Extend Equipment Life Research Articles

Fretting wear behaviour of high strength alloy steel induced by plasma nitriding and post-oxidation

Fretting wear is caused by a relative movement of the material surface on a small scale. Finding ways to reduce fretting wear is of great importance for extending equipment life and reducing maintenance costs. With the increase of load, the effecrt of fretting wear adhesion of surface treated material becomes more obvious. The materials treated with post-oxidation have stronger wear adhesion than those treated only with plasma nitriding, but the total wear volume is reduced. In this paper, the fretting wear of high strength steel alloy with different surface treatments is simulated by the finite element method. The coefficient of friction and wear volume can be obtained from the experimental results, and the wear coefficient is calculated using the wear energy model. The predicted two-dimensional wear profile curves (U-shape and W-shape wear profiles) under different wear conditions are compared with the experimental results. The W-shape wear profile is used to describe the adhesion state of the worn surface. In addition, the surface deformation and mechanical properties of U-shape and W-shape wear profiles are analysed. The simulation results show that double-sided wear and the addition of adhesive layer is important for the prediction of wear characteristics.

INTRODUCTION OF THE «PREDICTIVE MAINTENANCE» CONCEPT TO INCREASE RELIABILITY AND PREDICTABILITY OF REPAIRS

This research article discusses the importance of implementing the Predictive Maintenance concept in the context of rocket and artillery weapons to improve the reliability and predictability of repair work. The article discusses key aspects of this concept, including its basic principles and methods, including the use of data analytics and artificial intelligence. The authors analyze the benefits of Predictive Maintenance in the military context, pointing to reduced repair costs, increased availability of military equipment, and extended equipment life. In addition, the article examines the challenges that arise when implementing this concept and provides recommendations on how to overcome them. Modern military equipment requires a high level of reliability and combat readiness. The introduction of the Predictive Maintenance concept is becoming extremely important to ensure the efficiency and safety of military equipment. This research article is devoted to the study and discussion of the possibilities of implementing Predictive Maintenance in the context of rocket and artillery weapons in order to increase the reliability and predictability of repair work. In conclusion, the article emphasizes the importance of implementing Predictive Maintenance as a strategic tool to ensure optimal performance and readiness of missile and artillery weapons in the modern military environment. Keywords: Implementation, concept, Predictive Maintenance, reliability, predictability, repair work, data analytics, artificial intelligence, monitoring systems, machine learning algorithms, sensors, failure prediction, planning, efficiency, readiness, rocket and artillery weapons.

Drip irrigation advantages for the cotton field in conditions of salty earth in Bukhara province region

Beginning with the existing irrigation technique used in cotton farming, this paper aims to compare the outcomes of using drip irrigation with traditional irrigation. There are drip irrigation advantages for the cotton field aimed at saving water and other resources, which is demonstrated by the example of decreasing the percent of losses and increasing the outputs and crop yields. Such irrigation conditions allowed collecting and saving water resources per hectare and increasing productivity, decreasing water consumption of surface irrigation thanks to wear-resistant cast iron parts that extended equipment life.

Development of a control system for sorting agricultural products according to specified criteria

The article presents a process control system for sorting agricultural products. The technological process of sorting is described, the composition of the system is indicated, the sorting algorithm is developed. ProgramLab software was used as the modeling environment. Color and weight were selected as sorting criteria. The parameters at the input of the control object, which have the greatest influence on the process were selected. The output parameters were the conveyor speed and the final position of the sorting object. A scheme of cause-and-effect relationships of the sorting process is obtained. A block diagram of the sorting process has been developed. A mathematical model for calculating the speed of a conveyor depending on the distance between the robotic arm and the conveyor, as well as between the robotic arm and the parts unloading points was obtained. A 3d model of the sorting process has been developed. Three basic scenarios for the location of places for unloading agricultural products are considered. The introduction of the developed system will help reduce number of rejects during sorting, increase the efficiency of the sorting process and improve economic performance. Improving the sorting process will help reduce equipment wear and extend equipment life.

