Operation Status Research Articles

GeP3 monolayer as a promising 2D sensing materials in detecting SO2, H2S, SOF2 and SO2F2

The detection of SF6 decomposition components using gas-sensitive sensors is significantly important for characterizing internal insulation failures and assessing the operational status of SF6 gas-insulated equipment. In this paper, the adsorption properties of GeP3 monolayers for SO2, H2S, SOF2 and SO2F2 gases were investigated based on density functional theory. Four gas adsorption systems were constructed, and the adsorption mechanisms and sensing characteristics of GeP3 monolayers on target gases were investigated by calculating parameters such as adsorption energy, charge transfer, density of states, and recovery time, along with their potential application as resistive gas sensors and field-effect transistor sensors. It is demonstrated that GeP3 monolayers were suitable for the detection of SO2, H2S, SOF2 and SO2F2 gases, all of which exhibited good chemisorption with adsorption energies of −1.36 eV, −0.78 eV, −1.82 eV and −2.91 eV, respectively. The adsorption of SO2 and H2S is found to cause a significant change in the conductivity of the GeP3 monolayers, and desorption is achieved at the optimal operating temperature in only 54.428 s and 10.686 s, respectively. Also the adsorption of SOF2 and SO2F2 can make the work function of the GeP3 monolayers significantly larger. Consequently, the GeP3 monolayers have the potential to be used as a resistive gas sensor for SO2 and H2S gases, or as a field effect transistor sensor for SOF2 and SO2F2 gases. This study provides theoretical guidance for the development of GeP3-based sensors for monitoring the insulation status and operational conditions of SF6 gas-insulated equipment.

Coordinated control strategy of photovoltaic energy storage power station based on adaptive dynamic programming

In order to solve the problem of variable steady-state operation nodes and poor coordination control effect in photovoltaic energy storage plants, the coordination control strategy of photovoltaic energy storage plants based on ADP is studied. Establish the photovoltaic energy storage power station model including photovoltaic system model, super capacitor system model and battery system model; Set the maximum limit of active power change as the power constraint condition for coordinated control of photovoltaic energy storage station; The optimal control problem of multi voltage and reactive power resource coordination is fully considered, the optimal voltage control model is established by using ADP algorithm, and the optimal coordinated control strategy is obtained by online learning the collected dynamic operation information. The experimental results show that this strategy can improve the coordinated control effect of the photovoltaic energy storage station, ensure the photovoltaic energy storage station in a stable operation state, improve the service life of the energy storage device in the photovoltaic energy storage station, and stabilize the steady-state operating node voltage within the ideal fluctuation range.

Forecasting chaotic behavior of the drill bit in real-time using a DNN model integrated with rock surface morphology

The multiple regenerative effects during the drilling process cause the drilling system to exhibit complex motions, even leading to chaotic behavior. Additionally, due to the inability to obtain real-time rock parameters, traditional drilling dynamics models and time-series forecasting models cannot accurately forecast the real-time operational status of the drilling system. In this paper, a deep neural network (DNN) model integrated with rock surface morphology model is proposed to forecast the real-time operational status of the drill bit. This new model can permanently record the changes in rock surface morphology and multiple regenerative effects during the drilling process, enhancing the perception of rock parameters and improving the forecasting accuracy of the drill bit’s operational status. Experimental results show that the new model forecasts the drill bit’s operational status with higher accuracy compared to other typical time-series forecasting models without integrating surface morphology model. Furthermore, this new model demonstrates superior convergence speed, stability, and generalization ability compared to other models. In addition, the influence of hyperparameters on the forecast accuracy of the new model has also been studied. These results play an important role in forecasting and preventing adverse vibrations such as stick slip, impact, reversal, and rebound of the drill bit.

