Preventive Maintenance Strategy Research Articles

Application of the FMECA method on the electro-hydraulic system for drilling machine type NKH45

This article focuses on the implementation of a preventive maintenance strategy, responding to a major challenge: the search for optimization in minimizing downtime of the radial drilling machine (NKH45). Crucially positioned at the heart of the mechanical manufacturing process, this machine holds significant influence on overall production performance and availability. In consideration of the strategic importance of this equipment within the framework of the mechanical manufacturing activity, an advanced methodology, in particular FMECA (Failure Mode Effects and Criticality Analysis), is implemented. FMECA represents one of the most recommended approaches in this context, based on reliability, allowing the meticulous identification of the main causes of functional failures of equipment. FMECA is a risk prevention method. It is used to improve the reliability of a product, a process or even a means of production. It makes it possible to assess the criticality of potential failures in a system. According to the recommendations of the maintenance department of the company specializing in the construction of agricultural equipment, this study will focus on a subsystem of the electro-hydraulic part of the NKH45 radial drilling machine, we use the FMEA Analysis method (Failure Mode Effects and Criticality Analysis). Thanks to this approach, it is possible to identify the main reasons for equipment breakdowns that affect production and to classify these breakdowns in order to develop an optimal maintenance strategy aimed at: reducing the number of breakdowns, preventing breakdowns, improving the preventive maintenance, reduce downtime, improve corrective maintenance, therefore the ultimate objective of this approach is to design an optimal maintenance action plan in order to significantly minimize downtime, which slows down production flow.

Transfer Learning for Prognostics and Health Management: Advances, Challenges, and Opportunities

Transfer Learning for Prognostics and Health Management: Advances, Challenges, and Opportunities

Optimal Preventive Maintenance Policy for Equipment Rented under Free Leasing as a Contributor to Sustainable Development

Leasing has proven to be a business model that is perfectly suited to the circular economy. It significantly contributes to sustainable development by enabling the reuse of machinery and equipment after each lease period and by including preventive maintenance and overhauls within and between lease terms. This helps to extend the life cycle of equipment, promote value recovery, and reduce waste. This paper examines an imperfect preventive maintenance (PM) strategy applied to equipment rented under the terms of “free leasing”. In free leasing, the lessor makes the equipment available to the customer for a specified period of time without charging rent. In return, the customer is required to purchase the equipment’s consumables exclusively from the lessor. The lessor is also responsible for the maintenance of the equipment at the customer’s premises. The greater the quantity of consumables used by the customer, the more the equipment will deteriorate. Consequently, the lessor must be able to determine the most effective approach to preventive maintenance, ensuring that it aligns with the customer’s planned usage rate while maximizing profit. This work proposes a PM strategy to be adopted by the lessor during the free lease period. This strategy involves the performance of imperfect PM actions just before the start of the lease period and then periodically. Different packages of preventive actions can be applied each time, with each package having a different cost depending on the level of effectiveness in terms of rejuvenating the equipment. Minimal repairs are performed in the event of equipment failure. The decision variables are the PM period to be adopted and the maintenance efficiency level to be chosen for each preventive intervention. The objective is to determine, for a given customer with an estimated consumption rate profile of consumables, the optimal values of these decision variables so that the lessor maximizes their profit. A mathematical model is developed to express the lessor’s average profit over each lease period. A solution procedure is developed for small instances of the problem, and an Artificial Bee Colony algorithm is implemented for larger instances. A numerical example and a sensitivity analysis are presented.