Análise de estratégia de manutenção preventiva sistemática em caminhões fora de estrada

Abstract Preventive maintenance is an important strategy to improve physical availability, reduce downtime and extend equipment life. In the context of mining, transport fleets are large equipment and are subject to unplanned events that, in turn, interrupt operation and lead to unavailability. Therefore, the objective of this work is to analyze the main causes that influence the throughput of performance indicators of truck maintenance, focusing on the measurement of impacts caused by inefficiency of preventive maintenance. Thus, the data of 118 trucks were collected in 2022 and the performance calculated. The fleet CAT® 797F was prioritized was not adherent to the programmed, resulting in a production loss of 5,086,957 t o n s and, for maintenance, an unavailability was 9,722.88 h and 3,870 failures. Two evaluation requirements were proposed focusing on evaluating the notes and analyzing the adherence to the preventive. In summary, it was found that of the 783 scheduled preventive maintenance only 607 were performed and presented problems related to compliance with the guidelines. Finally, the inefficiency of the preventive ones generated an increase of more than 819 failures and an impact in the availability of 8.16%.

Modeling and fault detection of a turbofan engine by deep-learning approach

Throughout the world, thousands of passengers travel by air, their quality depends on that of the equipment used. Predictive maintenance is increasingly used to estimate. The remaining useful life of system components and in particular turbofan engines as an essential component. It is used to predict failure before it occurs, optimize component design, extend equipment life, and reduce maintenance costs. However, the algorithms proposed in the literature to date to determine the remaining useful life lack precision with a quadratic error around 20 while the physical models have errors of the order of 0.02. The problem here is how to increase the accuracy of predicting the remaining useful life of a turbofan engine. The objective of this study is to develop a more realistic and accurate algorithm for calculating the remaining useful life of a turbofan engine. To do this, we considered the degradation of the high pressure compressor and the fan as essential organs of the turbojet engine and we used deep learning, known for its high precision linked to a great capacity for extracting information. More specifically, it involved acquiring data on a turbojet engine in operation, pre-processing this data, developing the prediction model, training the model and finally validating the approach in comparison with other diagnostic methods. and to model these defects. We compared two deep learning architectures per application against the CMAPSS dataset to assess their performance. The LSTM architecture we developed prevailed with an RMSE of 13.76, well positioned compared to the literature architecture.

A Predictive Maintenance System for Reverse Supply Chain Operations

Background: Reverse supply chains of machinery and equipment face significant challenges, and overcoming them is critical for effective customer service and sustainable operation. Maintenance and repair services, strongly associated with the reverse movement of equipment, are among the most demanding reverse supply chain operations. Equipment is scattered in various locations, and multiple suppliers are involved in its maintenance, making it challenging to manage the related reverse supply chain operations. Effective maintenance is essential for businesses-owners of the equipment, as reducing costs while improving service quality helps them gain a competitive advantage. Methods: To enhance reverse supply chain operations related to equipment maintenance, this paper presents the operational framework, the methodological approach, and the architecture for developing a system that covers the needs for predictive maintenance in the service supply chain. It is based on Industry 4.0 technologies, such as the Internet of things, machine learning, and cloud computing. Results: As a result of the successful implementation of the system, effective equipment maintenance and service supply chain management is achieved supporting the reverse supply chain. Conclusions: This will eventually lead to fewer good-conditioned spare part replacements, just in time replacements, extended equipment life cycles, and fewer unnecessary disposals.

Optimization of equal-cycle maintenance strategy considering imperfect preventive maintenance

Preventive maintenance is an important means to extend equipment life and improve equipment reliability. Traditional preventive maintenance decision-making is often based on components or the entire system, the granularity is too large and the decision-making is not accurate enough. The meta-action unit is more refined than the component or system, so the maintenance decision-making based on the meta-action unit is more accurate. Therefore, this paper takes the meta-action unit as the research carrier, considers the imperfect preventive maintenance, based on the hybrid hazard rate model, established the imperfect preventive maintenance optimization model of the meta-action unit, and the optimization solution algorithm was given for the maintenance strategy. Finally, through numerical analysis, the validity of the model is verified, and the influence of different maintenance costs on the optimal maintenance strategy and optimal maintenance cost rate is analyzed.

