivering secure and sustainable transportation services.

Predictive maintenance of rotational machinery using deep learning

This paper describes an implementation of a deep learning-based predictive maintenance (PdM) system for industrial rotational machinery, built upon the foundation of a long short-term memory (LSTM) autoencoder and regression analysis. The autoencoder identifies anomalous patterns, while the latter, based on the autoencoder’s output, estimates the machine’s remaining useful life (RUL). Unlike prior PdM systems dependent on labelled historical data, the developed system doesn’t require it as it’s based on an unsupervised deep learning model, enhancing its adaptability. The paper also explores a robust condition monitoring system that collects machine operational data, including vibration and current parameters, and transmits them to a database via a Bluetooth low energy (BLE) network. Additionally, the study demonstrates the integration of this PdM system within a web-based framework, promoting its adoption across various industrial settings. Tests confirm the system's ability to accurately identify faults, highlighting its potential to reduce unexpected downtime and enhance machinery reliability.

Design of Motor Condition Monitoring System for Belt Conveyor in Coal Mine

With the increase of the number of mines and the depth of mining coal in China, it is more and more important for the safety performance of underground equipment. On the basis of analyzing the coal mine safety production and transportation system, we design the motor condition monitoring system of coal mine belt conveyor, choose high-precision sensors to collect the signals of voltage, current, temperature, torque and speed of the coal mine belt conveyor motor, and use the CAN communication circuit to transfer the collected data to the computer, so as to realize the condition monitoring of the coal mine belt conveyor motor.

Tool condition monitoring in micro milling of brittle materials

This paper introduces a novel indirect tool condition monitoring (TCM) system for micro-milling brittle materials (glass and silicon) based on acoustic emission (AE) and cutting force signals. The milling experiments are also applied to a ductile material (steel) for comparison. Tool wear calibration is conducted to determine the three tool wear stages. The collected signals are processed in time, frequency, and time-frequency domains. Specific frequency subrange combinations of cutting force signals in three directions after wavelet packet decomposition show strong correlation to the tool wear stages, whilst processed AE signals are the secondary feature to the tool wear. A tool wear prediction model for each material is built based on all the chosen features and back propagation (BP) neural network. The prediction model is simply structured and highly efficient with the highest prediction accuracy of more than 95%.

Remote condition monitoring of rail tracks using distributed acoustic sensing (DAS): A deep CNN-LSTM-SW based model

Railroad condition monitoring is paramount due to frequent passage through densely populated regions. This significance arises from the potential consequences of accidents such as train derailments, hazardous materials leaks, or collisions which may have far-reaching impacts on communities and the surrounding areas. As a solution to this issue, the use of distributed acoustic sensing (DAS)-fiber optic cables along railroads provides a feasible tool for monitoring the health of these extended infrastructures. Nevertheless, analyzing DAS data to assess railroad health or detect potential damage is a challenging task. Due to the large amount of data generated by DAS, as well as the unstructured patterns and substantial noise present, traditional analysis methods are ineffective in interpreting this data. This paper introduces a novel approach that harnesses the power of deep learning through a combination of CNNs and LSTMs, augmented by sliding window techniques (CNN-LSTM-SW), to advance the state-of-the-art in the railroad condition monitoring system. As well as it presents the potential for DAS and fiber optic sensing technologies to revolutionize the proposed CNN-LSTM-SW model to detect conditions along the rail track networks. Extracting insights from the data of High tonnage load (HTL)- a 4.16 ​km fiber optic and DAS setup, we were able to distinguish train position, normal condition, and abnormal conditions along the railroad. Notably, our investigation demonstrated that the proposed approaches could serve as efficient techniques for processing DAS signals and detecting the condition of railroad infrastructures at any remote distance with DAS-Fiber optic cable setup. Moreover, in terms of pinpointing the train's position, the CNN-LSTM architecture showcased an impressive 97% detection rate. Applying a sliding window, the CNN-LSTM labeled data, the remaining 3% of misclassified labels have been improved dramatically by predicting the exact locations of each type of condition. Altogether, these proposed models exhibit promising potential for accurately identifying various railroad conditions, including anomalies and discrepancies that warrant thorough exploration.

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. This research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

Supervision, Condition Monitoring and Fault Diagnosis System in a Hybrid Renewable Energy Systems (HRES) Laboratory

Hybrid renewable energy is a good alternative to get a reliable power supply; this idea allows local resources optimization, but, its ideal arrangement depends on available resources and final users’ needs. To explore and evaluates possible solutions, the Institute for Energy Engineering of the Universitat Politècnica de València has assembled different renewable sources and energy storage in a laboratory of distributed renewable resources LabDER. This paper presents the progress in the supervisory system that includes condition monitoring and fault diagnosis.

An IoT-Based Road Bridge Health Monitoring and Warning System.

Recent earthquakes worldwide have led to significant loss of life and structural damage to infrastructure, especially road bridges. Existing bridge monitoring systems have limitations, including restricted detection capabilities, subjectivity, human error, labor-intensive inspections, limited access to remote areas, and high costs. Aging infrastructures pose a critical concern for organizations and government funding policies, showing signs of decay and impending structural failure. To address these challenges, this research proposes an IoT-based bridge health status monitoring and warning system that is wireless, low-cost, durable, and user-friendly. The proposed system builds upon engineering standards and guidelines to classify bridge health status into categories ranging from excellent to collapse condition. It incorporates deflection, vibration, temperature, humidity, and infrared sensors, combined with IoT and a fuzzy logic algorithm. The primary objective is to reduce bridge maintenance costs, extend lifespans, and enhance transportation safety through an early warning system via a mobile application. Additionally, a Google Maps interface has been developed to display bridge conditions along with real-time traffic video. To validate the proposed system, a 3-D prototype model was constructed and tested. Practical testing of the fuzzy logic algorithm aligned with the simulation outcomes, demonstrating expected accuracy in determining bridge health status.

On the effectiveness of standards application to threshold setting in vibration condition monitoring in industrial machinery

One of the goals of Industry 4.0 is to minimize unplanned maintenance emergencies and to leverage the efficiency of condition-based, just-in-time labor and repairs. Achieving the benefits of predictive maintenance via an Industry 4.0-style plant requires not only a data capture ecosystem, but also reference values to inform the monitoring system. Effectively estimating the values of monitoring thresholds is not an easy task, especially in those cases where the manufacturer does not provide data, or the machine is not new but the monitoring system has just been installed, therefore a measurement history is not yet available. The purpose of the present work is assess, through the analysis of two separate test cases, whether the monitoring thresholds suggested by different internationally recognized standards may serve as effective starting values in a vibration-based condition monitoring system. While general agreement on the order of magnitude of the thresholds is present in all cases, the differences between the standards allow for conclusions to be drawn on the most convenient format and processing technique to handle the data and to assess the most/least conservative standards. This paper constitutes, to the authors knowledge, the only systematic study in which the recommendations of all available international standards for vibration monitoring of non-rotating parts are applied to the same test cases and compared.