Critical Equipment Research Articles

Vibration energy harvesting from an unmanned surface vehicle: Concept design, open sea tests and harvester optimization

With the growing worldwide attention to marine environment protection, marine resource exploration, and so on, unmanned surface vehicles (USVs) have become one of the critical mobile marine observation equipment. However, USVs generally rely on limited onboard batteries to provide electricity, and thus, they have low endurance and poor load capacity. The lack of a long-term power supply is currently a great challenge for the further application and development of USVs. Harvesting energy from the working environment of USVs is a promising solution to address that problem. To demonstrate the possibility of this scheme, a physical prototype of a novel USV with a built-in vibration energy harvester has been fabricated and tested in an open sea for the first time. Moreover, a full-coupled mathematical model is developed to further optimize the power performance of the USV based on the Taguchi method. Experimental results show that this prototype can generate a maximum instantaneous electric power of 277.71 mW, 112.95 mW and 101.27 mW when the USV is in a status of towage, propulsive and free-floating, respectively. Simulation results present that the optimized USV can achieve a mean electric power of 246.14 mW, which is improved by 49.88% compared with the unoptimized results of the developed prototype.

Shaking table tests of a switching power cabinet considering physical damage and functionality loss

Switching power cabinets are critical equipment that converts alternating current (AC) to direct current (DC) for electrical power system of telecommunication buildings. Post-earthquake damage to the cabinet may result in entire functionality loss of telecommunication buildings, which will have a significant impact on post-earthquake emergency response. In this study, unidirectional shaking table tests were conducted separately in X-direction and Y-direction to evaluate the seismic responses of a switching power cabinet. The seismic input motion was generated based on required response spectrum of YD 5083 code. The peak table acceleration levels were scaled from 0.3 g to 2.5 g in order to capture progressive damage of the switching power cabinet. Based on the test results, the physical damage and functionality loss of tested cabinet were analyzed first. Then the response characteristics of the cabinet were evaluated and explained in terms of the natural frequency, acceleration, and deformation response. Both outside cabinet and in-cabinet acceleration amplification factors were analyzed. The seismic fragility curves of switching power cabinet were also developed considering four observed seismic damage states.

IMPLEMENTATION OF OEE INDICATOR IN DENTAL IMPLANTS MACHINING MACHINE

Introduced by Seiichi Nakajima, one of the creators of Total Productive Maintenance (TPM), the indicator OEE – Overall Equipment Effectiveness – is used as a metric for operational performance, being a productive management tool increasingly used in the industry. Study, lacks an effective indicator to indicate the efficiency of its equipment. Thus, this work applied the Overall Equipment Effectiveness (OEE) indicator in order to identify the overall efficiency of the most critical equipment in the manufacturing process of dental implants in a machining company located in Joinville-SC. access to machine data in the company which served as the study environment, collect the main equipment data, perform the calculation of the OEE indicator with the information obtained and direct the improvement teams with the results of indicators obtained. Therefore, the objective of the referred work is to implement the OEE indicator in a dental implant machining machine. Among the performance, quality and availability indicators, only the performance indicator was above the recommended global average, which is 95%. For the other two factors, incremental measures had to be studied and recommended to the improvement teams, since the value obtained for quality was 98% and for availability was 85%, against the recommended 99% and 95% respectively. The work shows that the adapted methodology was efficient to identify the losses of the process, to measure the real use of the equipment and to apply improvement actions in the two indicators that presented values ​​below the globally recommended.

Influence of Ultra-High-Voltage hybrid reactive power compensation on interruption performance of circuit breakers under different faults

For extremely large ultra-high-voltage (UHV) power transmission and distribution lines with typical project using hybrid reactive power compensation (HRPC), single-phase earth fault (1LG), three-phase ground fault (3LG), phase-to-phase fault (2LS), and two-phase grounding fault (2LG) often occurs. Circuit breaker is one of the most critical equipment of UHV transmission system. And the interruption performance is crucial to circuit breakers’ breaking capability. In this study, formal expressions of the indicators for the interruption performance of the circuit breaker are deduced in case of different faults based on the equivalent-valued parameter circuit based on the structure a UHV demonstration project. The simulations are used to validate the accuracy of formal expressions. Then, the influence of such factors as the location of the fault and the degree of HRPC on the interruption performance of the 1LG, 2LG, 3LG, and 2LS is analyzed. Finally, the interruption performance of circuit breakers under different faults are compared. A quantitative evaluation of the influence of changes in the relevant indicators on the interruption performance of circuit breakers can provide a technical basis for choosing the appropriate design of the circuit breaker in light of the application as well as equipment development and assessment.© 2017 Elsevier Inc. All rights reserved.

