Cause Of Equipment Failure Research Articles

An approach to assessing the technical condition of electrical equipment using weighted fuzzy rules

To ensure the smooth operation of all existing electric power systems, it is necessary to periodically diagnose electrical equipment using various methods and models that take into account all possible parameters and factors affecting the condition. The paper considers an approach to assessing the technical condition of electrical equipment using weighted fuzzy rules, taking into account different types of information (measuring, expert). The novelty of the approach is the representation of parameters in the form of fuzzy triangular numbers and the formation of importance weight vectors of term sets parameter values. This makes it possible to accurately assess the technical condition of electrical equipment in the context of different types of information to predict the condition of the equipment; to quickly identify parameters which values are outside acceptable limits, thereby determining the preliminary cause of equipment failure, as well as making informed diagnostic decisions regarding the condition of electrical equipment.

Prediction of external corrosion rate in Oil and Gas platforms using ensemble learning

Goal: This study aims to use artificial intelligence, specifically a random forest model, to predict the annual corrosion rate on FPSO offshore platforms in the oil and gas industry. Corrosion is a significant cause of equipment failure, leading to costly replacements. The random forest model, a machine learning technique, was developed using climatic and other relevant data to forecast corrosion trends based on selected variables. Design/methodology/approach: The methodology involved four steps: identifying influential factors affecting corrosion, selecting factors based on reliability and accessibility of measurements, applying the machine learning model to predict annual corrosion progression, and comparing the random forest model with other ML models. Results - The results showed that the random forest regression model successfully predicted corrosion rates, indicating an average yearly increase of 2.43% on the analyzed platforms. The main factors influencing this increase were wind speed, percentage of measured corrosion, and platform operating time. Regions with higher incidence of these factors are likely to experience higher corrosion rates, necessitating more frequent maintenance. Limitations of the investigation - The research sample consisted exclusively of 4 platforms located in the offshore region of Rio de Janeiro, Brazil. Thus, the results obtained must be interpreted as representative of these platforms and respective climate conditions. Practical implications – The use of data science tools to improve corrosion management allows managers to have knowledge of which areas has a greater or lesser tendency to corrode, helping to prioritize maintenance activities over time. Originality/value - This study aims to fill gaps regarding the use of random forest techniques for regression focused on predicting the rate of increase in corrosion. It offers a novel approach to assist decision-making in maintenance planning, providing insights into influential corrosion factors and facilitating more effective painting plans to preserve industrial unit integrity.

Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research

Power generation from wind farms is growing rapidly around the world. In the past decade, wind energy has played an important role in contributing to sustainable development. However, wind turbines are extremely susceptible to component damage under complex environments and over long-term operational cycles, which directly affects their maintenance, reliability, and operating costs. It is crucial to realize efficient early warning of wind turbine failure to avoid equipment breakdown, to prolong the service life of wind turbines, and to maximize the revenue and efficiency of wind power projects. For this purpose, wind turbines are used as the research object. Firstly, this paper outlines the main components and failure mechanisms of wind turbines and analyzes the causes of equipment failure. Secondly, a brief analysis of the cost of wind power projects based on equipment failure is presented. Thirdly, the current key technologies for intelligent operation and maintenance (O&M) in the wind power industry are discussed, and the key research on decision support systems, fault diagnosis models, and life-cycle costs is presented. Finally, current challenges and future development directions are summarized.

Supporting the life cycle of household appliances during the operation stage

The last decade of the twentieth century was characterized by a rapid digitalization of all areas of activity from traditional research to the automation of production, trade, commerce, banking, and other activities. The CALS technology has become largely popular in the world, including Russia. Continuous Acquisition and Lifecycle Support (CALS) stands for the continuous information support of supplies and the life cycle of products, or the information support for product life cycle (IPS) The product life cycle comprises a set of processes performed from the identification of needs for specific products to the satisfaction of such needs and the scrapping of the products. The analysis of the household appliance service companies helped identify the key causes of equipment failure, one of which is failure to meet the operating requirements. We also established that servicing household appliances takes too much of the consumers’ time. They have to understand what consumables they need, when and where to buy them, and how to carry out the maintenance (themselves or through an agent). This article presents an instrument for the operation of products and services required by average consumers in their everyday lives. The tool is made up of a database that stores information about the useful life of different products by specific users as well as information about the maintenance, adjustment, and other actions required for the rational operation of the said products. The database will be used by specific vendors who will keep a database of their clients and products. Alternatively, a retail chain may keep a register of its clients and products sold to them and offer household appliance registration and maintenance services. The third option is creating a cloud service to register consumers, vendors, and service agencies.

