Condition Of Equipment Research Articles

Метод оценки критериев оптимизации системы электробезопасности в инфраструктуре села

The article provides an analysis of the state of energy availability and equipment of electric installations for infrastructure facilities in rural settlements. The main type of technogenic hazards is marked – the electrical hazard that occurs in emergency modes of operation of electrical installations. The conditions for solving the safety problem and choosing the optimal variant of the electrical safety system are given. The indicators of the technical efficiency of the electrical safety system are determined, characterized by the likelihood of electrical injury to a person and the likelihood of a fire in electrical installations at facilities. The choice of the most appropriate options for the use of electrical protection equipment, providing for the joint use of automatic switches and protective shutdown devices, was made as a result of comparing the likelihood of electrical injury and fire with a normalized risk level of electrical and fire safety. A method for optimizing the electrical safety system is considered according to two particular criteria representing the average values of the probabilities of electrical injury and fire with the equivalence of their values. The task of optimizing the security system is indicated, which consists in constructing a vector criterion of optimality and converting it into a scalar criterion using the "assignment" method to obtain a normalized partial criterion. The normalized private criteria of electrical and fire safety are determined using a scalar criterion for the selection of electrical protection options for an agricultural facility. An algorithm for evaluating the effectiveness of the electrical safety system in dangerous situations of electrical injuries has been built. The description of the construction of an algorithm for modeling the state of fire safety of the system in the event of fires in electrical installations is given. A comprehensive assessment of the normalized criterion for optimizing the electrical safety system was obtained according to two normalized criteria for electrical and fire safety and the choice of optimal electrical protection options at the studied facility. Keywords: TECHNOGENIC HAZARD, ELECTRICAL INSTALLATION, ACCIDENT, ELECTRICAL INJURY, TECHNICAL EFFICIENCY, EVALUATION CRITERIA, OPTIMIZATION OF ELECTRICAL SAFETY SYSTEM, ELECTRICAL PROTECTION

A MODEL FOR ASSESSING SHIPPING SAFETY WITHIN PROJECT-ORIENTATED RISK MANAGEMENT BASED ON HUMAN ELEMENT

Safety risk management in shipping projects is an extremely important aspect aimed at ensuring the success of the project and the safety of all participants in the maritime transportation process. This paper presents an approach to assessing safety risks that considers multiple factors including equipment condition, external circumstances, and human factors. The risk assessment utilizes the probability of accidents and their consequences, as well as the weighting factors of each factor. The results of the assessment are interpreted using a scale that defines the hazard level. The proposed methodology can effectively identify, analyse and manage safety risks, which can contribute to the success and safety of shipping projects. The study also discusses the importance of dividing the crew into functional groups based on the operations performed, which helps to better identify the safety risk for each group. Safety risk assessment is conducted for each operation individually as well as for the entire project or multiple operations to provide a comprehensive safety assessment. The results of the study have shown the feasibility of the proposed method for assessing the safety risks of shipping projects and its suitability to the initial data “safety” taking into account its separate sides, features, as well as the constituent aspects of the concept, systematization of the ship's safety structure in order to develop solutions to improve integral safety and optimize decision-making in emergency situations. Achievement of the general purpose of shipping safety thus means realization of ways of reduction of influence of the human factor on the number of accidents, and an estimation of the degree of influence of a set of factors on a ship during operation.

Sample-by-sample Power Quality Disturbance classification based on Sliding Window Recursive Discrete Fourier Transform

Power Quality Disturbances (PQDs) are caused by large-scale grid connections of nonlinear loads and Distributed Generations (DG). They affect the electrical and electronics equipment performance and may cause serious accidents and economic losses. The classification of PQDs becomes a key issue for end-users in order to enhance the Power Quality (PQ) and comprises an important step in monitoring the Electric Power System (EPS). Accurate PQDs classification is crucial for improving the quality of power supply, monitoring the condition of power equipment, and mitigating power grid issues. In the literature, several methods for detecting and classifying PQDs have been proposed. However, these methods consume a significant amount of processing time due to the detection and sample storage steps required to classify each disturbance class. In this work, we propose a method for classifying PQDs on a sample-by-sample basis. The innovation of this method lies in its ability to classify PQDs for each individual sample, thereby eliminating the need for window-based (batch) processing. To do so, it is applied the Sliding Window Recursive Discrete Fourier Transform (SWRDFT) to real-time estimation of PQ parameters. For the classification stage, decision tree, Artificial Neural Networks (ANN), Ensemble and the Naive Bayes classifiers were employed. The proposed method using Ensemble achieved the most satisfactory results for six different types of PQDs (oscillatory transients, harmonics, notches, sags, spikes and swells), in which oscillatory transients and spikes were identified in the first monitored samples (sample 2 and 11 respectively), sags were identified in the 14th sample counting from the beginning of the disturbance, notches were captured from the 35th sample, and harmonics and swells were identified after the 113th sample.