An overview of the application of machine learning in predictive maintenance

With the rise of industrial artificial intelligence (AI), smart sensing, and the Internet of Things (IoT), companies are learning how to use their data not only for analysing the past but also for predicting the future. Maintenance is a crucial area that can drive significant cost savings and production value around the world.
 Predictive maintenance (PdM) is a technique that collects, cleans, analyses, and utilises data from various manufacturing and sensing sources like machines usage, operating conditions, and equipment feedback. It applies advanced algorithms to the data, automatically compares the fed data and the information from previous cases to anticipate or predict equipment failure before it happens, thus helping optimise equipment utilisation and maintenance strategies, improve performance and productivity, and extend equipment life. Robust PdM tools enable organisations to leverage and maximise the value of their existing data to stay ahead of potential breakdowns or disruptions in services, and address them proactively instead of reacting to issues as they arise. Therefore, it has attracted more and more attention of specialists in recent years.
 This paper provides a comprehensive review of the recent advancements of machine learning (ML) techniques widely applied to PdM by classifying the research according to the ML algorithms, machinery and equipment used in data acquisition. Important contributions of the researchers are highlighted, leading to some guidelines and foundation for further studies. Currently, BIENDONG POC is running some pilot PdM projects for critical equipment in Hai Thach - Moc Tinh gas processing plant.

Effect of Intermittent Traveling Water Screen Operation on Fish Survival

AbstractThe U.S. Environmental Protection Agency’s final rule for complying with section 316(b) of the Clean Water Act states that cooling water intakes withdrawing >7.6 million liters per day must use one of seven alternatives to reduce fish impingement mortality. Fish‐friendly traveling water screens (TWSs) are expected to be frequently utilized because of relative ease of retrofit. The final rule requires “continuous or near continuous screen rotation” to facilitate recovery of collected fish as soon as practical. However, intermittent TWS operation could be advantageous for operators because reductions in screen system operating time will extend equipment life and reduce operating costs. A laboratory evaluation was conducted with Golden Shiner Notemigonus crysoleucas, Bluegill Lepomis macrochirus, Largemouth Bass Micropterus salmoides, and Common Carp Cyprinus carpio (42–106 mm TL) to determine whether intermittent rotation would optimize TWS operation and maintain biological performance. A preliminary evaluation was conducted with three approach velocities (0.30, 0.45, and 0.60 m/s) at four stationary screen durations (2, 4, 6, 12 h) in small test flumes to select parameters for a larger‐scale evaluation. The full‐scale evaluation tested two approach velocities (0.30 and 0.45 m/s) at two stationary durations (2 and 4 h at 0.45 m/s; 6 and 12 h at 0.30 m/s) and continuous screen operation. Fish avoided impingement at 0.30 m/s, and approach velocity was the primary determinant of survival probability, with increased velocity leading to increased mortality. Severe injuries occurred more frequently during stationary screen trials than with continuous operation; over 50% of fish had >40% scale loss at 0.45 m/s for stationary trials. The results indicate that continuous rotation is optimal for fish survival at approach velocities between 0.30 and 0.45 m/s. However, intermittent operation at average approach velocities of 0.30 m/s or less may be acceptable for fishes with equal or greater swimming ability than those tested in this study.

Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven

Currently, the Remaining Useful Life (RUL) prediction accuracy of stochastic deterioration equipment is low. Existing researches did not consider the impact of imperfect maintenance on equipment degradation and maintenance decisions. Therefore, this paper proposed a remaining useful life prediction-based maintenance decision model under data-driven to extend equipment life, promoting sustainable development. The stochastic degradation model was established based on the nonlinear Wiener process. A combination of real-time update and offline estimation estimated the degradation model’s parameters and deduced the equipment’s RUL distribution. Based on the RUL prediction results, we established a maintenance decision model with the lowest long-term cost rate as the goal. Case analysis shows that the model proposed in this paper can improve the accuracy of RUL prediction and realize equipment sustainability.