A real-time adaptive model for bearing fault classification and remaining useful life estimation using deep neural network

Rolling element bearings are essential components of a wide variety of industrial machinery and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) and fault diagnosis is an essential component of equipment diagnosis and health management. Real-time Conditional Based Monitoring (CBM) faces a significant challenge in the Internet of Things and Industrial 4.0 eras due to automatically processing enormous quantities of data. A novel three-phase architecture based on deep learning has been proposed to perform real-time monitoring. The model is end-to-end adaptive, and the data is sequentially processed. The proposed hybrid approach incorporates change-point detection, RUL prediction, and fault classification. Adaptive data pre-processing is employed in conjunction with the unsupervised CPD module, followed by a model training on bearing data deterioration to increase efficiency. The autoencoder employed for CPD is also utilized to supply additional features from the bottleneck layer to acquire an enhanced Health Index (HI). The experiments were conducted on NASA’s prognosis and diagnosis datasets, and the proposed method’s effectiveness was compared to other benchmark approaches. The results show that the proposed methodology can execute real-time CBM efficiently and reliably, outperforming other methods.

JIP Focuses on Equipment Reliability in Subsea Production Systems

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 30219, “SEAR JIP: A Success Story of Collaboration and How To Improve Equipment Reliability on Subsea Production Systems,” by Adriana Botto, Wood; Manish Tomar, Chevron; and Stuart Ferrier, ConocoPhillips, et al. The paper has not been peer reviewed. Copyright 2020 Offshore Technology Conference. Reproduced by permission. The Subsea Equipment Australia Reliability (SEAR) joint industry project (JIP) is a partnership led by Wood with participation from a group of operators including Chevron Australia, ConocoPhillips, Inpex, Santos, Shell Australia, and Woodside. Now delivering Phase 6, the JIP is focused on collaboration and knowledge-sharing to improve the competitiveness of Australia’s oil and gas industry by addressing critical challenges associated with premature subsea-equipment failure. The complete paper provides an overview of the JIP and outlines lessons learned and value created. Background The SEAR JIP was initiated in 2014 with an aim of generating significant cost savings by improving subsea-equipment reliability and design for Australian operations, a goal best achieved through collaboration and knowledge-sharing. A reliability database was developed to collect offshore-operations failure information from SEAR members. The database provides a low-cost/high-value method of capturing lessons learned for subsea-equipment performance in Australia. A complementary test program, Transforming Australian Subsea Equipment Reliability (TASER), is under way to model innovative technologies under variable environments to validate equipment advancements. Phase 6 of the JIP aims to continue populating the reliability database while addressing two major industry challenges: the effect of marine fouling affecting equipment operability, and unwanted gas in subsea controls umbilicals. The outcome of this phase should provide opportunities for standardization, life extension, and development of new technologies. The JIP plans to issue an industry recommended practice in 2022 harnessing the lessons learned. This recommended practice will identify root causes of equipment failure and recommend material selection and mechanical design requirements for different environmental conditions. This will enable an improvement in equipment availability and a reduction in operating expenditures by minimizing offshore intervention to address failed equipment.

Research on Fault Diagnosis Method of Electric Energy Metering Equipment Based on Expert System

The realization of the full coverage of the smart meter and the metering automation terminal across the entire network brings great challenges to the operation and maintenance of the field. Aiming at the long-term problems of on-site failure operation and maintenance of the electric energy metering equipment, this paper combines the metering and collecting of failure types to analyse the main causes of equipment failure, based on the field topology structure for fault reasoning, and designs fault diagnosis and fault knowledge base as the core module for fault diagnosis. The expert system has been proved by practice that this method can effectively diagnose and solve the failure problem of the energy metering equipment, which is beneficial to realize the intelligentization of on-site fault diagnosis and improve the on-site maintenance efficiency of the energy metering equipment, and has practical reference value.

APPLICATION OF AUTOMATIC PHASE SHUNTING FOR SUPPRESSION OF THE SECONDARY ARC IN PAUSE PAIRS OF EXTRA HIGH VOLTAGE TRANSMISSION LINES