Incremental analysis of load handling device deflection considering lubrication degradation for predictive maintenance

In the Industry 4.0 era, unmanned automated factories heavily rely on load handling devices (LHDs), which are indispensable but susceptible to failures. Deflection is a crucial health indicator, with excessive deflection causing issues such as tilting and reduced positioning accuracy. This underscores the necessity for reliable deflection prediction methods in LHDs, a currently under-researched area, essential for informing predictive maintenance strategies. This study introduces an incremental analysis method that addresses the impact of internal cumulative wear and lubrication degradation on deflection increase. It involves iteratively updating a multiparametric model to represent the cumulative wear process and utilizes an empirical model based on the Stribeck curve to describe lubrication degradation. To enhance accuracy, particle filtering (PF) is employed to revise friction coefficients derived from the empirical model. Experimental data from an LHD operating in a load-carrying mission validates the accurate and robust nature of this method, particularly when compared with non-friction coefficient revising counterparts. As a typical application case study for preventive maintenance, this method is utilized to simulate various scenarios of lubrication replenishment and quantify the effect of timing on LHD lifespan enhancement through lubricant replenishment. The results facilitate the optimization of maintenance schedules and offer a new approach for updating lubrication-related parameters in digital twin models of equipment-level products, in line with preventative maintenance strategies.

Multi-Objective Fuzzy Green Scheduling Optimization Method of Special Vehicle Body-in-White Prototype Shop Considering Equipment Preventive Maintenance

Based on the advantages of non-contact, flexibility, and small heat-affected zone, laser processing technology is widely used in material removal, addition, modification and other manufacturing fields. However, the emission of large amounts of smoke during laser processing of metal materials will result in environmental pollution in the job shop. Additionally, the processing time of laser equipment becomes uncertain as the power decays with prolonged use. Therefore, this paper is mainly focus on a multi-objective flexible job shop fuzzy green scheduling problem to minimize the fuzzy makespan, fuzzy total energy consumption and fuzzy total smoke emission. To solve it, an improved multi-objective particle swarm optimization algorithm has been designed. Firstly, a preventive maintenance strategy is proposed to reduce the makespan and equipment failure frequency by considering periodic power attenuation of laser equipment. Next, different particle updating strategies are employed to enhance particles’ exploration capabilities. Subsequently, five neighborhood structures are introduced to improve the performance of local search. Finally, comparison experiments are conducted with an expanded common benchmark, and a production case study in a special vehicle body-in-white prototype job shop. The results effectively demonstrate the feasibility and effectiveness of the developed model and improved multi-objective particle swarm optimization algorithm.

Hybrid Approach for Detection and Diagnosis of Short-Circuit Faults in Power Transmission Lines

In this article, the main problem under investigation is the detection and diagnosis of short-circuit faults in power transmission lines. The proposed fault detection (FDD) approach is mainly based on principal component analysis (PCA). The proposed fault diagnosis/identification (FAI) approach is mainly based on sliding-window versions of the discrete Fourier transform (DFT) and discrete Hilbert transform (DHT). The main contributions of this article are (a) a fault detection approach based on principal component analysis in the two-dimensional scores space; and (b) a rule-based fault identification approach based on human expert knowledge, combined with a probabilistic decision system, which detects variations in the amplitudes and frequencies of current and voltage signals, using DFT and DHT, respectively. Simulation results of power transmission lines in Portugal are presented in order to show the robust and high performance of the proposed FDD approach for different signal-to-noise ratios. The proposed FDD approach, implemented in Python, that can be executed online or offline, can be used to evaluate the stress to which circuit breakers (CBs) are subjected, providing information to supervision- and condition-based monitoring systems in order to improve predictive and preventive maintenance strategies, and it can be applied to high-/medium-voltage power transmission lines as well as to low-voltage electronic transmission systems.