Система комплексной диагностики элементов высокоскоростного подвижного состава и инфраструктуры: анализ и внедрение

Objective: to consider the issue of integrated diagnostics of the undercarriage equipment of high-speed rolling stock and track infrastructure, to analyze the impact of operational factors on the technical condition of rolling stock, to identify the main problems and propose possible solutions, to consider the prospects of domestic development in the field of integrated diagnostics of rolling stock and infrastructure. Methods: analysis of design features and impact on train operation safety, criteria and parameters of running stability and passenger comfort of the undercarriage equipment of the high-speed electric train “Sapsan”, description of prerequisites and stages of creation, testing and operation of the system of continuous monitoring of parameters of the dynamic system “undercarriage and track” of electric trains “Sapsan”, analysis of the impact of the main operational factors on the technical condition of the rolling stock undercarriage equipment. Results: the key role of the undercarriage equipment of the Sapsan electric train in ensuring operational safety and passenger comfort has been determined. The necessity of creating and implementing a system for continuous monitoring of dynamic reactions in the system “undercarriage equipment - track infrastructure” has been identified. The results of experimental operation of the prototype of the system in the Sapsan electric trains are considered on the example of research of track sections with increased impact on the rolling stock and problems of running stability of the electric train. It is revealed that vertical accelerations of axlebox units with significant amplitudes allow to reveal vertical irregularities of the track up to 3 m long, and constant registration of increased lateral accelerations of the bogie frame shows the necessity of solving the issues related to the geometry of wheel-rail contact at sinuous movement of rolling stock in terms of conicity. The current state and prospects of development of domestic systems of complex diagnostics are determined. Practical importance: the considered diagnostic system allows to carry out complex analysis of problems of technical condition of undercarriage equipment and infrastructure, to determine interdependence and level of influence of operational factors, which is the way to increase reliability and fault tolerance of rolling stock, operational safety and passenger comfort. Its integration into the digital platform will increase the efficiency of rolling stock maintenance planning and enterprise resource management.

Image Analysis Method of Substation Equipment Status Based on Cross‐Modal Learning

In response to increasing power supply needs, maintaining stable substations is vital for reliable electricity. Traditional manual equipment inspections in these substations are inefficient and risky, often leading to hazards and delayed detection of faults. Therefore, there's a growing shift towards using intelligent image recognition technology in video surveillance systems for safer and more efficient inspections. This paper focuses on enhancing the level of intelligent inspection in substations using artificial intelligence‐based visual recognition technology. It introduces a novel small‐sample classification algorithm based on the CLIP architecture. This method uses cross‐modal equipment status information as additional training samples, optimizing the loss function together with image samples, and devises hand‐crafted strategies for text sample inputs to distinguish between equipment and states. The experimental results show that with only 16 training samples per category for 21 types of electrical equipment states, our method achieved a maximum accuracy of 93.38%. This represents a 2.98% higher accuracy than the PPLCNet trained on the full dataset and an 8.63% higher accuracy than the PPLCNet trained with an equal number of samples, with significantly reduced training time. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Creation of an Information and Measuring Protection System against Arc and Partial Discharges in Complete Switchgears of Substations

In the article, the author considers the development and description of the principle of operation of an information and measuring protection system against arc and partial discharges in complete switchgears of electrical substations. The data acquisition by the information and measurement system is carried out on the basis of the acoustic method, followed by analysis and decision-making on disconnecting the power supply circuits when a pre-emergency condition of electrical equipment is detected. A block diagram of an information and measurement system and a schematic diagram of an acoustic sensor are presented. The criteria characterizing the technical condition of the control panel are defined. A method of protection against arc and partial discharges by recording and processing an acoustic signal is proposed.

