Preventive Maintenance Strategy Research Articles

Hierarchical explicit–implicit combined sensing-based real-time monitoring method for the service performance of complex equipment

PurposeReal-time monitoring of the critical physical fields of core components in complex equipment is of great significance as it can predict potential failures, provide reasonable preventive maintenance strategies and thereby ensure the service performance of the equipment. This research aims to propose a hierarchical explicit–implicit combined sensing-based real-time monitoring method to achieve the sensing of critical physical field information of core components in complex equipment.Design/methodology/approachSensor deployable and non-deployable areas are divided based on the dynamic and static constraints in actual service. An integrated method of measurement point layout and performance evaluation is used to optimize sensor placement, and an association mapping between information in non-deployable and deployable areas is established, achieving hierarchical explicit–implicit combined sensing of key sensor information for core components. Finally, the critical physical fields of core components are reconstructed and visualized.FindingsThe proposed method is applied to the spindle system of CNC machine tools, and the result shows that this method can effectively monitor the spindle system temperature field.Originality/valueThis research provides an effective method for monitoring the service performance of complex equipment, especially considering the dynamic and static constraints during the service process and detecting critical information in non-deployable areas.

Improving Equipment Maintenance—Switching from Corrective to Preventative Maintenance Strategies

This paper explores different building maintenance strategies in commercial buildings in Sydney, Australia, focusing on corrective maintenance (CM) and preventive maintenance (PM). While CM involves rectifying issues after they occur, PM aims to enhance productivity by anticipating potential issues. Although PM seems more logical, the decision to implement this type of maintenance strategy are typically made based on item reliability, failure frequency, and downtime cost, commonly found in manufacturing facilities or critical environments. However, as found in the selected/surveyed commercial real estate buildings, CM was more frequently adopted in aged facilities with older infrastructure, and PM was favoured for buildings without structural deficiencies; however, operating equipment failures were common. However, in many cases, decision makers did not consider the broader effects of downtime beyond direct financial losses, costs associated with customer satisfaction, worker efficiency, rent abatements, and reputation damage. While each building is unique and may require a bespoke maintenance schedule, this study’s insights may help managers select the most appropriate maintenance strategy. Nonetheless, further research is needed to investigate the role of innovative technologies (such as machine learning and artificial intelligence) in enhancing maintenance efficacy and explore the influences of economic shifts, corporate and financial objectives, and the availability of technical resources.

Optimization of Maintenance Strategies in an Oil and Gas Production Facility to Minimize Maintenance Cost

Abstract: In this study, maintenance strategies in an oil and gas production facility were optimized to minimize maintenance costs. The centrifugal pump system in Port Harcourt Refinery was selected for this study because it significantly impacts on the productivity of the refining plant. The pump system’s failure mode effects and criticality analysis (FMECA) were examined, and an optimized maintenance task was developed for the system. The exponential reliability method was employed to analyse the pump’s failure data to determine the pump systems' failure rate and reliability while reliability centred maintenance (RCM) and linear programming (LP) model were employed to optimize the pump’s maintenance strategies and the pump’s maintenance labour force respectively. Broad-based results of the optimized maintenance strategies consisted of on-condition-directed (CD) maintenance, preventive maintenance (PreM), and Proactive maintenance (ProM) strategies to be carried out monthly. The optimized maintenance labour force result revealed that approximately three engineers and two technicians should be employed for the pump’s maintenance task with a minimum of N2, 585, 700 as cost of salaries for the labour force monthly as against the current maintenance labour force requiring fourteen engineers and thirteen technicians monthly. The results showed that the labour cost decreased from N180,000,000 per year to N31,028,400 per year (approximately 82.76% reduction) using the proposed optimized maintenance task compared to the current maintenance plan. Conclusion and recommendation were made that the maintenance optimization technique presented in this work should be adopted by the oil and gas processing and production companies in order to minimize maintenance cost.