Rams-based comprehensive evaluation method for EMU components

EMU (Electric Multiple Units) components were evaluated in multiple dimensions, i.e. reliability, availability, maintainability, and safety. A parameter estimation method, which is based on the data on the mileage between failures and quantities monitored in the actual operation, was put forward for the Weibull proportional hazard model to determine their operational reliability. A multi-index component maintainability evaluation model, which is based on empirical distribution functions, and a maintainability cluster evaluation model, which is based on whitened weight functions, were built, and the entropy weight method was introduced to determine the estimates of the indexes. A component safety evaluation method, which has the safety risk factor as its evaluation basis, was built using both the fuzzy theory and the analytic hierarchy process. A RAMS comprehensive evaluation model based on the linear weighted synthesis method was proposed. All of the models and methods are aimed at meeting the needs of this multidimensional component evaluation. This evaluation method was verified using actual gearbox operation parameters. The reliability and availability evaluation curves based on the Weibull proportional hazard model, whose parameters were determined according to the failure data, show that the EMU component reliability and availability decrease as the EMU mileage increases, thus verifying that this evaluation method is reasonable. The calculated EMU gearbox degree of safety is 0.8659, at an excellent level. The evaluated EMU gearbox maintainability is at the moderate level of III. The proposed evaluation method can reflect the relations of the component reliability and availability to the quantities monitored in operation more accurately and more comprehensively and the actual component maintainability and safety levels in a more objective way. It can provide a theoretical basis for managers and design departments to improve and evaluate the performance of the components, determine their maintenance intervals, and develop scientific preventive maintenance strategies in order to reduce their overhaul cost and increase their maintenance efficiency.

Research on joint scheduling method of order grading and machine maintenance

AbstractIn the multi‐variety and large‐scale order production mode, enterprises must balance delivery deadlines and maintain customer satisfaction while also considering the health status of machines. Therefore, the authors propose a method for jointly optimising production scheduling and machine maintenance. Before machine processing, an order value grading and sorting model and a machine health‐status group partitioning model are constructed to classify orders into different production value levels and machines into different health‐status groups, respectively. During machine processing, based on the Weibull distribution theory, a ‘health evaluation function value’ constraint machine preventive maintenance (PM) model and PM strategy are proposed to account for the changing health status of machines; these are integrated with the order allocation machine strategy as decision‐making elements in the production schedule. Finally, two case studies are used to verify the effectiveness of this proposed model and method. The results show that compared to general scheduling schemes, the proposed method can reduce total delay and improve customer satisfaction. Additionally, the PM plan proposed in this method can improve production efficiency and line stability compared to periodic maintenance.

Impacts of climate change on environmental and economic sustainability of flexible pavements across China

Pavements are susceptible to accelerated deterioration due to changing climate conditions, leading to increased maintenance and excess fuel consumption through pavement-vehicle interaction. China's diverse climates raise concerns about the environmental and economic sustainability of flexible pavements amid climate change and the effectiveness of preventative maintenance strategies. This study examines climate change's potential impacts on long-term pavement performance, greenhouse gas (GHG) emissions, and costs, employing the Mechanistic-Empirical Pavement Design Guide method. The calibrated World Bank's Highway Development and Management Model 4 assesses the impacts of surface characteristics on vehicle fuel consumption. Life cycle assessment and life cycle cost analysis quantify GHG emissions and costs. Results reveal significant pavement deterioration in Southeast and Central China. Preventative maintenance strategies reduce fuel consumption, with GHG emissions and cost savings from smoother driving conditions outweighing those from maintenance. These insights stress the importance of proactive maintenance strategies for mitigating climate-induced deterioration and enhancing sustainability.

A hybrid deep learning approach to integrate predictive maintenance and production planning for multi-state systems

This paper develops a data-driven approach to dynamically integrate tactical production and predictive maintenance planning for a multi-state system composed of several series-parallel machines. The objective is to determine an integrated lot-sizing and preventive maintenance strategy that will minimize the sum of maintenance and production costs, while satisfying the demand for all products over the entire horizon. A rolling horizon planning strategy is adopted to continuously update the production and maintenance plans based on new data obtained through sensors. Unlike the existing integrated models, we develop a hybrid deep learning (DL) approach to coordinate maintenance and production decisions for a multi-state system composed of multiple machines. To accurately predict the health condition of each machine, the developed hybrid DL method combines the powers of convolutional neural network (CNN), long-short-term memory (LSTM), and attention technique. We use multi-state reliability theory to estimate the production capacity. Furthermore, a genetic algorithm is developed to solve large-scale problems. Benchmarking data are used to compare the results of our data-driven approach with a model-based approach, a pure LSTM, and a CNN-LSTM approach. This comparison is based on prediction accuracy, solution quality, and computational time. The obtained results show the superiority of the suggested CNN-LSTM-attention data-driven framework integrating maintenance and production.