Fuzzy Based Failure Mode and Effect Analysis Towards to Risks of Autonomous Maintenance Activities: As a TPM Implementation

Manufacturing companies attach importance to Total Productive Maintenance (TPM) applications to extend equipment life and increase efficiency. The recommended applications with TPM are carried out under the pillar activities. Autonomous Maintenance (AM) pillar manage the assignments of the operators to undertake routine maintenance work on machine maintenance. Cleaning, lubrication and control activities are one of the steps of AM pillar and operators need to do them daily. However, there are a number of occupational health and safety risks that operators may face during these activities. Safety (S) pillar which serve as another TPM pillar, deal with occupational accidents and possible situations during TPM applications. AM and S pillars work together to assess the risks that may occur during AM applications. Failure Mode and Effect Analysis (FMEA) is also frequently utilized for risk assessment, but this is criticized in terms of the difficulty in reaching the common point of decision-makers’ risk assessment and equal weighting of risk factors. Therefore, it is appropriate to support with the fuzzy logic approach. In this study, the entropy-weighted fuzzy based FMEA method was utilized for identify and prioritize potential risks that may be encountered during the AM activities. Potential risks were revealed and evaluated with the FMEA team. Eleven potential risks were identified in the study. The risk factors of the assessment were weighted by the entropy method. The risk of hand injuring during cleaning the oil below material cutting saw has the highest risk priority.

Measurement, Analysis and Optimization of the Technical Parameters of an Offshore Drilling Platform Top Drive Gearbox

The top drive of an offshore drilling platform is imported. After 5 years of use, bearings of the top drive gear box run on the outer ring, and the bearing seats are worn. Since there are no technical parameters of the gearbox, the paper explains the measurement and analysis process of the technical parameters of the gearbox, and determines the technical parameters of the gearbox according to the technical standards. Besides, the technical parameters are optimized when repairing the gearbox, and the tolerance matching parameters with the bearing seat are improved, which solves the problems of bearings running on the outer ring and the bearing seat wear. It has improved the carrying capacity of the equipment, achieved independent repair of imported equipment and made useful attempts to reduce equipment failures and extend equipment life.

Statistical Features and Expert Knowledge for Monitoring Operating Efficiency and Conditions of Wastewater Treatment Pumps.

Wastewater treatment requires energy-intensive technology. The demand for industrial and residential wastewater treatment is increasing, and it has been widely accepted that low energy consumption and high operating efficiencies are essential to achieve high operating benefits in wastewater treatment plants (WWTPs). In this study, characteristic parameters of equipment operation were collected and subject to statistical analysis for trend observations in combination with expert knowledge to monitor equipment operating conditions. A methodology was developed to monitor and assess operating efficiencies of industrial equipment while not interfering with the existing operation. The proposed methodology was applied to monitor the pump efficiency in a WWTP. The results showed that the new methodology resulted in real-time acquisition of statistical operating data and enabled online detection of abnormal pump operation. The energy loss to low operating efficiencies of a malfunctioning pump was prevented, thereby allowing the pump to operate at high efficiency with an extended equipment life.

Investigation of various maintenance parameters for auxiliaries in a coal‐handling plant aided by a Visual Basic 6.0–based scheduled program

Schedule maintenance programs rely on the analysis of collected data during running and shut down time. In a preventive maintenance program, it is difficult to a priori determine the time limit or set an alarm indicating the failure time of a specific component. To this end, a Visual Basic 6.0–based software program is here proposed for a comprehensive running and maintenance database management. The developed software is capable of storing all relevant data related to parts, equipment, personnel, spares, failures, costs, etc, and displays a warning message when the expected life of a particular component has been reached. An unscheduled outage heavily impacts on the production cycle costs, depending on the nature of the failure and the damaged part. Since the variation of the used parts and equipment is high, it is not possible to have an all‐encompassing maintenance guide, which is applicable to all equipment. A successful program of preventive and routine maintenance may hence decrease equipment failures rates, extend equipment life, and drastically reduce the overall operating costs. Therefore, the present work can help in preventing unscheduled breakdown of operating systems due to a failure in a component.

Launch of Weir Edge aftermarket services program

Weir Oil & Gas's newly-branded Weir Edge aftermarket services program is designed to extend equipment life and reduce total cost of ownership.