An Improved Mask R-CNN Micro-Crack Detection Model for the Surface of Metal Structural Parts.

Micro-crack detection is an essential task in critical equipment health monitoring. Accurate and timely detection of micro-cracks can ensure the healthy and stable service of equipment. Aiming at improving the low accuracy of the conventional target detection model during the task of detecting micro-cracks on the surface of metal structural parts, this paper built a micro-cracks dataset and explored a detection performance optimization method based on Mask R-CNN. Firstly, we improved the original FPN structure, adding a bottom-up feature fusion path to enhance the information utilization rate of the underlying feature layer. Secondly, we added the methods of deformable convolution kernel and attention mechanism to ResNet, which can improve the efficiency of feature extraction. Lastly, we modified the original loss function to optimize the network training effect and model convergence rate. The ablation comparison experiments shows that all the improvement schemes proposed in this paper have improved the performance of the original Mask R-CNN. The integration of all the improvement schemes can produce the most significant performance improvement effects in recognition, classification, and positioning simultaneously, thus proving the rationality and feasibility of the improved scheme in this paper.

Ensuring Fair Play: Abuse Of Dominance And Intellectual Property Rights Top Of Form Bottom Of Form

Dominance in the market gives businesses many advantages, enabling them to change business dynamics and influence customer choices. This dominance, while used improperly, can damage clients via way of means of stifling competition and innovation. The distinct rights that intellectual property rights deliver authors and inventors over their works, on the alternative hand, lead them to critical equipment for selling innovation. Abuse of dominance and Intellectual Property rights are associated while dominant corporations make the most of their intellectual property rights to stifle competition. This article attempts to examines the feasible reasons of the subject matter, which includes inflated licensing charges, unwarranted litigation, and patent hold-up tactics. The interplay among abuse of dominance with intellectual property rights (IPR) marks a pivotal factor withinside the improvement of competition law. This article captures the essence of this complicated connection and affords a window into its complicated dynamics. It further attempts to emphasise the critical factor of convergence among those fields: the sensitive stability among selling innovation and retaining marketplace opposition receives disenchanted while dominant groups use their intellectual property rights to impede competition. Such abuse of IPR might also additionally take many unique forms, from extortionate licensing charges to pointless court cases meant to save your competition from coming into the marketplace. Case studies that function an examples from across the globe provide important insights into the rational consequences of intellectual property rights violations. The difficulties in finding the appropriate stability are demonstrated by notable instances such as the legal actions related to Qualcomm's SEP and Microsoft's antitrust actions in the EU. Emerging technologies in the virtual age, like as biotechnology and artificial intelligence, raises new concerns about supremacy and intellectual property rights. One example of an increasing burden in 5G technology is Standard-essential patents (SEPs). Using well-known case studies and international regulatory reactions, this article also attempts to provide a brief discussion of the issues and headaches associated with the abuse of dominance within the framework of intellectual property rights. Strict regulatory scrutiny is necessary to prevent dominant marketplace contributors from engaging in anti-competitive behaviour. The conflict between preventing anti-competitive behaviour and promoting innovation through intellectual property rights safety is a fundamental dilemma. Finding a balance between the two needs is essential if one is to sell innovation and keep markets competitive. In this regard, the author attempts to highlight how crucial it is to handle the junction between rights to intellectual property and abuse of dominance in an appropriate manner. It draws attention to the necessity of prudent legislation that preserves innovative incentives while maintaining sincere competition. In order to fully utilize intellectual property rights for the goodness of society, it is imperative to achieve this delicate balance. In a global economic system that is rapidly changing, legislators, lawyers, corporations, and students must all comprehend and navigate the intricate relationship between abuse of power and intellectual property rights. This article lays the groundwork for additional investigation and debate in the article by offering a concise yet comprehensive synopsis of the key traits and problems in this intricate field.