진로체험지원센터의 운영 개선을 위한 탐색적 연구

Objectives This study analyzed the current status of career exploring support centers nationwide and aimed to derive implications and recommendations for the activation of these centers based on an exploratory analysis. Methods The survey was conducted targeting employees (administrators, practitioners) of all 218 career exploring support centers nationwide. To reflect the diverse staffing and operational situations of the career exploring support centers, we utilized the stratified cluster sampling method among probabilistic sampling methods. Managers were categorized as either center directors or operational managers, while practitioners were categorized as operational staff. One individual from each category was selected to respond to the survey autonomously within the career exploring support centers. Frequency analysis was conducted using SPSS 25.0 software to analyze the operational status of the centers. Results The survey revealed a high proportion of difficulties related to human resources at career exploring support centers, indicating that challenges in staffing and resources, beyond personnel and physical resources, significantly impact the centers' effectiveness. National-level training emphasized macroscopic elements such as systems, policies, and societal changes, while regional-level training focused on tailored integration within local communities. Conclusions Through this study, it is necessary to discuss the establishment of a task management and collaboration system for rationalizing manpower at the career exploring support centers, as well as to discuss the institutional support for the status of the career experience center at a systemic level. Furthermore, there is a need for training and consulting tailored to the requirements of career exploring support centers, as well as active support from external entities such as local governments and provincial education offices to establish networks between career exploring support centers and relevant organizations.

Transfer learning with cross-domain adaptation of vibro-acoustic signals for electric motor mechanical fault detection and diagnosis.

Contact-based vibration measurement techniques involving accelerometers are typically utilized for machine health monitoring and manufacturing end-of-line quality control. Acoustic signals, encompassing sound pressure and particle velocity, carry crucial information about the operational status of underlying equipment and can be utilized for detecting various machine faults. Utilizing one-dimensional Convolution Neural Networks (1D-CNN) based on deep learning for processing raw acoustic time signals has proven to be a valuable approach in detecting machinery faults, offering an effective alternative to using traditional accelerometer signals. However, the success of deep learning methods is heavily dependent on the quantity and availability of data for robust network training. When transitioning to new non-contact type sensor technology, manufacturers may face challenges due to insufficient data for training deep learning models. In such cases, leveraging existing historical accelerometer-based data becomes crucial. Transfer learning emerges as a promising solution, allowing deep learning models trained on a source domain (SD) to be applied to a separate but related target domain (TD). This paper hence introduces a domain adaptation methodology by implementing transfer learning, where the SD 1D-CNN network is trained on accelerometer signals, then the trained model weights are transferred to the TD 1D-CNN network that can process acoustic signals.

Multi-Scenario Research on the Coupled and Coordinated Development of the Economic–Energy–Environmental (3E) System under the Reconstruction of the Power System—New Exploration Based on the “Dual Triangle” Theory

Under the background of building a new-type power system, as the core of the energy system, the restructuring of the power system will inevitably have a linked impact on the coordinated development of the economy–energy–environment system. A “Dual Triangle” theoretical framework consisting of the “Energy Impossibility Triangle” and “Sustainable Development Triangle” was constructed in this paper. By combining historical statistical analysis with scenario predictive analysis, the historical characteristics of the “Dual Triangle” and future trends under different scenarios were analyzed, and the coupled interaction relationship of the “Dual Triangle” was also studied. Conclusions were as follows: The energy system gradually inclined towards economy and cleanliness from 2010 to 2022, and the operation status of the “Dual Triangle” was stable. A positive coupling relationship within the “Dual Triangle” in vigorously promoting renewable energy scenarios is close, which can be regarded as the preferred path for the reconstruction of the power system. The security and low-carbon phased development scenario is a development model that dynamically mitigates the challenges of the “Energy Impossibility Triangle”. Security has become the core contradiction of the “Energy Impossibility Triangle”. This paper conducts an integrated and innovative application of the “Dual Triangle” theory and provides a case basis for how to achieve the coupled interaction of the “Dual Triangle”.

Methods for Determining Losses and Parameters of Cylindrical-Rotor Medium-Power Synchronous Generators

This paper presents the analytical and numerical application of a method to determine the parameters and power losses in the core of two medium-power synchronous generators. These generators are used as emergency power sources powered by diesel engines, gas engines, and gas turbines. They cover peak electricity demand but can also be used in traction drives. This article presents a new numerical method for determining losses in the generator core based on the use of a time-stepping solution using the FEM method and calculating these losses using analytical formulas. In calculating the losses for the FEM method, approximations of the loss characteristics of the sheet were used with a wide range of induction values and frequencies. This method is specific to the solution used and was adapted from the authors’ previous work on losses in induction machines. A one-phase winding with alternating voltage was supplied to determine the basic parameters in the form of synchronous reactance. Also, an important novelty is the introduction of a new method of determining the saturation state of the magnetic circuit, which significantly affects the machine parameters. The obtained results were used in analytical calculations and implemented in a computer program that allows for the calculation of electromagnetic parameters, operating characteristics, and core losses, taking into account additional losses, total losses, and efficiency, as well as machine parameters in unsteady operating states and the current characteristics of a three-phase symmetrical short circuit at the machine terminals. The calculations obtained were verified experimentally by measurements of real machines.

