Equipment Failure Research Articles

Study on the superhydrophobic coatings with antifouling and anticorrosion properties

The corrosion caused by marine organisms adhering to the wall surface is one of the main reasons for the failure of ships and equipment, and leads to shortened life and increased maintenance costs, and this has seriously affected the development of the national economy. Therefore, it is very important to develop a new composite material to protect the metal substrate from biological corrosion. In this article, a superhydrophobic surface was prepared by simply spraying silicone rubber emulsion containing hydrophobic SiO2 nanoparticles onto a graphene silicone rubber (GSR) pre-coated substrate (SiO2-GSR). The antifouling ability was investigated under laboratory conditions and real marine environments, and the results show that the SiO2-GSR coating has good antiadhesion effect and longer antifouling ability. The anticorrosion performance was evaluated by electrochemistry methods, and the SiO2-GSR has good corrosion resistance ability. Additionally, the SiO2-GSR film still exhibited a strong antifouling and anticorrosion ability after immersed in marine environment for 6 months. Therefore, SiO2-GSR can be applied to the maritime field as an effective antifouling and anticorrosion coating.

Flexible resource optimal allocation strategy for distribution system resilience improvement under failure uncertainty

Extreme disasters have led to huge economical losses to the power system and posed a huge threat to the stable supply of electric energy. Especially for traditional passive distribution network systems, failures of transmission lines, towers, and other equipment will cause large-scale power outages and huge economic losses. In order to improve the ability of the power system to deal with extreme weather disasters, this paper proposes an optimal configuration strategy for the distribution network backup power resource oriented to resilience improvement, considering the uncertainty of equipment failure caused by disasters in the case of a known disaster range. The location and capacity of backup resources can be properly configured to reduce the load shedding of the power distribution network during the disaster process, thereby improving the resilience of the distribution network. The method proposed in this paper is modeled as a two-stage stochastic programming, which includes mix-integer constraints. In the first stage, the optimization problem confirms the capacity and location of the flexible resources. In the second stage, the operational constraints and costs of load shedding are considered. The resilience enhancement method is verified on the improved IEEE-33 node system, and the result shows that the method proposed in this paper can effectively enhance the resilience of the urban power network.

Experimental Investigation of a Modified Enzyme-Induced Carbonate Precipitation Solution for Sand Production Control Applications

Summary Sand production is one of the major problems that can occur in an oil or gas well. Enzyme-induced carbonate precipitation (EICP) methods have recently emerged as possible environment-friendly solutions for enhancing loose sand consolidation and preventing it from being produced with the fluids to the surface. This work explores increasing the consolidated sand strength and its treatment procedure using a modified EICP. The study also examines the characterization of precipitation generated by microorganisms using a computed tomography (CT) scan. To consolidate the sand specimen, nine different solutions were prepared. The solutions were a mixture of urea, urease, CaCl2, MgCl2, and xanthan gum in varying quantities. X-ray diffraction (XRD) analysis was conducted to determine the type of calcium carbonate (or CaCO3) polymorph. The morphology of calcium carbonate precipitation in the sand sample was visualized through scanning electron microscopy (SEM) imaging. The strength of consolidated samples was determined by the scratch test. The baseline EICP solution was exposed to different curing temperatures, namely, 25°C, 70°C, and 90°C. Out of these temperatures, the sample cured at 70°C showed the maximum strength, while the ones cured at 25°C demonstrated the weakest strength. This outcome emphasizes how crucial temperature control is in determining the strength development of the samples. The results highlight the importance of evaluating how varying curing temperatures affect specimen performance as well as emphasizing the need for accurate temperature control during experimental setups. Interestingly, samples made with a combination of CaCl2 and MgCl2 salts exhibited more strength when compared with EICP solutions formulated with only one type of salt. The consolidated sample that was prepared with xanthan gum with a concentration of 3 g/L showed high strength at 70°C. Notably, this technique offers a cost-effective solution compared with other methods developed to address sand production-related failures in production equipment. Furthermore, CT scans prove to be a valuable tool for investigating the characterization of microbially induced precipitation, including calcite, dolomite, and other minerals. This research underscores the professional approach in evaluating the efficacy of xanthan gum and CT scans in the context of EICP applications.