Метод визначення вібраційних характеристик промислового обладнання в частотній та часовій областях

This paper proposes hardware and software solutions and a data processing method for vibration diagnostics of industrial equipment, which uses discrete Fourier transform and Allan dispersion to increase the accuracy and stability of measurement processes and result processing. The object of this study is the use of vibration diagnostic methods to implement the concept of maintenance of industrial equipment based on monitoring its current and future condition. The subject of this research is the hardware and software solutions for vibration diagnostics systems and methods for processing measurement results. The purpose of this work is to develop a new resource-saving IoT-oriented wireless solution for vibration diagnostics, where the contact method and MEMS accelerometers are used to measure vibration parameters and to evaluate the effectiveness of new methods and algorithms for processing experimental data. The task: justify the need to find new hardware and software solutions and methods of processing the obtained results for the implementation of the service concept based on the tracking of vibration indicators of technical equipment; provide basic hardware and software solutions for the implementation of the cloud platform of vibration diagnostics; develop methods of processing results; check the developed methods and algorithms using mathematical modeling methods and in an on-site experiment; compare the effectiveness of own and competitive solutions; draw conclusions and formulate a plan for further research. Conclusions. It has been proven that the combination of known analysis methods in the time and frequency domains with multi-level processing gives better results than analogous methods. The developed hardware and software tools and the method of processing measurement results effectively implement the contact method of vibration measurement, which provides the possibility of tracking the state of technical equipment. The developed equipment for the calibration of vibration acceleration sensors can reduce accelerometer errors. Further areas of research are the search for the optimal distribution of calculations on IoT levels, reducing the computational complexity of algorithms, increasing the time of continuous autonomous operation of the lower-level microcontroller, creating micro services for time-series analysis, and researching the dependence of the technical state of the equipment on the calculated Allan deviation.

Design and Application of Intelligent Control System for Molten Iron Transportation Based on 5G Technology

Molten transport is an important link in the iron and steel enterprise production, involves many complex factors, artificial management is difficult. Therefore, puts forward a kind of molten iron transport wisdom control system based on 5G technology, which mainly contains the intelligent identification tracking system, equipment status collection information acquisition system, locomotive vehicle terminal system, etc. Combined with the analysis of the actual application situation, the system could integrate all the processes and elements of molten iron production and transportation, realize the integration of operation and management, and also promote the improvement of the turnover efficiency of molten iron tank, reduce the demand for personnel, and reduce the labor cost.

Accelerating lPBF process optimisation for NiTi shape memory alloys with enhanced and controllable properties through machine learning and multi-objective methods

ABSTRACT NiTi shape memory alloys (SMAs) prepared by the laser powder bed fusion (LPBF) technology have demonstrated promise in aerospace and medical applications. Nevertheless, ensuring repeatability and customised design in printed parts remains challenging. This paper addressed this challenge by introducing a machine learning model that effectively predicted the performance of NiTi SMAs across diverse LPBF processing and equipment conditions. Trained on a dataset of 195 entries from 23 publications, the model accurately predicted critical metrics, including density, ultimate tensile strength, elongation, and thermal hysteresis. Validation using data from eight experimental groups confirmed its reliability and generalisation capability. Multi-objective optimisation identified processes yielding synergistic improvements, achieving a tensile strength of 783 ± 8 MPa, an elongation of 13.7 ± 0.8% and a low hysteresis of 15.1 K. This study also discussed strategic applications of the model for LPBF process optimisation and proposed a method for constructing tailored LPBF process maps for specific NiTi alloy performance attributes.

Monitoring the operating state of crystal growth process based on digital twin model

The reliable assessment of the operational status during the silicon single crystal growth process is a prerequisite for ensuring system safety and improving crystal quality. However, in the actual silicon single crystal growth process, due to limitations in manpower, material resources, financial resources, and current technical methods, the establishment of monitoring models is still in its infancy. To address this issue, this paper proposes a hybrid deep belief network (HDBN) algorithm aided by the digital twin (DT) model to achieve real-time monitoring of equipment operational status. Firstly, this study constructs the DT model based on the basic principles of crystal growth, mainly to achieve high-precision simulation of the actual silicon single crystal growth process and generate abnormal data for the equipment. This operation can expand the sample set, enhance the diversity and coverage of data, and effectively solve the problem of insufficient sample size. Secondly, this study uses the variational mode decomposition (VMD) algorithm to decompose the dataset composed of obtained abnormal and normal data, and constructs sub-deep belief network (DBN) for the decomposed subsequences to capture deep feature information at different frequencies of the data. Subsequently, based on the concept of ensemble learning, the outputs of each sub-DBN network are used as inputs to construct the overall DBN network, achieving monitoring of the equipment operational status. Through the combination of VMD decomposition and DBN networks, this algorithm can better capture the frequency characteristics and time-domain features of the signal, enhancing monitoring accuracy. Experimental results show that this algorithm can accurately identify abnormal equipment states, effectively improve monitoring performance, and is of significant importance for the optimization and control of the semiconductor-grade silicon single crystal growth process, contributing to increased production efficiency and product quality.