A Novel RUL-Centric Data Augmentation Method for Predicting the Remaining Useful Life of Bearings

Maintaining the reliability of rotating machinery in industrial environments entails significant challenges. The objective of this paper is to develop a methodology that can accurately predict the condition of rotating machinery in order to facilitate the implementation of effective preventive maintenance strategies. This article proposed a novel RUL-centric data augmentation method, designated as DF-MDAGRU, for the purpose of predicting the remaining useful life (RUL) of bearings. This model is based on an encoder–decoder framework that integrates time–frequency domain feature enhancement with multidimensional dynamic attention gated recurrent units for feature extraction. This method enhances time–frequency domain features through the Discrete Wavelet Downsampling module (DWD) and Convolutional Fourier Residual Block (CFRB). This method employs a Multiscale Channel Attention Module (MS-CAM) and a Multiscale Convolutional Spatial Attention Mechanism (MSSAM) to extract channel and spatial feature information. Finally, the output predictions are processed through linear regression to achieve the final RUL estimation. Experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches on the FEMETO-ST and XJTU datasets.

Mechanics-informed autoencoder enables automated detection and localization of unforeseen structural damage

Structural health monitoring ensures the safety and longevity of structures like buildings and bridges. As the volume and scale of structures and the impact of their failure continue to grow, there is a dire need for SHM techniques that are scalable, inexpensive, can operate passively without human intervention, and are customized for each mechanical structure without the need for complex baseline models. We present a novel “deploy-and-forget” approach for automated detection and localization of damage in structures. It is a synergistic integration of entirely passive measurements from inexpensive sensors, data compression, and a mechanics-informed autoencoder. Once deployed, the model continuously learns and adapts a bespoke baseline model for each structure, learning from its undamaged state’s response characteristics. After learning from just 3 hours of data, it can autonomously detect and localize different types of unforeseen damage. Results from numerical simulations and experiments indicate that incorporating the mechanical characteristics into the autoencoder allows for up to a 35% improvement in the detection and localization of minor damage over a standard autoencoder. Our approach holds significant promise for reducing human intervention and inspection costs while enabling proactive and preventive maintenance strategies. This will extend the lifespan, reliability, and sustainability of civil infrastructures.

Research on bridge safety early warning method based on strain energy theory and health monitoring data

Bridges are technology-intensive and heavily invested permanent infrastructure. After completion and opening to traffic, bridge structures are easy to be affected by factors such as traffic load and atmospheric environment. Therefore, it is necessary to do safety warning and evaluation of bridges, especially the abnormal behavior in the early stages of bridge operation. In this research paper, a large-span continuous rigid frame bridge installed with the health monitoring system (HMS), of which a large amount of health monitoring data are collected by the HMS, is used as an example, and then a bridge safety early warning method is proposed when the bridge is during early operating period. First of all, the research finding that the internal stress of the prototype bridge obeys normal distribution through statistical analysis is used; next, we deduce that the strain energy inside the prototype bridge is subject to the Non-central Chi-square distribution combined with the strain energy and statistical theories; in the end, the key probability density distribution function of strain energy and its parameters are derived by using the key stress distribution function of the high performance concrete C50 strength grade used in the prototype bridge. The method recommended in this paper is conducive to the formulation of bridge preventive maintenance strategies.

Integrating Learning-Driven Model Behavior and Data Representation for Enhanced Remaining Useful Life Prediction in Rotating Machinery

The increasing complexity of modern mechanical systems, especially rotating machinery, demands effective condition monitoring techniques, particularly deep learning, to predict potential failures in a timely manner and enable preventative maintenance strategies. Health monitoring data analysis, a widely used approach, faces challenges due to data randomness and interpretation difficulties, highlighting the importance of robust data quality analysis for reliable monitoring. This paper presents a two-part approach to address these challenges. The first part focuses on comprehensive data preprocessing using only feature scaling and selection via random forest (RF) algorithm, streamlining the process by minimizing human intervention while managing data complexity. The second part introduces a Recurrent Expansion Network (RexNet) composed of multiple layers built on recursive expansion theories from multi-model deep learning. Unlike traditional Rex architectures, this unified framework allows fine tuning of RexNet hyperparameters, simplifying their application. By combining data quality analysis with RexNet, this methodology explores multi-model behaviors and deeper interactions between dependent (e.g., health and condition indicators) and independent variables (e.g., Remaining Useful Life (RUL)), offering richer insights than conventional methods. Both RF and RexNet undergo hyperparameter optimization using Bayesian methods under variability reduction (i.e., standard deviation) of residuals, allowing the algorithms to reach optimal solutions and enabling fair comparisons with state-of-the-art approaches. Applied to high-speed bearings using a large wind turbine dataset, this approach achieves a coefficient of determination of 0.9504, enhancing RUL prediction. This allows for more precise maintenance scheduling from imperfect predictions, reducing downtime and operational costs while improving system reliability under varying conditions.