Reliable Temperature Measurement in Radiation-Intensive Environments

Radiation-resistant temperature sensors are vital for ensuring reliability in radiation-intensive environments, where the highly energetic and penetrating nature of radiation can significantly impact electronic devices and sensors. In such environments, like those near intense radiation sources or in challenging radiation-rich settings, such as space, gamma radiation can lead to erroneous measurements or equipment failures. Radiation-resistant sensors play a crucial role in maintaining measurement accuracy as they are designed to minimize interference caused by radiation, protecting electronic components and providing precise and reliable temperature readings. Their resilience to radiation-induced effects ensures data durability, reducing the need for frequent replacements, and enhancing the overall reliability of measurements in these demanding conditions. In this paper, we present and analyze two different configurations, aiming to address the challenges posed by radiation in sensitive environments. By exploring these novel approaches, we seek to enhance the robustness and accuracy of temperature sensors in radiation-intensive settings, enabling reliable data collection and facilitating successful operations in challenging radiation-rich conditions. The comparative analysis of these configurations will shed light on their performance and effectiveness in mitigating radiation-induced effects, thereby contributing to the advancement of radiation-resistant temperature sensing technologies.

Artificial intelligence (AI) in renewable energy: A review of predictive maintenance and energy optimization

The integration of Artificial Intelligence (AI) in the renewable energy sector has emerged as a transformative force, enhancing the efficiency and sustainability of energy systems. This paper provides a comprehensive review of the application of AI in two critical aspects of renewable energy in relation to predictive maintenance and energy optimization. Predictive maintenance, enabled by AI, has revolutionized the renewable energy landscape by predicting and preventing equipment failures before they occur. Utilizing machine learning algorithms, AI analyzes vast amounts of data from sensors and historical performance to identify patterns indicative of potential faults. This proactive approach not only minimizes downtime but also extends the lifespan of renewable energy infrastructure, resulting in substantial cost savings and improved reliability. Furthermore, AI plays a pivotal role in optimizing the energy output of renewable sources. Through advanced data analytics and real-time monitoring, AI algorithms can adapt to changing environmental conditions, predicting energy production patterns and optimizing resource allocation. This ensures maximum energy yield from renewable sources, making them more competitive with traditional energy sources. The paper delves into specific AI techniques such as deep learning, neural networks, and predictive analytics employed for predictive maintenance and energy optimization in various renewable energy systems like solar, wind, and hydropower. Challenges and opportunities associated with implementing AI in renewable energy are discussed, including data security, interoperability, and the need for standardized frameworks. The synthesis of AI technologies with renewable energy not only addresses operational challenges but also contributes to the global transition towards sustainable and clean energy solutions. This review serves as a valuable resource for researchers, practitioners, and policymakers seeking insights into the evolving landscape of AI applications in the renewable energy sector. As technology continues to advance, the synergies between AI and renewable energy are poised to shape the future of the global energy paradigm.

Development of a methodology for asset integrity management in Universities and Research Centers

This article describes the application of a differentiated methodology that I developed to enhance and facilitate asset management work in renowned universities and research centers. Frequently, important scientific studies and research are interrupted or wasted due to unexpected failures in equipment and infrastructure systems provided by universities and research centers for researchers’ use. To address this issue, this methodology incorporates structured concepts and practices commonly employed in the field of asset integrity. These are uniquely integrated into comprehensive engineering assessment reports developed over several years during my fieldwork in various countries. This integration occurs within a computerized maintenance management system. The ultimate goal of this process is to maximize the reliability and performance of critical systems and equipment used in the infrastructure and conduct of highly relevant research at the national and international levels. Through case studies conducted at two major universities in the United States, the efficiency of this innovative methodology was analyzed quantitatively and qualitatively. As presented in this article, the main results obtained include a reduction in the number of unplanned repairs or shutdowns, improved quality and efficiency of maintenance programs, increased reliability and lifespan of critical research equipment and systems, an increase in the number of assets with an appropriate maintenance plan, and the enhancement of computerized maintenance management systems used by these institutions. These results translate into faster completion of scientific studies and research with more reliable data.