Pt3 cluster doped SnS2 monolayer as a gas-sensing material to C4F7N decomposition: A DFT study

Perfluoroisobutyronitrile (C4F7N) is a new type of eco-friendly insulating gas widely used in various gas insulation systems. An effective method for diagnosing the operational status of insulation equipment is to design gas sensor materials to detect C4F7N breakdown products. Using density functional theory, we investigated the sensing ability of the Pt3 cluster doped SnS2 monolayer for common C4F7N decomposition gases (CF4, COF2, C2N2, and CF3CN). We explored the adsorption energy, charge transfer, adsorption distance, and density of states of the Pt3-SnS2 monolayer as a C4F7N breakdown gas sensor. The results indicate that the doping of Pt3 clusters has a relatively small improvement in the adsorption capacity of CF4 and COF2 gases, indicating physical adsorption, but a significant improvement in the adsorption capacity of C2N2 and CF3CN gases, indicating chemical adsorption. This work provides additional potential applications for Pt3 cluster doped SnS2 monolayer gas sensing materials.

A lightweight network architecture for traffic sign recognition based on enhanced LeNet-5 network.

As an important part of the unmanned driving system, the detection and recognition of traffic sign need to have the characteristics of excellent recognition accuracy, fast execution speed and easy deployment. Researchers have applied the techniques of machine learning, deep learning and image processing to traffic sign recognition successfully. Considering the hardware conditions of the terminal equipment in the unmanned driving system, in this research work, the goal was to achieve a convolutional neural network (CNN) architecture that is lightweight and easily implemented for an embedded application and with excellent recognition accuracy and execution speed. As a classical CNN architecture, LeNet-5 network model was chosen to be improved, including image preprocessing, improving spatial pool convolutional neural network, optimizing neurons, optimizing activation function, etc. The test experiment of the improved network architecture was carried out on German Traffic Sign Recognition Benchmark (GTSRB) database. The experimental results show that the improved network architecture can obtain higher recognition accuracy in a short interference time, and the algorithm loss is significantly reduced with the progress of training. At the same time, compared with other lightweight network models, this network architecture gives a good recognition result, with a recognition accuracy of 97.53%. The network structure is simple, the algorithm complexity is low, and it is suitable for all kinds of terminal equipment, which can have a wider application in unmanned driving system.

Prediction maintenance based on vibration analysis and deep learning — A case study of a drying press supported on a Hidden Markov Model

The main objective of this paper is to describe a methodology that was developed to support maintenance decision-making methods based on equipment condition. Condition-Based Maintenance allows to increase equipment availability and maximize investments. This is mainly due to the prevention of unexpected equipment downtime. By avoiding turning on/off industrial equipment, production flows are more efficient, allowing manufacturers to improve the quality of the end-product. The industry aims more and more to correspond satisfying customer expectations. We argue that the methodology developed in this paper adds value to the existing literature, namely because the fact that it is possible to anticipate the state of an equipment without a large amount of support data. In other words, although one could find information gaps regarding the occurrence of failures, it was possible to accurately assess the state of the equipment. This approach is robust, as it can be used in distinct equipment with different sensors, making this methodology generalizable for Condition-Based Maintenance. The paper presents the validation of the preceding through a case study on drying presses in the paper industry. To do so, three states were adopted, namely: “Proper function”; “Alert state”; and “Equipment failure”. The methodology follows a series of steps, going through the collection of values from vibration sensors, imputation of values using Deep Artificial Neural Networks through on-line sensors, until reaching the last stage of classification carried out by the Hidden Markov Model. Through optimized observations from the previous steps, it was possible to define the hidden states through the Viterbi algorithm, which corresponds to the health states of the equipment. Additionally, it was possible to demonstrate that the proposed methodology can accurately characterize the condition states of the equipment based on the data obtained and can be generalized to other types of equipment.