Reliability-Based Preventive Maintenance Strategy for Subsea Control System

The subsea control system, a pivotal element of the subsea production system, plays an essential role in collecting production data and real-time operational monitoring, crucial for the consistent and stable output of offshore oil and gas fields. The increasing demand for secure offshore oil and gas extraction underscores the necessity for advanced reliability modeling and effective maintenance strategies for subsea control systems. Given the enhanced reliability of subsea equipment due to technological advancements, resulting in scarce failure data, traditional reliability modeling methods reliant on historical failure data are becoming inadequate. This paper proposes an innovative reliability modeling technique for subsea control systems that integrates a Wiener degradation model affected by random shocks and utilizes the Copula function to compute the joint reliability of components and their backups. This approach considers the unique challenges of the subsea environment and the complex interplay between components under variable loads, improving model accuracy. This study also examines the effects of imperfect maintenance on degradation paths and introduces a holistic lifecycle cost model for preventive maintenance (PM), optimized against reliability and economic considerations. Numerical simulations on a Subsea Control Module demonstrate the effectiveness of the developed models.

Maximizing Machine Efficiency in Indonesia's Noodle Industry

This study utilizes the Total Productive Maintenance (TPM) method, focusing on the Overall Equipment Effectiveness (OEE) to evaluate machine efficiency within a notable Indonesian noodle manufacturer. In the rapidly growing food manufacturing industry, particularly dry and instant noodles, machine performance is critical for meeting increasing domestic and international market demands. Results reveal that the OEE values are below industry standards, primarily due to speed reductions, idle and minor stoppages, process defects, and breakdown losses. These findings emphasize the urgent need for preventive maintenance strategies to mitigate downtime and enhance productivity, providing valuable insights for global industries facing similar challenges. Highlights: Rapid growth in the food manufacturing industry demands increased productivity. Machine performance is crucial for maintaining optimal production processes. Total Productive Maintenance (TPM) and Overall Equipment Effectiveness (OEE) methods help measure and improve machine efficiency. Keywords: Total Productive Maintenance, Overall Equipment Effectiveness, Six Big Losses.

A stochastic model of preventive maintenance strategies for wind turbine gearboxes considering the incomplete maintenance

In contemporary large wind farms, the combination of condition-based maintenance (CBM) and time-based maintenance (TBM) has become a prevalent approach in preventive maintenance, which is an indispensable part to ensure the safe, stable and environmental operation of equipment. However, the utilization of an inappropriate maintenance strategy may result in over-maintenance or under-maintenance, leading to unstable equipment operation. Furthermore, the majority of preventive maintenance involves replacement maintenance, which may have adverse effects on the performance of wind turbines with excessive maintenance time. Therefore, this paper takes the gearbox as a case study to introduce the incomplete maintenance parameters into the failure rate function to establish a state model based on the stochastic differential equation (SDE) and describing the state change of incomplete maintenance. And then simulating the state model of the gearbox and the joint preventive maintenance strategy of TBM and CBM through examples, resulting the time-based incomplete maintenance (TBIM) is proposed based on the TBM and the incomplete maintenance, and a new joint preventive maintenance strategy incorporating TBIM and CBM is proposed. Through developing the decision-making process of the maintenance strategy to optimize the inappropriate maintenance which including over-maintenance and under-maintenance and simulating the optimized preventive maintenance strategy to compare with that of TBM and CBM and verify the superiority and effectiveness of the proposed maintenance method.