Preventive Maintenance Strategy Prediction of the Firewater Systems Based on the Pythagorean Fuzzy Cost–Benefit–Safety Analysis and Fuzzy Dematel

The firewater system is a complex system associated with the safety process of Hydrogen storage tanks. Predicting preventive maintenance strategies is essential to ensure the long-term reliability of this system. Therefore, it is necessary to evaluate the multistate reliability of the firewater system in order to predict preventive maintenance strategies and provide safety measures. A polymorphic fuzzy fault tree analysis (PFFTA) for the risk analysis of complex systems has attracted much attention because of its powerful evaluation capability and its ability to analyze relationships among basic events. However, obtaining multistate failure probability (MFP) data for basic events in PFFTA has always been a major challenge. It is also difficult to quantify the minimum cut set (MCS) in PFFTA and determine the critical components for selecting a preventive maintenance strategy. In this study, we propose the Pythagorean fuzzy cost–benefit–safety analysis by using the PFFTA, an improved consistency aggregation method (I-CAM), and fuzzy Dematel for a predictive preventive maintenance strategy. In the proposed approach, the I-CAM method was used to collect and aggregate weights of experts’ opinions to evaluate the MFP of basic events in PFFTA. As a result, a triptych cost–benefit–safety analysis based on Pythagorean fuzzy sets (PFSs) and the sum-product method (SPM) was estimated to reduce expert subjectivity, support an improved cost-effectiveness index to rank critical components, and fuzzy Dematel to evaluate influence of proposed preventive maintenance actions. To clarify the effectiveness and feasibility of the proposed methodology, a case study of the firewater system related to the plant is located in SONELGAZ electricity power plant (OUMACHE Unit) was demonstrated. Both evaluations of the cost–benefit–safety analysis of the critical component were performed, and selected the influence of preventive maintenance strategy of the firewater system was predicted.

Research on wind turbines preventive maintenance strategies based on reliability and cost-effectiveness ratio

Purpose The purpose of this study is to optimize the operational efficiency of the entire system by developing a reasonable maintenance strategy for wind turbines that improves component reliability and safety while reducing maintenance costs. Design/methodology/approach A hybrid incomplete preventive maintenance (PM) model based on boundary intensity process is established to give dynamic PM intervals for wind turbines using an iterative method with reliability as a constraint; the selection method of PM and replacement is given based on the cost-effectiveness ratio, which in turn determines the optimal number of PM for wind turbines. Findings The reliability is used to obtain the components’ maintenance cycle, and the cost-effectiveness ratio is used to select the number of maintenance times, thus, getting the optimal maintenance strategy. The validity of this paper’s method is verified by arithmetic cases, which provides a new method for formulating a reasonable PM strategy for wind turbines. Practical implications The wind turbine preventive maintenance strategy for Boundary intensity process proposed in this paper can scientifically formulate the maintenance strategy, optimize the cost-effectiveness per unit of time of the wind power generation system, and solve the problems of difficulty in formulating a reasonable maintenance strategy for the wind turbine components and high operation and maintenance costs. Originality/value In this paper, the authors describe the failure pattern by a Boundary intensity process, establish a hybrid incomplete PM model by introducing a failure intensity increment factor and an age reduction factor and establish a maintenance strategy optimization model with comprehensive consideration of reliability and cost-effectiveness ratio. Finally, the validity of the model in this paper is verified by arithmetic case analysis. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0153/

An aero-engine remaining useful life prediction model based on clustering analysis and the improved GRU-TCN