Development of a CRISPR/Cas12a-based fluorescent detection method of Senecavirus A

BackgroundSenecavirus A (SVA), identified in 2002, is known to cause porcine idiopathic vesicular disease (PIVD), which presents with symptoms resembling other vesicular diseases. This similarity complicates field diagnosis. Conventional molecular diagnostic techniques are limited by their cost, sensitivity, and requirement for complicated instrumentation. Therefore, developing an effective and accurate diagnostic method is crucial for timely identification and isolation of affected pigs, thereby preventing further disease spread.MethodsIn this study, we developed a highly-specific and ultra-sensitive SVA detection method powered by CRISPR/Cas12a. To enhance the availability in laboratories with varied equipment conditions, microplate reader and ultraviolet light transilluminator were introduced. Moreover, PCR amplification has also been incorporated into this method to improve sensitivity. The specificity and sensitivity of this method were determined following the preparation of the recombinant Cas12a protein and optimization of the CRISPR/Cas12a-based trans-cleavage system.ResultsThe method demonstrated no cross-reactivity with ten kinds of viruses of swine. The minimum template concentration required to activate substantial trans-cleavage activity was determined to be 106 copies/µL of SVA templates. However, when PCR amplification was incorporated, the method achieved a detection limit of one copy of SVA templates per reaction. It also exhibited 100% accuracy in simulated sample testing. The complete testing process does not exceed three hours.ConclusionsImportantly, this method utilizes standard laboratory equipment, making it accessible for use in resource-limited settings and facilitating widespread and ultra-sensitive screening during epidemics. Overall, the development of this method not only broadens the array of tools available for detecting SVA but also holds significant promise for controlling the spread of PIVD.

A physics-based dimension reduction and modeling method for monitoring data and its application to tunnel engineering

The promotion of intelligent control of engineering equipment requires extracting vital information from numerous original features in the monitoring data. Data-driven methods solely rely on data distribution to extract dimensional information, facing difficulties in comprehensively understanding and scrutinizing system behavior. To satisfy the control and optimization requirements of intelligent systems, this paper introduces a physics-based dimension reduction and modeling method. According to scientific principles, original features are transformed into causal variables with physical meaning. Then important causal variables are selected through regularization in machine learning. Finally, a concise prediction model with physical representation is established. An application to tunnel engineering demonstrates that the generated low-dimensional space reveals the essential laws between the control parameter and its main influencing factors (i.e., the geological conditions, operating status, and structural characteristics of the tunneling equipment), which can further provide a better prediction model balancing accuracy and dimensionality.

Improved spectral amplitude modulation for tacholess estimation of rotation speed

Rotation speed is key information for rotating machinery, whether it’s a negative feedback control method based on speed or equipment status monitoring and fault diagnosis. In order to accurately and efficiently estimate the rotation speed under tacholess condition, a novel method named improved spectral amplitude modulation (ISAM) is proposed in this paper. In ISAM, the vibration signal is first filtered through a band-pass filter to eliminate interference from irrelevant frequency components. Subsequently, an appropriate weight, which modifies the amplitude spectrum of the time–frequency representation (TFR) to decompose different components based on their powers, is selected to enhance the instantaneous frequency (IF) of rotation speed. The modified signal is then reconstructed based on the edited amplitude and original phase. Finally, the IF can be extracted from the TFR of the modified signal with the multiple time–frequency curve extraction (MTFCE) technique. Additionally, entropy is utilized to guide the selection of weight. The effectiveness of the proposed method is evaluated through simulation analysis and case studies. The results show that the ISAM can accurately estimate rotation speed, with the mean absolute error (MAE) at 0.13. Compared to an existing IF estimation method, ISAM can reduce computation time by 95%.

Thermal imaging control of electrical equipment of industrial enterprises

RELEVANCE. Thermal imaging control is used to monitor the technical condition of electrical equipment, as well as contacts and contact connections of switching devices, which allows to increase the efficiency of work.OBJECT. Timely detection of defects of contacts and contact connections of low-voltage switching devices to prevent emergencies.METHODS. Determination and control of technical condition of electrical equipment, contacts and contact connections of apparatuses in real time by means of indicators and thermal imaging control.RESULTS. The values of contact heating temperatures from material and type of contact connections were analysed. The criteria for estimation of the state of contacts of switching devices with the help of indicators of their state have been determined. The optimum values of loading coefficients of the circuit breaker in moulded case VA04 and contactors KMI and KTI are obtained. Emergency cases at the industrial object are shown, which prove the necessity of application of indicators and thermal imaging control as one of the tools of automation of technical condition control of low-voltage electrical equipment.CONCLUSIONS. The offered noncontact method of estimation and control of technical condition of electrical equipment, contacts and contact connections of devices in real time with the help of indicators and thermal imaging control allows both to receive data in real time mode and to increase inter-repair periods for power equipment and to increase reliability of operation of the whole system.