Construction of Mining Robot Equipment Fault Prediction Model Based on Deep Learning

In the field of mining robot maintenance, in order to enhance the research on predictive modeling, we introduce the LODS model (long short-term memory network (LSTM) optimized deep fusion neural network (DFNN) with spatiotemporal attention network (STAN)). Traditional models have shortcomings in handling the long-term dependencies of time series data and mining the complexity of spatiotemporal information in the field of mine maintenance. The LODS model integrates the advantages of LSTM, DFNN and STAN, providing a comprehensive method for effective feature extraction and prediction. Through experimental evaluation on multiple data sets, the experimental results show that the LODS model achieves more accurate predictions, compared with traditional models and optimization strategies, and achieves significant reductions in MAE, MAPE, RMSE and MSE of 15.76, 5.59, 2.02 and 11.96, respectively, as well as significant reductions in the number of parameters and computational complexity. It also achieves higher efficiency in terms of the inference time and training time. The LODS model performs well in all the evaluation indexes and has significant advantages; thus, it can provide reliable support for the equipment failure prediction of the mine maintenance robot.

A Critical Review of Anti-Corrosion Chemical Surface Treatment of Aluminum Alloys Used for Sports Equipment

Aluminum alloys with low-weight property are promising structure materials for sports equipment. Alloying element-rich second-phase particles create the risk of localized corrosion and result in failure of sports equipment. Chromate conversion coatings as conventional and successful surface treatments were employed to provide a thin but compact film against corrosion. However, chromate species were toxic and carcinogenic for human beings and this process has been highly restricted. In this sense, alternative processes such as trivalent chromium conversion coating with low environmental risk require better corrosion-resistant performance compared to chromate conversion coating. In addition, the closed-loop system of the chromate electroplating process has been used in Europe and the United States. This is also a sustainable process for surface treatment of aluminum alloys applied in sports equipment. The present paper aims to summarize the methods and types of different aluminum alloy surface treatments and compiles the effects of various surface treatments on the corrosion resistance of aluminum alloys. The eco-friendly application of aluminum alloys in the field of sports equipment may be facilitated in the future.

Transition paths for condition‐based maintenance‐driven smart services

AbstractThis research investigates growth inhibitors for smart services driven by condition‐based maintenance (CBM). Despite the fast rise of Industry 4.0 technologies, such as smart sensoring, internet of things, and machine learning (ML), smart services have failed to keep pace. Combined, these technologies enable CBM to achieve the lean goal of high reliability and low waste for industrial equipment. Equipment located at customers throughout the world can be monitored and maintained by manufacturers and service providers, but so far industry uptake has been slow. The contributions of this study are twofold. First, it uncovers industry settings that impede the use of equipment failure data needed to train ML algorithms to predict failures and use these predictions to trigger maintenance. These empirical settings, drawn from four global machine equipment manufacturers, include either under‐ or over‐maintenance (i.e., either too much or too little periodic maintenance). Second, formal analysis of a system dynamics model based on these empirical settings reveals a sweet spot of industry settings in which such inhibitors are absent. Companies that fall outside this sweet spot need to follow specific transition paths to reach it. This research discusses these paths, from both a research and practice perspective.

New approaches to determine the technological challenged of oil fields at the late stage of development