Joint production, transportation, and maintenance in downstream fuel supply chain

This paper presents an integrated production, maintenance, and transportation strategy for a multi-site, multi-demand, multi-tank fuel production system. Firstly, each site’s economic fuel production plan is developed under a random demand and service level. Then, a transportation plan is proposed to collaborate between sites in case of unavailability site or unmet needs, minimizing the total cost. According to the correlation between production and maintenance, a periodically preventive maintenance strategy is developed for both refining machines and storage tanks, while minimizing the incurred costs by considering the influence of fuel production rate on their failure rates. Additionally, in this study, experimental tests are performed on an industrial tank, based on the real-life case of fuel companies, to investigate the tanks’ degradation due to corrosion phenomena. The resolution complexity of the analytical model arises from its stochastic nature due to random demands. Hence, a transformation to a deterministic form is performed. Numerical examples and a sensitivity study are presented to illustrate the effectiveness of the proposed methodology and the obtained results.

Optimization of preventive maintenance in production lines: an approach based on reliability statistics

The maintenance strategy must be taken from the results required by the organization, that is, aiming to meet the budgeted costs, the defined quality and the production program. Therefore, it is up to this strategy to seek to maximize effectiveness in the relationship: use of resources, costs and useful life. This article aims to define a preventive maintenance strategy for a production line, using RCM concepts, aiming to determine an optimal periodicity for the execution of this type of maintenance activity and consequently the reduction of business costs. Experimental data were obtained from the previously referenced article, linking them to the concepts of Reliability-Centered Maintenance and the Maximum Likelihood Estimator. With this, it was possible to obtain the optimal stop times for preventive maintenance of the studied machines, assuming a ratio of costs of preventive maintenance and corrective maintenance. By following the reliability techniques covered throughout the article, a company's production line can ensure that its assets are running smoothly and that any potential issues are addressed efficiently.

Bayesian network approach for reliability analysis of mining trucks

Having a safe and efficient system for mineral transportation is a top priority for all mining operations. Trucks are the most widely used material transportation systems that are applied in both surface and underground mines. Any truck failure disrupts the mineral transportation process and consequently decreases the overall output. Therefore, the reliability analysis of such equipment plays a critical role in increasing the efficiency and productivity of a mining operation. This paper proposes a novel method for analyzing the reliability of a fleet of mining trucks based on the Bayesian Network modeling. Considering the reliability block diagram, the fault tree of trucks was developed according to the logical relationship between the units. Then, a dynamic Bayesian network was constructed according to the conditional probability analysis. Moreover, the relative contributions of each truck’s component to the occurrence of the fleet failure were studied by using critical analysis. The results of this paper show that the successful operation of the fleet of trucks is most sensitive to truck no. 5, which has the highest reliability level in all time intervals. The reliability of the fleet of trucks reaches 0.881 at 20 h, and the fuel injection system of the truck’s engine is the main leading cause of the trucks failure. A proper preventive maintenance strategy should be paid more attention to improve the reliability and availability of the engine system.

School Facilities Maintenance Strategies and Quality Education Delivery in Public Junior Secondary Schools, Lagos State