System for assessment and prediction of the technical condition of power oil-filled transformer equipment of distribution networks using machine learning

RELEVANCE the research is to develop a new system for assessing the technical condition of power oil-filled transformer equipment of distribution networks.OBJECT. To increase the accuracy of assessing the technical condition of power oil-filled transformer equipment (POTE) of distribution networks through the use of machine learning methods. Currently, an increase in the volume of analyzed information about the state of the management system of distribution networks leads to significant changes in the choice of data processing methods. The use of machine learning methods is associated both with the need to apply operational experience (in the form of expert assessments) and to obtain objective assessments of the condition of transformer equipment of distribution networks from instrumentation and sensors.METHODS. This work uses research methods such as mathematical modeling and the method of paired comparisons. As an example, we consider the oil-filled power transformer TMN-6300, its diagnostic parameters, external and operating parameters. The technical condition of the TMN-6300 transformer is assessed and a predictive model is created based on the existing monitoring system and machine learning methods, which make it possible to formalize expert knowledge and automate the process of data processing and analysis.RESULTS. A database has been created to assess and predict the technical condition of POTE of distribution network management systems. The algorithm for predicting the technical condition of POTE of the technical equipment in the form of an artificial neural network model was tested in the developed assessment system.CONCLUSION. The results of assessing and predicting the technical condition of POTE of the metering system of distribution networks obtained in this work prove the unconditional relationship between the parameters of the metering system and external, operating parameters. The data obtained as a result of modeling helps to increase the accuracy of forecasting the technical condition and determine the longterm prospects for the functioning of POTE the equipment management system, timely maintenance and repairs over the course of years and months.

Explainable proactive control of industrial processes

One of the goals of Industry 4.0 is the adoption of data-driven models to enhance various aspects of the manufacturing process, such as monitoring equipment conditions, ensuring product quality, detecting failures, and preparing optimal maintenance plans. However, many machine-learning algorithms require a large amount of training data to reach desired performance. In numerous industrial applications, such data is either not available or its acquisition is a costly process. In such cases, simulation frameworks are employed to replicate the behavior of real-world facilities and generate data for further analysis. Simulation frameworks typically provide high-quality data but are often slow which can be problematic when real-time decision-making is required. Control approaches based on simulation-based data commonly face challenges related to inflexibility, particularly in dynamic production environments undergoing frequent reconfiguration and upgrades. This paper introduces a method that seeks to strike a balance between the reliance on simulated data and the limited robustness of simulation-based control methods. This balance is achieved by supplementing available data with additional expert knowledge, enabling the matching of similar data sources and their combination for reuse. Furthermore, we augment the methods with an explainability layer, facilitating collaboration between the human expert and the AI system, leading to informed and actionable decisions. The performance of the proposed solution is demonstrated through a case study on gas production from an underground reservoir resulting in reduced downtime, heightened process reliability, and enhanced overall performance. This paper builds upon our conference paper (Kuk et al., 2023), addressing the same problem with an extended, more generic methodology, and presenting entirely new results.

Research on Critical Factors Influencing Organizational Resilience of Major Transportation Infrastructure Projects: A Hybrid Fuzzy DEMATEL-ISM-MICMAC Approach

To strengthen major transportation infrastructure projects’ (MTIPs’) organizational resilience and fortify their capacity for crisis management and project risk prevention. In this paper, based on the resilience theory development process, the connotation of organizational resilience of MTIPs is defined, and 20 influencing factors of organizational resilience of MTIPs are extracted from four categories of stability, redundancy, adaptability, and rapidity according to the literature analysis and case study method. The significance, causality, and multilevel recursive order structure of the influencing factors were investigated by the fuzzy DEMATEL-ISM approach, and their driving and dependent characteristics were analyzed through MICMAC. The results indicate that risk warning and prediction, human resources management, inter-organizational synergies, resource reserve situations, organizational leadership, and organizational learning are the crucial factors of organizational resilience in MTIPs. There are three levels and five ranks in the multilevel recursive rank structure of the factors affecting MTIPs’ organizational resilience. Among them, risk warning and prediction, equipment condition and performance, human resources management, and organizational leadership have the deepest impact on organizational resilience in MTIPs. The findings can clarify ideas for subsequent research on organizational resilience in this area and inform project decision-makers in developing strategies for optimizing organizational resilience.