Background: The main problem of the process of field development in the later stages is the limitation of free funds associated with the decline in production and the corresponding decrease in the profitable part of enterprises. At the same time, the accumulated effect of critical production problems is manifested, for the solution of which in practice a significant amount of capital investments is required in different areas of field development.
 The late stage of development of fields is associated with a number of problems, the main of which are the deterioration of the structure of the reserves and the slow pace of their replenishment, low withdrawal rates and high water cut, frequent failure of ground equipment, technological limitations of the production infrastructure, the quality of construction and completion of wells, a short turnaround time of production wells affecting the wells’ operation coefficient, and insufficient coverage of production processes with digitalization.
 Aim: Considering the large amount of work involved in the oil production process (from geology to onshore infrastructure), an internal benchmarking analytical tool is needed to identify current and most common technological challenges, to focus scientific, technical and production personnel in a certain direction and develop a systematic approach to solving production problems. The purpose of this work is to develop such a tool
 Materials and methods: As the input data, historical data on the production, hydrocarbons reserves, the current technological parameters of underground and surface equipment of 12 oil fields of the group of the NK “Kazmunaygaz” JSC companies were used. 17 basic criteria were formed in the areas of geology, development, oil production, drilling and infrastructure, which served as the basis for internal benchmarking of oil fields.
 Results: As a result of this work, a tool was developed for the diagnosis of key production problems of KMG oil fields. The diagnostic chart is effective for determining zones of the spread of production problems both in one oil field and in all oil fields of KMG. This approach can be scaled to the level of oil fields and horizons.
 Conclusion: The developed internal benchmarking tool serves as the basis for the annual analysis of key problems of the oil fields of the group of the NC “KazMunaygaz” JSC companies and the formation of a long -term plan to solve key production challenges.

Review on Soil Corrosion and Protection of Grounding Grids.

The corrosion of grounding grid materials in soil is a prominent factor in power and electrical equipment failure. This paper aims to delve into the corrosion characteristics of grounding grid materials and the corresponding methods of safeguarding against this phenomenon. Firstly, the influencing factors of the soil environment on the corrosion of the grounding grid are introduced, including soil physicochemical properties, microorganisms, and stray currents. Then, the corrosion behavior and durability of common grounding grid materials such as copper, carbon steel, and galvanized steel are discussed in detail and compared comprehensively. In addition, commonly used protective measures in China and outside China, including anti-corrosion coatings, electrochemical protection, and other technologies are introduced. Finally, it summarizes the current research progress and potential future directions of this field of study.

Rain-t: Daily Rainfall Predictive Model Using 6-Gene Genetic Expression for Historical Data-Based Forecasting

Extreme weather conditions such as heavy rainfalls have been wreaking havoc not only in urban areas but also in an entire watershed. The development of a flood management plan and flood mitigating structures to alleviate the impacts of flooding is very crucial because it needs intensive and continuous historical data. However, missing data due to equipment failure that gathers the rainfall data could be a problem. Rainfall data is not only useful in designing flood mitigating structures but also in planning our day-to-day activities ahead of time. To address this problem, this paper proposes a predictive model which able to forecast in a short lead-time and predict missing data within the dataset. In this paper, three predictive models will be compared namely recurrent neural network, Gaussian processing regression, and the proposed 6-gene genetic expression-based predictive modeling (MGGP). 29-year 24-hour cumulative rainfall data which were sourced in PAGASA Tacloban city weather station, Philippines, was used. The data were cleaned by removing negative values. Two datasets were created, the first (RFDS1) dataset which makes use of three indices (year, month, and days), and the second (RFDS2) dataset which was orchestrated and transformed to increase correlation and reduce prediction errors which had an additional two datasets (ave(t-1,t-2),t-1). Each method used three and five time-based indices. The result shows an erratic behavior of the model from three methods that used the RFDS1, while RFDS2 had a more stable predictive model. This shows that the data orchestration and transformation greatly improved the correlation and reduced errors. However, MGGP showed the best results among the three methods.

Real-time performance evaluation and optimization of electrical substation equipment inspection algorithm based on distributed computing

Abstract Substation equipment inspection faces problems such as large amounts of data, various information types, complex analysis and calculations, and low resource integration. Distributed computing system uses networks to realize computer connectivity and collaboration, achieving in-depth sharing of computing resources, software resources, information resources, and communication resources. Therefore, firstly, this article introduces segmentation method for load balancing and recommendation calculation based on path search; secondly, an algorithm model for substation equipment inspection based on distributed computing is constructed; then, the article analyzes and discusses multi-core cluster task allocation, heterogeneous task scheduling framework, fragmented transmission mechanism, and vertical expansion mechanism; finally, case analysis and performance evaluation are carried out. Distributed computing has significant advantages in the inspection algorithm of power substation equipment. It can efficiently process large-scale data, improve computing power, ensure data redundancy and fault tolerance, provide good scalability, optimize resource utilization, and support remote data processing. By integrating multi-source data, distributed computing improves the accuracy and comprehensiveness of the algorithm, enhances security, supports complex algorithms such as machine learning and deep learning, and realizes real-time monitoring and early warning. The results show that the proposed algorithm can improve the efficiency of distributed parallel computing and analysis of substation equipment inspections and can simulate, evaluate, and screen substation equipment failure scenarios that may cause serious consequences within an effective time.