Trajectory Optimization of Pneumatic Marking Needle Based on Genetic Algorithm

Due to the ability to print all types of characters and patterns on the surface of metal parts or components, pneumatic marking machine is widely used in machinery, automobile, aerospace and other fields. For reducing equipment wear and extending equipment life, reducing the printing time as well as printing distance are the key points. In this paper, path optimization objective function model is established which refers to the TSP model. And the genetic operations such as genetic coding, fitness function, selection operator, crossover operator and mutation operator are described. The simulation results show that the optimization of the pneumatic marking path by using genetic algorithm can significantly improve the marking efficiency.

Surface Machining after Deposition of Wear Resistant Hard Coats by High Velocity Oxygen Fuel Technology

The article deals with some aspects related to the machining of surfaces that have been deposited by wear-resistant hard coats using HVOF (high velocity oxygen fuel) technology. HVOF coating is a thermal spray process used to improve a component's surface properties, thus extending equipment life by significantly increasing errosion and wear resistance, along with corrosion protection. For a purpose of this research, the chromium-cobalt alloy - Stellite 6 was used as a sprayed material. Considering that the parts made of stellites are widely used in various industries and very popular in specific applications, it is a big drawback that adequate machining processes for stellites have not yet been developed other than the costly and time-consuming technique of grinding. However, in this research, an attempt has been made to reveal the optimal machining parameters for a lower value of surface roughness for the purpose of successful machining of Stellite 6 using inserts with various radii of cutting edge. Authors have evaluated the quality of machined surface not only by means of Abbott curve, but they have also observed the dependency of arithmetical mean roughness value on both cutting-edge radius and feed. The analyses of cutting tips wear at changeable inserts have shown that typical wear appears in the form of a notch.

Availability analysis of mechanical systems with condition-based maintenance using semi-Markov and evaluation of optimal condition monitoring interval

Maintenance helps to extend equipment life by improving its condition and avoiding catastrophic failures. Appropriate model or mechanism is, thus, needed to quantify system availability vis-a-vis a given maintenance strategy, which will assist in decision-making for optimal utilization of maintenance resources. This paper deals with semi-Markov process (SMP) modeling for steady state availability analysis of mechanical systems that follow condition-based maintenance (CBM) and evaluation of optimal condition monitoring interval. The developed SMP model is solved using two-stage analytical approach for steady-state availability analysis of the system. Also, CBM interval is decided for maximizing system availability using Genetic Algorithm approach. The main contribution of the paper is in the form of a predictive tool for system availability that will help in deciding the optimum CBM policy. The proposed methodology is demonstrated for a centrifugal pump.

Modeling of hot stamping process procedure based on finite state machine (FSM)

Hot stamping technology of high strength steel creates new possibilities for vehicle manufacturers in promoting safety and fuel efficiency. The proportion of hot stamping parts in vehicles increases each year, and there are already hundreds of different types of hot stamping lines throughout the world. However, few studies regarding modeling of hot stamping process procedure have been reported, nor have there been studies regarding cooperative scheduling of manufacturing units (including heating units, transferring units, and forming units), or the relationship between productivity and energy consumption. With the shortening of the vehicle life cycle, auto parts manufacturing must be flexible for multiple types and small batches to meet the challenges of the market. In this paper, a model of the hot stamping process procedure based on finite state machine is established, then the model is adapted for the procedure control of a hot stamping line, consisting of multi-chamber furnaces, linear conveying robots, and a mechanical servo press. The cooperative scheduling of various manufacturing units, especially the method which matches multiple heating chambers with a single forming die, is focused on. Finally, a hot stamping line with multi-chamber furnaces is designed and implemented, and the analysis of its production sequence, energy consumption, and delivery cycles is performed. The results show that with the new modeling method of hot stamping process based on finite state machine, the new developed hot stamping line is capable of multiple production objectives and flexible configuration in accordance with production and delivery periods. This active configuration aids in extending equipment life and reduce energy consumption. The modeling method expands production models for hot stamping, from the mass production model to multiple types and small batches production model.