Ameliorative effect of nano-pregabalin in gastrocnemius muscle of gamma irradiated rats with an experimental model of fibromyalgia: Crosstalk of Sirt3, IL-1β and PARP1 pathways

BackgroundFibromyalgia (FM) is a complex syndrome with somatic symptoms connected to the operational state of muscles. Although radiotherapy is a cornerstone in cancer treatment, it is implicated in the aggravation of FM. Lately, formulation of medicines in nano-forms become of great prominence due to their prospective applications in medicine. So, this study aimed to assess possible therapeutic benefits of formulating pregabalin in a nono-form (N-PG) for managing FM during exposure to gamma radiation. MethodsGamma rays administered in fractionated doses (2 Gy/day) to male rats after one hour of s.c. injection of reserpine (1 mL/kg per day) to induce FM, then treated with single daily dose of (30 mg/kg, p.o.) PG or N-PG for ten successive days. Rats were subjected to behavioral tests, then sacrificed to obtain serum and gastrocnemius muscles. ResultsN-PG significantly antagonized reserpine-induced FM as proved by; the immobility and performance times in forced swim and rotarod performance tests, respectively were restored near to the normal time, serum IL-8 and MCP-1 chemokines were nearby the normal levels, mitigated oxidative stress through increasing total thiol, Sirt3, CAT enzyme and decreasing COX-1, inhibition of inflammation via IL-1β and MIF significant reduction, it possessed anti-apoptotic effect verified by decreasing PARP-1 and increasing Bcl-XL, gastrocnemius muscles had minimal fibrosis levels as seen after Masson trichrome staining. Histopathological results were coincidence with biochemical inspection. ConclusionThis study identifies N-PG as a novel drug that could be of a value in the management of FM particularly in cancer patients undergoing radiotherapy.

A device for dynamic testing of the refractory ceramic resistance to biomass ash

The corrosive effects of biomass ash on refractory materials in combustion chambers pose challenges. Development of new castable refractory materials which are more resistant to corrosion requires tests that accurately reflect the conditions occurring in real devices. This article describes the Computational Fluid Dynamics (CFD) design and construction of a device for evaluating the corrosion of refractory materials in dynamic regime. The device allows to simulate combustion chamber conditions accurately, to regulate an injection of corrosive medium on the tested material surface under the specified conditions (temperature, time, and flow). In the present investigation the conventional refractory material was exposed to attack of biomass ash at the temperature of 1300 °C for 3 h to test the functionality of the device. A burner with technology oxygen–enriched combustion was used to heat the device. The corrosion tests were conducted in accordance with the standard P CEN/TS 15,418 and the standard STN EN ISO 21404. The operational state required for corrosion tests at 1300 °C was achieved within 1.12 h. The extent of refractory material degradation in the dynamic corrosion regime was evaluated using electron microscopy-energy dispersive spectroscopy (EM-EDS) and compared with the results of a static crucible test (3 h at 1300 °C). The dynamic test provided sufficient information on the interactions of corrosion medium with the refractories. Its main advantages are the maintenance of non-equilibrium conditions by controlled deposition of the corrosion medium in quantity from 1 to −100 g/h, fast heating rate to operating temperature (20 °C/min), and the possibility of conditions variability during tests (temperature, ox-red. atmosphere, fuel gas flow etc.).

Energy transfer, conversion and mechanical regulation in graded piezoelectric semiconductor bipolar junction transistor

While the effective regulation of carrier redistribution in piezoelectric semiconductor devices by mechanical loads through piezoelectric polarized electric fields is well-studied, there is a scarcity of reported research on the influence of energy transfer and conversion characteristics in a non-thermal equilibrium state of these devices. This paper establishes a nonlinear multi-field coupling model for piezoelectric semiconductor graded bipolar junction transistor (PS-GBJT) and proposes a corresponding multi-gradient iterative algorithm. We regard the product of current and built-in electric field as the work done by electric field force on carriers. By incorporating the energy absorbed/released during carrier recombination/generation process, we observe that non-equilibrium carriers act as a medium that facilitates energy conversion between these two mechanisms. Additionally, the non-uniformly distributed doping extends the influence of space charge region throughout entire transistor. In terms of external input electrical energy, the emitter, base, and collector regions play roles of input energy, transfer energy, and output energy, respectively. The study concludes by demonstrating the regulation of energy transfer or conversion efficiency in emitter, base, and collector regions through mechanical loads, thereby altering the heating behavior of entire GBJT and reshaping its energy transfer path. This leads to the significant finding that distinct energy transfer paths fundamentally define the various operational states of GBJT. The novel insights gained from this research hold reference value for thermal design and energy management of electronic devices.