One of the main causes of equipment failure in the main electrical network is a overvoltage, that is, increase the value of the operating voltage above the maximum value according to the technical requirements. This is because the relatively small insulation reserve provides for the components of the electricity grid because of its high cost for a given voltage. Generally, cost of power systems insulation is a significant factor in capital expenditures. Internal overvoltage appears through resonance, due to coincidence of the parameters of the circle. In order to increase the reliability and efficiency of extra-high voltage power lines, a single-phase automatic reclose is applied when a single-phase short circuit occurs. During the operation cycle of a single-phase automatic reclose, a secondary arc arises, which receives supplying from the capacitive connections of the unconnected phases of the line. In turn, the success of a single-phase reclosing cycle depends on the characteristics of the secondary arc. The aim of the article is the development and application of an effective measure of suppressing a secondary arc in the form of automatic shunting of the disconnected phase. To achieve this goal, an approach has been developed to determine the characteristics of the repeated arc of the current of the disconnected phase, based on the matrix method of analysis of power line elements. To assess the degree of influence of automatic phase shunting on the aperiodic component of the charge arc current, the equivalent circuit of the disconnected damaged phase was compared. Based on the developed approaches, the effectiveness of the use of automatic phase shunting to increase the efficiency of operation of extra-high voltage power lines in o-phase operation modes was proved.

Study of erosion prediction of turbulent gas-solid flow in plugged tees via CFD-DEM

In many industrial applications, sand erosion wear is the main cause of equipment failure, particularly in the transferring pipeline fittings. In order to reduce the adverse consequences of erosion in bends, in this study the potential usage of plugged tees instead of standard elbows under a wide range of flow conditions is examined. A computational fluid dynamics (CFD) model coupled with discrete element method (DEM) was used and the particle-laden flows with different fluid velocities in the corresponding geometries carrying various entrained particle mass loadings were simulated. The reliability of selected turbulence models as well as rebound and erosion models were verified by comparing the model predictions with the available experimental data. The final simulation model included the E/CRC erosion model, and the Grant and Tabakoff rebound model. The DEM used in the computational model includes the effects of particles rotation and collisions between the particles. The study also considered the effect of modifications of the plugged length of the tee geometries. The simulation results indicated that, as the particle mass loading increases, the effectiveness of plugged tee compared to the standard elbow increases. Moreover, although increasing the plugged length will generally reduce the erosion rate due to the cushioning effect, the rate of reduction depends on mass loading.

The resonance overvoltages in nonsinusoidal modes of extra high voltage transmission lines

Due to the rising role of overhead lines extra high voltage there is a need of application of modern electronic devices that allow controlling modes of bulk electricity system. Durable resonance overvoltage may cause damage of electrical power equipment. This kind of overvoltage is caused by properties of the network and can be eliminated by changing the ratio between the parameters of the network and its mode. Unlike switching overvoltage that exist centiseconds, resonance overvoltage not only occur unexpectedly, but they may go on for a long time until the automatic action or action of personnel will not change electrical schemes or operation modes. The resonance overvoltage are neglected when selecting insulation or parameters for high-voltage surge arrester as this protective measure is calculated to limit switching overvoltage, not to extinguish a long resonance process. Therefore, the probability and development of system accidents due to resonance overvoltage is significant. One of the main causes of equipment failure in the main electrical network is overvoltage. Overvoltage are the values which according to the technical requirements exceed the maximum value of normal operating voltage. The reason is that the relatively small insulation reserve provides for the equipment of the electricity grid because of its high cost for a given voltage. Generally, cost of power systems insulation is a significant factor in capital investment. The main source of distortion in extra high voltage networks is nonlinearity of volt-ampere characteristic of magnetic shunt in unloaded power autotransformer. This mode causes the conditions for the significant overvoltage on even harmonics. It should be noted that the processes of the second harmonic overvoltage are generally known, but their specific occurrence conditions depend on many factors of abnormal mode. That is why the aim of the article is developing measure for reduction and prevention of overvoltage in accordance with conditions for their existence. In investigations residual magnetization is neglected because capacitance between autotransformer and circuit breaker is large enough. In the article the features of the operation of SF6 switches that should be considered when introducing them to the power lines of 750 kV. The switching control unit Switchsync F236 is one of such devices which enables to connect and disconnect contacts of poles circuit breakers at required time. This function of Switchsync F236 can be used not only for interruption of short circuit. One of the objectives of application Switchsync F236 is prevention of resonance overvoltage occurrence at closing extra high voltage line on no-load autotransformer. The analysis of electromagnetic switching transients when operating a three-phase auto recloser with really existing power transmission lines has been implemented. The influence of initial conditions of electromagnetic transients based on simulation has been analyzed. Based on the research it is proposed to use suppression equipment components with aperiodic current line switches. The research of recommended exposure settings of the controlled switching device on duration of current aperiodical component has been done. Recommendations on the use pre-insertion resistors and switching units controlled have been proposed. The importance of application and prevention of resonance overvoltage caused research area which is presented in article. Ref. 11, fig. 6.