Predictive Analytics-Based Methodology Supported by Wireless Monitoring for the Prognosis of Roller-Bearing Failure

Roller mills are commonly used in the production of mining derivatives, since one of their purposes is to reduce raw materials to very small sizes and to combine them. This research evaluates the mechanical condition of a mill containing four rollers, focusing on the largest cylindrical roller bearings as the main component that causes equipment failure. The objective of this work is to make a prognosis of when the overall vibrations would reach the maximum level allowed (2.5 IPS pk), thus enabling planned replacements, and achieving the maximum possible useful life in operation, without incurring unscheduled corrective maintenance and unexpected plant shutdown. Wireless sensors were used to capture vibration data and the ARIMA (Auto-Regressive Integrated Moving Average) and Holt–Winters methods were applied to forecast vibration behavior in the short term. Finally, the results demonstrate that the Holt–Winters model outperforms the ARIMA model in precision, allowing a 3-month prognosis without exceeding the established vibration limit.

Sensitivity analysis on tailpipe design for mitigating terrain slug in horizontal wells

Terrain slugs are a common challenge faced in horizontal well completions, leading to significant operational difficulties and reduced well performance. The lateral wellbore can cause hydrodynamic slug problems, even severe terrain slug issues. With undulations along the lateral, gas pockets can build up within the peaks and valleys of the wellbore. These gas pockets can restrict liquid flow until bottom hole pressure builds up enough energy to push the liquid slug to the surface. The terrain slug flow not only frequently results in foaming or emulsions at the surface but also causes downhole equipment failures, especially for Electrical Submersible Pumps (ESPs). Even with the downhole separator and gas handling pump stages, the ESP system is challenged by the elongated gas pocket that results in gas locks and electrical failures. To mitigate these issues, the use of Tailpipes (TP) in combination with ESP has become a popular solution. This study aims to explore the impact of tailpipe design on the performance of ESP systems in horizontal wells. With the help of oilfield operating companies, 5 representative wells are selected from 24 horizontal ESP wells that are facing or will potentially face the slugging issue.A total of 284 simulations are conducted using the transient simulator (OLGA, 2019) to comprehensively investigate the tailpipe design methodology for well conditions that are comparable to this study, i.e., flow rate from 500 to 2000 bpd, GLR from 500 to 2000 scf/bbl, 5 ½” and 7” casing, 9000–12000 ft well depth. Different TP diameters and lengths are simulated to evaluate the TP effectiveness at different flow rates, Gas-Liquid Ratios (GLRs), and well conditions. The results of this study can provide valuable insights and recommendations to the ESP tailpipe design for a wide range of horizontal wells applications, and help to improve their overall performance and reliability.

Fires and fire risk perception predictors on Finnish farms

ABSTRACT The physical and operational farm environment has become more complex, challenging risk management. This study aimed to identify the number and typical features of farm fires and the factors connected to farmers’ assessments of fire risk. The data comprised a subset extracted from the national accident database of the Finnish rescue services from 2000 to 2021 (3,989 farm fire alarm calls) and farm survey data collected in the spring of 2021 (506 valid responses). The descriptive analysis of the fire incident data showed that the most typical fire sites on farms were dairy cattle buildings, grain dryers, and heating plants. Most farmers (83.4%) assessed the probability of fire as intermediate or high. The Bayesian machine learning approach used to analyse the survey data suggested the control of malfunctions, failures of machinery and equipment, and educational efforts were key elements in fire risk management.