A unified approach to modeling and stability equivalence analysis for grid-forming and grid-following converters

With the increasing penetration of renewable energy generation, the large-scale integration of grid-connected converters, serving as interfaces, has led to the characteristics of a weak grid with low strength and low inertia, resulting in insufficient stability of existing grid-following (GFL) converters. Grid-forming (GFM) converters, by emulating the characteristics of traditional synchronous machines, can operate stably in weak grids, but their introduction creates a complex operational state with mixed modes, presenting unprecedented challenges to the system. To address this, this paper first analyzes the structure and GFM/GFL control strategies of grid-connected converters; based on this, a comprehensive model framework for GFM/GFL converters is established, and the control of the converters is divided into three parts: LCL with the grid, output control, and power control, each modeled with a small-signal approach; using the established small-signal models, the stability of GFM/GFL converters under different grid short circuit ratios (SCR) is analyzed. Finally, a 20 kW test platform was constructed, and the correctness of the modeling and analysis was jointly verified using simulation frequency sweep methods and experimental time-domain stability analysis techniques.

A Health Monitoring Model for Circulation Water Pumps in a Nuclear Power Plant Based on Graph Neural Network Observer.

The health monitoring of CRF (circulation water) pumps is essential for prognostics and management in nuclear power plants. However, the operational status of CRF pumps can vary due to environmental factors and human intervention, and the interrelationships between monitoring parameters are often complex. Consequently, the existing methods face challenges in effectively assessing the health status of CRF pumps. In this study, we propose a health monitoring model for CRF pumps utilizing a meta graph transformer (MGT) observer. Initially, the meta graph transformer, a temporal-spatial graph learning model, is employed to predict trends across the various monitoring parameters of the CRF pump. Subsequently, a fault observer is constructed to generate early warnings of potential faults. The proposed model was validated using real data from CRF pumps in a nuclear power plant. The results demonstrate that the average Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) of normal predictions were reduced to 1.2385, 0.5614, and 2.6554, respectively. These findings indicate that our model achieves higher prediction accuracy compared to the existing methods and can provide fault warnings at least one week in advance.

A novel data-characteristic-driven modeling approach for imputing missing value in industrial statistics: A case study of China electricity statistics

As a direct reference tool to reflect the operational status and development level of national industry, industrial statistics hold significant value for numerous systematic studies. Nevertheless, it is crucial to recognize that the quality of these statistics can be compromised by the common occurrence of missing value. This issue poses a substantial challenge for analyzing and utilizing industrial statistics, impeding progress in tasks reliant upon them. Given the severity of the missing value problem in industrial statistical databases and the limitations of existing literatures on missing value imputation in terms of research objects and modeling approaches, this paper proposes a novel missing value imputation modeling approach for single-indicator panels of industrial statistics based on the idea of data-characteristic-driven (DCD). Accordingly, taking the inter-provincial “monthly power generation” data from China as an example, the imputation model was constructed and its validity was tested under different imputed objects (Jiangsu and Jilin), different missing types (continuous and discrete), and different missing rates (5%, 10% and 20%) respectively. The results indicate that the proposed DCD modeling approach in this paper exhibits excellent efficacy. The imputation model, constructed based on the data characteristic of the imputed object, demonstrates clear advantages in handling missing value with different missing types and rates. This is evident in its superior consideration of numerical accuracy, directional accuracy, and imputation stability, resulting in an outstanding comprehensive imputation effect.

Dynamics analysis of noncircular planetary gears

Noncircular planetary gear has simple structure and wide application, but its dynamics research is still blank. Therefore, this paper designs and establishes a 3–4 noncircular planetary gear model, and takes a deep dive into its dynamic characteristics. Firstly, the torsional vibration mechanical model of noncircular planetary gear is established, and the key factors in the planetary gear system are fully considered, such as time-varying meshing stiffness, backlash, meshing damping and excitation frequency. These factors have an important impact on the vibration characteristics of the system. Then, the vibration differential equation of the system is established, and the vibration characteristics of the system under different parameter conditions are analyzed. The results show that the noncircular planetary gear system with large damping and appropriate stiffness can maintain a more stable operating state at a larger excitation frequency. In addition, shortening the acceleration time of the system can effectively reduce the damage caused by the unstable state to the system. These findings provide a solid theoretical basis for the subsequent optimization and analysis of noncircular planetary gears.