This study used five assumptions to guide the examination of the techniques for maintaining school infrastructure and providing high-quality education in public junior secondary schools in Lagos State Education District 1. All 42 public junior secondary school principals and teachers in the chosen education district made up the study's population, which used a descriptive and correlational research approach. 216 principals and teachers made up the sample, which was selected using multistage - stratified, simple random, and purposive selection approaches from the sampled 18 schools. A self-constructed questionnaire with two dimensions, one with 20 items on quality education delivery and the other dimension on school facilities maintenance strategies, were used to collect data for the study. For each dimension, the reliability coefficients for this instrument's were found to be 0.77 and 0.79, respectively with a face and content validity being ensured. Person product moment correlation analysis was used as an inferential statistical technique to analyze the data at the 0.05 level of significance. The research findings indicates a noteworthy correlation between preventative maintenance strategy(r = .813; N=216; p< .05), corrective maintenance strategy (r = .786; N=216; p< .05), routine maintenance strategy (r = .765; N=216; p< .05), emergency maintenance strategy (r = .745; N=216; p< .05), remedial maintenance strategy (r = .925; N=216; p< .05) and quality education delivery in Lagos State Education District I. The study thereafter concluded that school facilities maintenance strategies is apparently a major determining factor of quality education delivery in public junior secondary schools in Lagos State Education District I. Thus, the study's recommendations include, among other things, the requirement that maintenance practices and culture be ingrained in everyone and that all parties involved in the education sector bear responsibility for them in order to guarantee the delivery of high-quality education.

Use of condition-based valuation approach to evaluate the maintenance decision of pavement assets: a case study of Yunnan Province in China

Pavements are critical components of highway assets, but accurately estimating their value can be challenging due to the lack of connection between valuation and long-term service performance degradation. To address this issue, the replacement cost and condition-based valuation methods were introduced using the Yunnan province highway in China as an example, which divided pavement into four performance states based on service stages. The rationality of maintenance decisions and the impact of preventive maintenance investment on life cycle assessments were also considered. The results indicate that there are issues of irrational allocation of maintenance funds of the highway in Yunnan province, and a higher proportion of preventive maintenance investment is required. Moreover, it is recommended to balance the maintenance funds for bridge and tunnel engineering in the following year, focusing on bridge engineering with a rapid decline in the newness rate. The implementation of preventive maintenance has a positive correlation with the replacement cost and newness rate of the road sections. Through comparison, it was found that the road surface renewal rate in 2019 was lower than that in 2018, and some highway management departments in certain regions need to adjust their preventive maintenance strategies. Overall, the condition-based pavement asset valuation method comprehensively considers each stage of pavement operation and can serve as an effective tool for evaluating pavement asset depreciation. This research finding can promote the sustainable development of road infrastructure.

Optimization of a preventive maintenance strategy for a product with limited repair times

This paper proposes a base warranty and replacement warranty strategy for repairable products. It should be emphasized that the product failure rate is low in the early stage of the base warranty. Therefore, in order to reduce the manufacturer’s maintenance cost, the manufacturer does not carry out preventive maintenance in the early stage of the base warranty, and carries out regular preventive maintenance in the later stage of the base warranty. At the same time, if the number of product failures in the base warranty exceeds the specified limit, the manufacturer must replace products for customers free of charge according to the warranty contract. After the product is replaced, if the number of product failures does not exceed the limit, the manufacturer will continue to provide customers with the remaining base warranty period of the original product, and the warranty period will continue to be extended for a period of time. By minimizing the expected cost rate, it is shown that the optimal maintenance policy for warranty products is unique. Finally, we give a numerical example and study the effects of periodic preventive maintenance and other relevant parameters on the optimal solution.

Evaluating the freeze-thaw vulnerability of soapstone monuments and geoheritage sites in the Parco del Paradiso (Chiavenna, Italy).

This study investigates the vulnerability of soapstone monuments and ancient quarries in the Parco del Paradiso, Chiavenna, to freeze-thaw cycles. It employs climate-based damage functions to assess the risk of weathering-induced damage. A crucial aspect of the assessment involves the study of the homogeneity and trends in the selected climate data sources in the area of the case study. These data sources are ground-based weather stations, and in-situ installed temperature-humidity data loggers. The findings of this contribution underscore the significance of appropriately chosen reference climatic series and validation tests in ensuring the accuracy of the climate signal to analyze freeze-thaw statistics for specific cultural heritage sites. These insights are essential not only for supporting reliable assessment of the vulnerability of these invaluable heritage sites, which have played a vital role in local architecture and trade since Roman times, and for the successive development of long-term preventive conservation and maintenance strategies.