Bottomhole Pressure Prediction of Carbonate Reservoirs Using XGBoost

The bottomhole pressure is one of the key parameters for oilfield development and decision-making. However, due to factors such as cost and equipment failure, bottomhole pressure data is often lacking. In this paper, we established a GA-XGBoost model to predict the bottomhole pressure in carbonate reservoirs. Firstly, a total of 413 datasets, including daily oil production, daily water production, daily gas production, daily liquid production, daily gas injection rate, gas–oil ratio, and bottomhole pressure, were collected from 14 wells through numerical simulation. The production data were then subjected to standardized preprocessing and dimensionality reduction using a principal component analysis. The data were then split into training, testing, and validation sets with a ratio of 7:2:1. A prediction model for the bottomhole pressure in carbonate reservoirs based on XGBoost was developed. The model parameters were optimized using a genetic algorithm, and the average adjusted R-squared score from the cross-validation was used as the optimization metric. The model achieved an adjusted R-squared score of 0.99 and a root-mean-square error of 0.0015 on the training set, an adjusted R-squared score of 0.84 and a root-mean-square error of 0.0564 on the testing set, and an adjusted R-squared score of 0.69 and a root-mean-square error of 0.0721 on the validation set. The results demonstrated that in the case of fewer data variables, the GA-XGBoost model had a high accuracy and good generalization performance, and its performance was superior to other models. Through this method, it is possible to quickly predict the bottomhole pressure data of carbonate rocks while saving measurement costs.

An analytical hierarchy approach for studying the impact of human error, environmental factors, and equipment failure on mine accidents: a case study in India

An analytical hierarchy approach for studying the impact of human error, environmental factors, and equipment failure on mine accidents: a case study in India

RETRACTED ARTICLE: IoT devices and data availability optimization by ANN and KNN

Extensive research has been conducted to enhance the availability of IoT devices and data by focusing on the rapid prediction of instantaneous fault rates and temperatures. Temperature plays a crucial role in device availability as it significantly impacts equipment performance and lifespan. It serves as a vital indicator for predicting equipment failure and enables the improvement of availability and efficiency through effective temperature management. In the proposed optimization scheme for IoT device and data availability, the artificial neural network (ANN) algorithm and the K-Nearest Neighbours (KNN) algorithm are utilized to drive a neural network. The preliminary algorithm for availability optimization is chosen, and the target is divided into two parts: data optimization and equipment optimization. Suitable models are constructed for each part, and the KNN-driven neural network algorithm is employed to solve the proposed optimization model. The effectiveness of the proposed scheme is clearly demonstrated by the verification results. When compared to the benchmark method, the availability forward fault-tolerant method, and the heuristic optimization algorithm, the maximum temperature was successfully reduced to 2.0750 °C. Moreover, significant enhancements in the average availability of IoT devices were achieved, with improvements of 27.03%, 15.76%, and 10.85% respectively compared to the aforementioned methods. The instantaneous failure rates were 100%, 87.89%, and 84.4% respectively for the three algorithms. This optimization algorithm proves highly efficient in eliminating fault signals and optimizing the prediction of time-limited satisfaction. Furthermore, it exhibits strategic foresight in the decision-making process.

Implementing Predictive Maintenance (PdM) Programs in Food and Beverage Manufacturing Facilities

Predictive maintenance (PdM) has emerged as a critical approach to maintaining equipment in food and beverage manufacturing facilities. By proactively monitoring and analyzing equipment data, PdM can help to identify and prevent equipment failures before they occur. This can lead to significant reductions in unplanned downtime, improved efficiency, and lower maintenance costs. This paper discusses the key steps involved in implementing a PdM program in a food and beverage manufacturing facility. These steps include: Establishing clear goals and objectives for the PdM program. Identifying critical equipment and collecting data from these assets. Analyzing the collected data to identify patterns and trends that could indicate potential equipment failures. Developing predictive models that can be used to forecast equipment failures. Implementing a process for responding to PdM alerts and taking corrective action. The paper also discusses some of the challenges of implementing a PdM program in a food and beverage manufacturing facility, such as the need for a strong data culture and the need to integrate PdM with other maintenance activities.