Enhancing high-speed networks using RGB-based WLAN through Ro-FSO integration in the 5 GHz band

Abstract The limited bandwidth constraints imposed by conventional wireless carriers pose a significant hurdle when it comes to the delivery of high-speed broadband services. In response to this challenge, Radio over Free Space Optics (Ro-FSO) has emerged as a viable and innovative solution, seamlessly amalgamating wireless and optical systems. This integration proves particularly invaluable in sensitive environments, such as hospitals, where the risk of electromagnetic interference disrupting critical medical equipment is a real concern. Ro-FSO offers a disruption-free avenue for high-speed data transmission, positioning it as the ideal choice for broadband services, including Wireless Local Area Networks (WLANs). Within the scope of this study, we introduce a high-speed Ro-FSO link, showcasing the capability to concurrently transmit three independent channels with Red, Green and Blue (RGB) laser respectively, each supporting a robust 1 Gbps data rate. These data streams are skilfully up-converted to the 5 GHz RF bands, encompassing transmission distances of 650 m across the FSO channel. Our numerical simulation findings underscore the successful transmission of all the channels using wavelength division multiplexing (WDM), seamlessly meeting the prescribed Bit Error Rate (BER) and eye pattern criteria, solidifying the Ro-FSO’s standing as a promising solution for high-speed broadband delivery.

Research on the control strategies of data flow transmission paths for MPTCP-based communication networks.

The performance of multipath transmission control protocol (MPTCP) subflow through the enhancement mechanism of the MPTCP communication is improved. When dealing with multiple MPTCP subflows occupying the same transmission path, critical issues such as selection and optimization of multipath, and efficient scheduling of available multiple tracks are effectively addressed by incorporating the technology called software defined network (SDN) that is constructed based on four key parameters, namely, network transmission bandwidth, transmission paths, path capacity, and network latency. Besides, critical equipment such as the network physical device layer and SDN controller are integrated with the four parameters. So, the network model defines the transmission control process and data information. Considering the predetermined total network bandwidth capacity to select multiple paths, the adequate bandwidth capacity is determined by defining the data transfer rate between MPTCP terminals and MPTCP servers. However, the processing latency of the OpenFlow switch and the SDN controller is excluded. The effective network transmission paths are calculated through two rounds of path selection algorithms. Moreover, according to the demand capacity of the data transmission and the supply capacity of the required occupied network resource, a supply and demand strategy is formulated by considering the bandwidth capacity of the total network and invalid network latency factors. Then, the available network transmission path from the valid network transmission path is calculated. The shortest path calculation problem, which is the calculation and sorting of the shortest path, is transformed into a clustering, Inter-Cluster Average Classification (ICA), problem. The instruction of the OpenFlow communication flow is designed to schedule MPTCP subflows. Thus, various validation objectives, including the network model, effective network latency, effective transmission paths, supply-demand strategies, ineffective transmission paths, shortest feasible paths, and communication rules are addressed by the proposed method whose reliability, stability, and data transmission performance are validated through comparative analysis with other conventional algorithms. Found that the network latency is around 20 s, the network transmission rate is approximately 10Mbps, the network bandwidth capacity reaches around 25Mbps, the network resource utilization rate is about 75%, and the network swallowing volume is approximately 3M/s.

Strategic Reliability Centered Maintenance Decision Analysis Model Using Fuzzy and Analytic Hierarchy Process for Refinery Valves

Modern industries, particularly oil and gas, necessitate robust maintenance management and optimization for increased profits, plant availability, and maintenance cost reduction. The maintenance strategy is therefore crucial, especially under global economic pressures on equipment reliability. This study examines the present maintenance management practices for refinery valves, critical equipment whose failure can have severe consequences. Many existing models for selecting critical equipment in maintenance management face limitations due to inadequate decision analysis and exclusion of key criteria. This research proposes a strategy model, combining artificial intelligence and multi-criteria decision-making techniques like Fuzzy Logic and Analytical Hierarchy Process, to fill this gap and manage refinery valves' maintenance. Proven by case studies and interviews, the model improves safety, reliability, operational availability and enables operators to monitor their decision consequences. The model can convince asset managers to invest in maintenance initiatives.