Sustainable competitive advantage by implementing lean manufacturing “A Case study for Indian SME”

This paper work presents a model of implementation of lean manufacturing for improving productivity and quality, by conducting a case study in a small scale industry at Ranchi, Jharkhand. The purposes of this paper are to identify waste related problems, cause of equipment failure, bottleneck problems and rectify them. The above problems are analyzed in concern with rejection control, inventory control, waiting time, set time and eliminating non value added time / activities. The complete problem is identified and depicted by value stream mapping. This paper conclude with discussing improvements and it proposes Value stream mapping lean tools can be applied in small scale industries and calculation of takt time for reduction of wastage and increasing productivity.

Maintenance Practice, Causes of Failure and Risk Assessment of Diagnostic Medical Equipment

Diagnostic medical equipment are medical equipment that aid in the observation of various aspects of a patient’s health condition. These devices help to form the basis for diagnosis and is therefore critical for this equipment to be efficiently maintained to ensure accurate and reliable output. In this study, we investigated the maintenance culture of diagnostic medical equipment and relate the findings to indices to find the exact causes of equipment failures. In the case study the sphygmomanometer was used. Our results have shown the following: (i) that most failures are due to preventable causes (ii) inadequate medical equipment technicians and engineers, (iii) Data deficiencies and (iv) lack of predictive maintenance.

An experimental study of coal-fines migration in Coalbed-methane production wells

Coalbed Methane (CBM) has become an important gas resource in the last several decades. The brittle property of coal matrix and overflushing operation make the migration of coal fines inevitable. As the liquid flows below a critical velocity, the coal-fines accumulate in the bottomhole. This leads to a significant production decline causes the problems of pump failure/underperformance, severe liquid/solid loading etc. It is important to study the apparent transport mechanisms of coal fines in the wellbore, which is crucial in terms of optimization design, avoiding coal fines setting and burying pump in some extreme cases. By innovative experiment under flowing conditions, this paper presents a piece-wise regressed correlation to calculate the critical velocity to lift the coal fines at different sizes, and consequently identify the onset of coal fine loadings in wells. Operators can then maintain the de-watering rate above the calculated critical velocity, which indeed lowers the maintenance frequency and related operational costs.The origin of mobile coal fines and/or particles falls into categories: 1) large coal fines created during drilling and completion, 2) and small coal fine generated in production and migrate through cleats, cracks, and fractures into bottomhole. The former is large in particle size but has a small volume in total. The latter is small in particle size while has a large cumulative volume. Because large particles required a high liquid velocity to be removed to the surface and are major cause of equipment failure. Small coal fines, although are the majority of migrating coal during back-flow after completion and during production, are easier to be lifted to surface and require a low lifting velocity. The velocity that can remove large coal fines will not have difficulty to lift small coal fines and will prevent them accumulating at bottomehole. Therefore, we focus on the removal of large solids because it is more important. The purposes of this study are to estimate statics settling velocity and critical velocity, which are critical to the selection of appropriate operation parameters.

Operation of Stand Technical Equipment for Primary Oil Processing under Control of Automatic Monitoring System of Condition and Diagnostics Compacs®

The operation experience and results of the stationary system of condition monitoring and automatic diagnostics COMPACS® of SPC “Dynamics” production is presented to prevent unexpected equipment failures and accidents in CDU-VDU. The system description in real conditions and the equipment analysis is presented. The causes of equipment failure are described in detail and recommendations for improving the technical and economic parameters of equipment operation and optimization of the repair cycle are offered.

A knowledge-based reasoning model using causal table for identifying corrosion failure mechanisms in refining and petrochemical plants

Corrosion is one of the main causes of equipment failure in refining and petrochemical plants. For the safety and normal operation of equipment, it is necessary to identify the corrosion mechanisms resulting in equipment failure and take appropriate mitigation measures. However, there are many corrosion mechanisms in refining and petrochemical plants and identifying them becomes a key issue. In this paper, a knowledge-based reasoning model, which uses a causal table rather than traditional IF–THEN rules to organize and represent domain knowledge, is proposed for the identification of corrosion failure mechanisms. Uncertainty in the reasoning process is addressed by using a hybrid method involving a combination of the Bayesian method and certainty factor theory. An application example for the identification of environmentally assisted cracking mechanisms in petroleum refineries is presented. The results demonstrate that the model is effective and feasible.