Joint risk-based maintenance strategy and integrated monitoring approach for manufacturing system operational process considering workpiece quality

The fluctuation of various parameters in the production process increases the risk of generating unqualified products and suffering from systematic failures and downtimes, and the preventive maintenance and control of potentially faulty machines in manufacturing systems should consider the continuous increase of operational risk. Therefore, to proactive control the manufacturing system operational risk, a joint model of risk-based maintenance (RBM) and integrated monitoring is proposed. The proposed model formulates an appropriate maintenance strategy and appropriate control charts for monitoring the manufacturing process, guaranteeing the stable operation of the manufacturing system. First, considering the co-effect of the production equipment state and workpiece quality, a novel connotation of the manufacturing system mechanism’s operational risk is proposed, and the RBM framework is constructed. Second, considering the manufacturing system operational state, an integrated maintenance decision-making model based on different risk scenarios is established using a dynamic monitoring scheme based on EWMA control charts. Third, a particle swarm optimisation algorithm is adopted to optimise the proposed model to obtain the optimal maintenance strategy for production equipment and monitoring parameter for workpiece quality. Finally, the effectiveness of the proposed approach is illustrated with a case study on a subway current receiver production line.

Investigations on interpretable nuclear-grade hydraulic damper monitoring and diagnosis from multi-source high-dimensional profile data

Condition monitoring and fault diagnosis is important to ensure smooth machine operation, in which the use of multi-source high-dimensional profile data composed of multiple physical signals collected by multiple sensors for machine health monitoring and fault diagnosis has not been fully explored. In this paper, interpretable monitoring and diagnostic methods are proposed to analyze multi-source high-dimensional profile data and identify equipment operational status. Firstly, polynomials of segmenting profiles as well as a Lasso model are established to obtain sparse features. Then, for offline data with historical fault labels, an optimal linear hyperplane classifier is constructed to interpretably study the impact of different features on a decision. For online data with only normal samples, constructing an optimal hypersphere enables the measurement of operational status. Large-scale nuclear-grade hydraulic damper profile data are used to demonstrate how the proposed interpretable models work and show their physical interpretation for monitoring and fault diagnosis.

Factors influencing the overall satisfaction of teachers participating in a nationwide school-based smoking prevention program in Korea.

Numerous studies of school-based smoking prevention programs (SSPPs) exist; however, most have been conducted from the students' perspective, and insufficient research has explored teachers' perceptions. Our study aimed to identify factors affecting overall satisfaction and operational status from the perspective of teachers participating in the SSPP. This is a cross-sectional study analyzing data from a survey regarding the operation of an SSPP conducted by the Korea Health Promotion Institute in 2022. The study sample comprised 669 teachers involved in the SSPP: 215 from elementary schools, 212 from middle schools, and 242 from high schools. To identify factors influencing teachers' overall satisfaction, independent variables were categorized into three types of factors: personal, school, and teacher perceptions. Multiple linear regression analysis was performed for each factor to test the independent association. For elementary school teachers, as the necessity of smoking prevention and cessation education (β=0.292; 95% CI: 0.182-0.382) increased, the overall satisfaction with the operation of the SSPP significantly improved. Similarly, for middle school teachers, as the necessity of smoking prevention and cessation education (β=0.231; 95% CI: 0.104-0.336) increased, the overall satisfaction significantly improved. Conversely, for high school teachers, the effectiveness (β=0.347; 95% CI: 0.184-0.520) was the variable that significantly improved overall satisfaction with the SSPP operation. The variables affecting teacher satisfaction across all school levels were the necessity of smoking prevention and cessation education, the effectiveness of the SSPP, and its impact on smoking cessation among smoking students, all of which belonged to the teacher perceptions factor. Smoking education in schools requires teachers to play a crucial role. Among participating teachers, overall satisfaction with SSPP operations and the influencing factors differed according to school level, highlighting the importance of careful consideration to establish a more effective operational environment tailored to each school level.