Mobile Energy Storage System Scheduling Strategy for Improving the Resilience of Distribution Networks under Ice Disasters

The distribution system is easily affected by extreme weather, leading to an increase in the probability of critical equipment failures and economic losses. Actively scheduling various resources to provide emergency power support can effectively reduce power outage losses caused by extreme weather. This paper proposes a mobile energy storage system (MESS) scheduling strategy for improving the resilience of distribution networks under ice disasters. First, the influence of wind and ice loads on power transmission lines is analyzed, and a detailed fault statistical model of transmission lines under an ice disaster is established. Then, the MESS scheduling problem considering the coupling of transportation-distribution networks is transformed into a two-stage optimization problem. The first stage determines the optimal configuration scheme for MESS, and in the second stage, the optimal path selection for MESS can be obtained. Finally, the effectiveness and feasibility of the algorithm proposed in this paper are verified through an improved IEEE-33 node testing system.

Optimizing Overall Equipment Effectiveness Using Kepner-Tregoe Decision Analysis: A Case Study of Semi-lean Pumps in Ammonia Plant 2 of PT Pupuk Kalimantan Timur

The increasing demand for Ammonia production requires innovative approaches to ensure equipment reliability and operational efficiency in its production facility. This study employs a multi-faceted approach to address the challenges associated with the reliability of Semi-lean Pump 107-JB in Ammonia Plant 2 of PT Pupuk Kalimantan Timur, a critical equipment in its production facility. Utilizing the Kepner-Tregoe Decision Analysis Method, the study evaluates various alternative solutions to enhance the pump’s reliability and overall equipment effectiveness (OEE). The research identifies the root causes of the pump’s low reliability, indicated by its declining OEE from 2021-2022. It is done through a series of OEE calculations, 6 Big Losses analysis, and Current Reality Tree analysis. Four major phenomena contributed to the issue: inadequate maintenance procedures and equipment, pump operation beyond its design capacity, insufficient training and competency of manpower, and improper material selection and compatibility testing. The study employs a combination of SCAMPER techniques and the Kepner-Tregoe Decision Analysis framework to develop feasible alternative solutions, which categorizes evaluation criteria into ‘Musts’ and ‘Wants.’ Factors such as cost, feasibility, and potential impact on OEE are assessed. Among the alternatives evaluated, implementing IoT Probe Sensors for real-time monitoring emerged as the most effective solution, addressing the research objective. The thesis culminates in the development and execution of a Maintenance Implementation Plan, which is subsequently assessed through OEE performance metrics. Post-implementation, the Semi-lean Pump 107-JB is estimated to experience an increase in OEE from 63.63% in 2022 to 96.74% in 2024, significantly reducing downtime and contributing an estimated savings of $12.6 million to the company. The research provides actionable insights for improving Semi-lean Pump 107-JB’s reliability. It offers a structured methodology for evaluating alternative solutions for similar applications in industrial settings, particularly rotating equipment in the petrochemical industry.

Asset Management Support Tool for Energy Systems using AHP and Monte Carlo Methods applied to Power Transformers

The assets of electric power transmission systems are characterized by their high cost and complexity, leading utilities to seek ways to improve the efficiency and economic-financial performance of these assets. Thus, this article proposes a tool to support asset management for electricity transmission systems. This tool considers the useful life of the equipment, its importance to the system, financial aspects, and the forecast of electricity demand. An evaluation of the criteria that impact decision-making on the replacement of assets with specialists was carried out. Thus, the tool uses the Analytic Hierarchy Process method to classify the most critical equipment in the system. For analysis of future scenarios, the Monte Carlo Method was incorporated into the tool to simulate the behavior of equipment during a defined time horizon. As a result, the tool presents a ranking of the most critical equipment in the system under analysis within the simulated time horizon. The tool was applied in a case study with real data in the power transformer of a Brazilian utility. The tool helps in decision-making indicating changes that may be made in the period under review, their likely impact on equipment loading, and the list of critical transformers in the system.

Spring-like Triboelectric Nanogenerator for Monitoring Body Vibration State of the Ship Power Equipment

In the navigation process, monitoring the running state of ship power plant equipment is crucial. In bad weather, when the critical equipment is abnormal, it is especially necessary to find out the root cause of the failure as soon as possible. In this case, it is required to use rapid detection equipment to detect and judge the key parameters. This paper proposes a vibration sensor (VS-TENG) of triboelectric nanogenerators based on spring vibration. The sensor adopts the spring structure inside and vibrates with the ship power equipment to collect the low-frequency vibration energy. This paper uses the VS-TENG sensors of two different spring parameters to study the electrical signal output under the excitation conditions of varying vibration frequencies. The results show that in the frequency range of 3–500 Hz, the efficient processing of different vibration excitation frequency signals can be realized, and the vibration frequency can be accurately identified. The error of medium-high frequency identification in VS-TENG is less than 1%. Especially at the resonant frequency, the maximum voltage output value can be achieved. On the PT500 Mini test bench, VS-TENG can reasonably identify the motor frequency and shutdown state. Therefore, VS-TENG can be applied to the condition monitoring of the vibration of the ship’s power plant and has a broad application prospect.