Equipment Failure: Causes and Consequences in Endoscopic Gynecologic Surgery

Study Objective To determine the incidence of equipment failure in gynecologic endoscopy and investigate causes and consequences. Design A prospective observational single-center study between January and April 2006. Setting Gynecologic surgery department of a university hospital. Interventions In all, 116 endoscopic interventions were included: 62 laparoscopies, 51 operative hysteroscopies, and 3 fertiloscopies. Emergency and equipment testing procedures were excluded. Measurements and Main Results Equipment malfunctions were divided into 4 categories with regard to imaging, transmission of fluids and light, the electric circuit, and surgical instruments. We also found cases with faulty connections between elements. Factors including human error, loss of time, and actual or potential consequences were analyzed. At least 1 equipment failure was noted in 38.8% of operative procedures, 41.9% of laparoscopies, and 37.3% of hysteroscopies. Fluid, gas, and light transmission was faulty in 36.2%, surgical instruments in 29.3%, the electric circuit in 22.4%, and imaging in 12.1%. Of malfunctions, 46.6% were a result of faulty connection between 2 elements. The most common cause for concern was bipolar forceps and cables in laparoscopy (42.3%) and the assembly of small parts in hysteroscopy (47.4%). Personnel were implicated in 43% of cases (nurses in 72%, surgeons in 12%, both in 16%). One equipment failure increased the total duration of laparoscopy by 7% and of hysteroscopy by 20%. The mean delay was 5.6 ± 4.0 minutes by equipment failure. Of the incidences, 19% could have led to serious complications for the patient; however, no morbidity or mortality actually occurred in this series. Conclusion Equipment malfunction is common in endoscopic surgery and concerns both laparoscopy and hysteroscopy. Consequences are potentially serious. It is mandatory to identify and rectify causes of equipment failure so as to optimize the daily use of endoscopic instruments and improve patient safety. The implementation of systematic checklists is currently under evaluation.

Self-reported checklist logs overestimate compliance with defibrillator inspections

Improperly maintained cardiac defibrillators are an important cause of equipment failure during in-hospital cardiac arrest. The American Heart Association and the European Resuscitation Council recommend that defibrillators be inspected each clinical shift to identify potential equipment failures. Most commonly, the results of this operator level inspection are logged manually onto a crash cart inspection checklist log. Unfortunately, this pen and paper log system relies on self-reported data, the shortcomings of which have been widely reported. We sought to investigate how accurately self-reported checklist logs reflect the true incidence of compliance with regularly required defibrillator inspections. The Lifepak® 12 defibrillator features a helpful option that can be utilized to verify whether the machine was turned on for the required operator function test. Using this feature, we objectively determined the true frequency of defibrillator inspections. We compared the frequency of missed inspections from the checklist log to that of the computerized archives feature for all hospital defibrillators. There was an overall inspection error rate of 3.7%. Of all missed inspections, 45.8% would not have been captured without this archives feature. This archives feature should prove useful for quality assurance efforts focused on cardiopulmonary resuscitation. Published in 2005 by John Wiley & Sons, Ltd.

Zinc whiskers: hidden cause of equipment failure

If you manage a data center, especially one that sits on a raised floor, zinc whiskers might eventually have an impact on your operations. Zinc whiskers, as the name implies, are tiny hair-like filaments of zinc that actually grow from certain zinc-coated metal surfaces. Zinc whiskers are typically less than a few millimeters long and only a few thousandths of a millimeter wide. Because of their miniscule dimensions, zinc whiskers can be extremely difficult to see without magnification and proper illumination.

Minimising equipment down time under shock load conditions

Random shocks are a leading cause of equipment failure. The shocks arise because of large variations in parameters like operating load, voltage, pressure, thermal load, hardness, contamination, tolerances on clearances and alignments, among other factors. Frequent inspections can help detect deterioration at an early stage and prevent failure. However, inspection is also expensive. The challenge is to develop a model that keeps inspection costs to a minimum while ensuring that the equipment down time is prevented or minimised. This paper first develops a model based on failure due to random shock loads, and then follows it up with a strategy for preventing or minimising such failure at optimum cost.

Expert System for Water Treatment Plant Operation

An expert system based on a combination of forward chaining, backward chaining, and metarules has been developed for water treatment plant operations. The expert system was developed by interviewing a number of plant operators and chemists. Decision trees and fault trees were used to speed the development of the expert system. The combined strategy of using both backward and forward chaining within the expert system allows the number of questions asked of an operator to be minimized to prevent the asking of redundant questions. The methodology developed is applied to a case study water treatment plant. Two of the modules of the expert system are described in this paper. In the first, turbidity treatment module assists an operator in deciding a change in alum dosing in response to a change in treated water quality. In the second, a fault diagnosis module to detect the cause of equipment failure in the plant is presented.