Hybrid strategy of flexibility regulation and economic energy management in the power system including renewable energy hubs based on coordination of transmission and distribution system operators

Energy management of energy hubs in the power system impacts both transmission and distribution networks and their operations. Hence, to enhance efficiency, there should be bilateral coordination between distribution and transmission networks. Moreover, thanks to including power sources, storage, and responsive loads, hubs can earn financial benefits from the energy and ancillary services market. Therefore, this paper presents the operation of distribution and transmission networks in the presence of flexible-renewable energy hubs based on transmission system operator (TSO)-distribution system operator (DSO) coordination according to the market clearing price modeling in energy and flexibility markets. The proposed scheme minimizes the sum of the expected operating costs of the networks and hubs minus the expected flexibility profit of the hubs. The scheme is constrained to optimal power flow equations of the networks, flexibility limit of the networks, and flexibility-operation model of renewable energy hubs located in transmission and distribution networks. The unscented transformation method is used to model the uncertainties of load, renewable sources, and stationary storage (aggregation of electric vehicles). Energy price and flexibility are among the dual variables of the problem. To simultaneously calculate these variables and the main variables of the problem, an integrated model of the suggested scheme based on the penalty function technique is extracted. Eventually, numerical results validate the capability of the scheme in enhancing the operation, economic, and flexibility status of the proposed scheme, in which renewable sources along with storage and responsive load in the energy hub can obtain 100 % flexibility conditions for the networks. They help enhance the economic and operation indices of distribution and transmission networks by about 26 %-44 % and 22 %-33 % compared to the case without hubs and renewable sources.

Synchronised WindScanner field measurements of the induction zone between two closely spaced wind turbines

Abstract. Field measurements of the flow interaction between the near wake of an upstream wind turbine and the induction zone of a downstream turbine are scarce. Measuring and characterising these flow features in wind farms under various operational states can be used to evaluate numerical flow models and design of control systems. In this paper, we present induction zone measurements of a utility-scale 3.5 MW turbine with a rotor diameter of 126 m in a two-turbine wind farm operating under waked and unwaked conditions. The measurements were acquired by two synchronised continuous-wave WindScanner lidars that could resolve longitudinal and lateral velocities by dual-Doppler reconstruction. An error analysis was performed to quantify the uncertainty in measuring complex flow situations with two WindScanners. This is done by performing a large-eddy simulation while using the same measurement layout, modelling the WindScanner sensing characteristics and simulating similar inflow conditions observed in the field. The flow evolution in the induction zone of the downstream turbine was characterised by performing horizontal-plane dual-Doppler scans at hub height. The measurements were conducted for undisturbed, fully waked and partially waked flows. Evaluation of the engineering models of the undisturbed induction zone showed good agreement along the rotor axis. In the full-wake case, the measurements indicated a deceleration of the upstream turbine wake due to the downstream turbine induction zone as a result of the very short turbine spacing. During a wake steering experiment, the interaction between the laterally deflected wake of the upstream turbine and the induction zone of the downstream turbine could be measured for the first time in the field. Additionally, the analyses highlight the affiliated challenges while conducting field measurements with synchronised lidars.

Development and Application of IoT Monitoring Systems for Typical Large Amusement Facilities.

The advent of internet of things (IoT) technology has ushered in a new dawn for the digital realm, offering innovative avenues for real-time surveillance and assessment of the operational conditions of intricate mechanical systems. Nowadays, mechanical system monitoring technologies are extensively utilized in various sectors, such as rotating and reciprocating machinery, expansive bridges, and intricate aircraft. Nevertheless, in comparison to standard mechanical frameworks, large amusement facilities, which constitute the primary manned electromechanical installations in amusement parks and scenic locales, showcase a myriad of structural designs and multiple failure patterns. The predominant method for fault diagnosis still relies on offline manual evaluations and intermittent testing of vital elements. This practice heavily depends on the inspectors' expertise and proficiency for effective detection. Moreover, periodic inspections cannot provide immediate feedback on the safety status of crucial components, they lack preemptive warnings for potential malfunctions, and fail to elevate safety measures during equipment operation. Hence, developing an equipment monitoring system grounded in IoT technology and sensor networks is paramount, especially considering the structural nuances and risk profiles of large amusement facilities. This study aims to develop customized operational status monitoring sensors and an IoT platform for large roller coasters, encompassing the design and fabrication of sensors and IoT platforms and data acquisition and processing. The ultimate objective is to enable timely warnings when monitoring signals deviate from normal ranges or violate relevant standards, thereby facilitating the prompt identification of potential safety hazards and equipment faults.