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoT-based environmental control system that integrates sensor technology and advanced machine learning decision support. Deploying a network of wireless sensors within the fish farm, we continuously collect real-time data on crucial environmental parameters, including water temperature, pH levels, humidity, and fish behavior. This data undergoes meticulous preprocessing to ensure its reliability, including imputation, outlier detection, feature engineering, and synchronization. At the heart of our system are four distinct machine learning algorithms: Random Forests predict and optimize water temperature and pH levels for the fish, fostering their health and growth; Support Vector Machines (SVMs) function as an early warning system, promptly detecting diseases and parasites in fish; Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule based on real-time environmental conditions, promoting resource efficiency and fish productivity; Neural Networks manage the operation of critical equipment like water pumps and heaters to maintain the desired environmental conditions within the farm. These machine learning algorithms collaboratively make real-time decisions to ensure that the fish farm's environmental conditions align with predefined specifications, leading to improved fish health and productivity while simultaneously reducing resource wastage, thereby contributing to increased profitability and sustainability. This research article showcases the power of data-driven decision support in fish farming, promising to meet the growing demand for seafood while emphasizing environmental responsibility and economic viability, thus revolutionizing the future of fish farming.

Research on real-time perception method of key targets in the comprehensive excavation working face of coal mine

Aiming at the real-time perception problem of key target objects caused by harsh environmental factors of high dust, low illumination, motion blur, and multi-target mixing in the comprehensive excavation working face of coal mine, a multi-target detection and tracking algorithm based on DDEB-YOLOv5s and StrongSORT is proposed. First, the YOLOv5s model is improved by using C3-Dense module, decoupled head, ECIoU loss function, and weighted bi-directional feature pyramid network to enhance the detection performance of the model in complex backgrounds of coal mine and complete the design of the DDEB-YOLOv5s multi-target detection network. Second, the DDEB-YOLOv5s algorithm is used as a detector and combined with the StrongSORT tracking algorithm to track critical equipment and miners in the complex context of coal mine. Experiments were conducted on the dataset of comprehensive excavation working face, and the experimental results show that the proposed DDEB-YOLOv5s has the best integrated detection performance compared with other YOLO series target detection algorithms, and its mean value of detection accuracy reaches 91.7%, which is 4.9% higher than that of the original YOLOv5s model. In addition, compared to the three tracking models, (YOLOv7-tiny)-(BoT-SORT), YOLOv5s-DeepSORT, and YOLOv8s-Bytetrack, the (DDEB-YOLOv5s)-StrongSORT model has the best tracking performance (with a mean tracking accuracy of 94.2%) and the least number of identifier switches. Finally, the real-time perception method proposed in this study for the key target of the coal mine working face can provide new technical support and effective guarantee for coal mine safety production.

Experimental and numerical investigation of the expansion mechanism of beam-end expansion devices in large-span bridge

The beam-end expansion device of a large-span bridge is critical equipment for relieving temperature stress and also a weak point. In this paper, a detailed analysis of the expansion mechanism and jamming phenomenon of the beam-end expansion device is conducted for the first time. A dynamic model of the beam-end expansion device is established using complex flexible body coupled kinematics theory. The model overcomes two major challenges in simulation: (1) the expansion function of the scissor device provides the beam-end expansion capability while transmitting force to drive the movable sleepers, and (2) the coupling effects in the longitudinal, vertical, and horizontal directions between the movable sleepers and the upper structure need to be simulated to provide not only vertical support stiffness but also force on contact surfaces during expansion motion. The phenomenon of jamming caused by the asymmetry of the scissor device is found and the reason is analyzed. By comparing the paths of load transmission caused by different types of scissor devices, the influence on scissor devices and movable steel sleepers is summarized. The conclusions derived from theoretical deductions are consistent with the simulation results and validated through four monitoring data sets. The research findings of this paper can provide insights for addressing the design, operation, and maintenance issues of beam-end expansion devices in large-span bridge projects, ensuring that they meet the requirements of engineering practice.

Few-Shot Learning Approaches for Fault Diagnosis Using Vibration Data: A Comprehensive Review

Fault detection and diagnosis play a crucial role in ensuring the reliability and safety of modern industrial systems. For safety and cost considerations, critical equipment and systems in industrial operations are typically not allowed to operate in severe fault states. Moreover, obtaining labeled samples for fault diagnosis often requires significant human effort. This results in limited labeled data for many application scenarios. Thus, the focus of attention has shifted towards learning from a small amount of data. Few-shot learning has emerged as a solution to this challenge, aiming to develop models that can effectively solve problems with only a few samples. This approach has gained significant traction in various fields, such as computer vision, natural language processing, audio and speech, reinforcement learning, robotics, and data analysis. Surprisingly, despite its wide applicability, there have been limited investigations or reviews on applying few-shot learning to the field of mechanical fault diagnosis. In this paper, we provide a comprehensive review of the relevant work on few-shot learning in mechanical fault diagnosis from 2018 to September 2023. By examining the existing research, we aimed to shed light on the potential of few-shot learning in this domain and offer valuable insights for future research directions.

Analysis of transient electromagnetic force on end windings of 300 Mvar synchronous condenser during dynamic reactive power compensation process

The uneven distribution of renewable energy supply and demand promotes the development of high-voltage direct current transmission. As the critical reactive power compensation equipment for high voltage direct current transmission, the synchronous condenser must have a strong overload capability. One of the critical factors limiting the improvement of its overload capacity is the reliability of the end winding, which depends on the magnitude of the force on end windings during dynamic large reactive power output. Therefore, this paper proposes a coupling model of transient electromagnetic force for the high voltage direct current transmission system and the end zone of the synchronous condenser. The transient response of the synchronous condenser under different fault degrees is calculated, analyzed, and verified by experiments. Based on the current response of the synchronous condenser, the transient electromagnetic force on end windings is calculated. The distribution law of the force on end windings of the synchronous condenser under different degrees of dynamic reactive power compensation process is revealed. Further, the regions on end windings with larger force during strong overload are indicated. The optimizing fixation scheme for end windings is proposed according to the distribution features of the force, which can improve the operational reliability of end windings.

Novel semi-ring type alternating current field measurement probe for inspection of coiled tubing

Coiled tubing is a critical equipment in the oil and gas exploration and development field. Due to the high pressure, corrosive medium and repeated buckling, cracks are easily introduced in the surface of the coiled tubing. It is necessary to detect cracks in-service for the maintenance of the coiled tubing. In this paper, a new semi-ring type alternating current field measurement (ACFM) probe is presented for imaging and evaluating the surface cracks in the coiled tubing. The finite element method (FEM) model is developed to analyze the characteristic signals of the crack. According to the simulate results, the tunnel magneto resistance (TMR) sensor arrays in semi-ring ACFM probe is placed between the coiled tubing wall and the coil. The experimental results show that the novel ACFM probe can achieve imaging and detection of surface axial and circumferential cracks in the coiled tubing. The through-wall crack can be judged effectively compared to the shallow crack. The length of axial cracks can be accurately evaluated by the peak-valley spacing of the Bz signal. The length of circumferential crack can be evaluated approximately by the location of sensor arrays in the circumferential direction.

Effective fault module localization in substation critical equipment: an improved ant colony optimization and back propagation neural network approach

AbstractThe rapid development of substations has increased the demand for accurate and fast fault prediction systems. In order to achieve rapid localization and autonomous decision‐making of fault modules and types in substations, the article proposes a fault autonomous localization algorithm based on improved ant colony optimization (IACO) and back propagation neural network (BPNN). The fault data of the substation secondary equipment for training and testing the BPNN model is based on the actual operating equipment of the substation, which can significantly improve the reliability of the model results. In addition, the IACO is used to globally optimize the weights and thresholds of BPNN, and the number of hidden layer nodes in BPNN was analyzed to further improve the accuracy of the established fault prediction algorithm. The test results show that the fault prediction accuracy of the BPNN model optimized by IACO is 93.67%, which is significantly improved compared to the traditional BPNN and BPNN with ant colony optimization method (with an accuracy of 82.98% and 91.04%). The above results effectively demonstrate the high accuracy and effectiveness of the established prediction algorithm in processing data and locating faults, which can improve the maintenance and operational efficiency of substations.