Abstract Accurately predicting the remaining useful life (RUL) of engines is paramount for implementing effective preventive maintenance strategies, preventing injuries and fatalities caused by equipment failures, and significantly reducing routine repair and replacement costs. However, existing deep learning models often ignore the variable operating conditions in real engineering applications and do not sufficiently consider the interaction between time series and degradation laws, which directly leads to the inability to effectively extract to degradation feature extraction. To address this problem, this study developed a novel combined network model named CA-DRGRU-TTCN, aimed at accurately predicting the RUL of engines. Firstly, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to identify multiple operating conditions, and incorporate the recognition results into the model as additional new features. The first degradation time point is determined by JS divergence. Secondly, the deep connectivity of the residual Gated Recurrent Unit (GRU) module is designed to extract deeper degradation features, and an improved TMSE loss function based on the first degradation time point is applied to Temporal Convolutional Networks (TCN) to better capture the dependency between the time series and the real degradation degree of the engine. Finally, experiment results on the C-MAPSS dataset show that the proposed method achieves better performance compared to existing state-of-the-art methods.

Importance de l’analyse vibratoire sur la maintenance préventive des réducteurs planétaires dans une cimenterie: « cas de la cimenterie de Lukala dans la province du Kongo Central en R.D. Congo »

Planetary gearboxes are essential for reducing the rotational speed of electric motors to a suitable level for cement production equipment, such as grinders and kilns. By integrating a preventative maintenance strategy for these reducers, the cement plant can minimize unplanned downtime, optimize performance and extend the useful life of the equipment. This involves regular inspections, replacing worn parts before they fail, and properly lubricating components to ensure optimal operation. By investing in effective preventative maintenance, cement plants can save money in the long term by avoiding high costs associated with emergency repairs and production interruptions. The impact of a planetary gearbox in a cement plant therefore becomes significant.

Multi-Criteria Decision-Making Framework (AHP-TOPSIS): Pavement Preventive Maintenance Case Study for Ordinary National Trunk Highways

Pavement maintenance and rehabilitation decision-making needs to weigh multiple strategic goals to achieve sustainable development through the pavement maintenance management system. Making decisions regarding pavement preventive maintenance is both intricate and costly. This study introduces a multi-criteria decision-making framework aimed at enhancing the scientific basis of such decisions. The framework first establishes an evaluation system for preventive maintenance strategies by considering three primary evaluation criteria—service functionality, pavement performance, and economic benefits, and then identifies nine specific evaluation indicators to influence these criteria, with a comparison matrix constructed to determine the weight of each indicator in relation to the maintenance decision hierarchy. Following this, the technique for order preference by similarity to ideal solution (TOPSIS) is employed to prioritize four commonly utilized preventive maintenance strategies. The results reveal that pavement condition and maintenance costs are the most influential factors in determining the appropriate preventive maintenance strategies for national highways. The priority rankings for the four strategies—slurry seal, micro-surfacing, chip seal, and ultra-thin overlays—are found to be 56.12%, 63.86%, 12.12%, and 83.52%, respectively, with ultra-thin overlays identified as the optimal choice for second-class highways. The decision-making model utilized in this study enables a multi-dimensional analysis, reducing the subjectivity inherent in expert evaluations and facilitating the prompt identification of the most suitable maintenance strategy.

Optimization of Quality Process Control and Preventive Maintenance Strategy: A Case Study

Optimization of Quality Process Control and Preventive Maintenance Strategy: A Case Study

A Fault Diagnosis Approach Utilizing Artificial Intelligence for Maritime Power Systems within an Integrated Digital Twin Framework

This research focuses on enhancing the preventive maintenance strategies currently employed for induction motors within ship propulsion systems, advocating for a shift towards a predictive maintenance model. It introduces a real-time monitoring framework that continuously observes the induction motor, providing essential support to maintenance personnel. The motor operates under a range of environmental and operational conditions, including temperature fluctuations, rotational speeds, and mechanical loads. These variations can obscure the current time series data, potentially masking signs of actual damage and hindering effective damage detection. To tackle this issue, the proposed framework utilizes artificial intelligence (AI) technology, specifically the well-established autoencoder, in conjunction with the Mahalanobis statistical distance. This approach accounts for the diverse operating conditions during the training phase, allowing it to model complex, non-linear relationships and effectively differentiate between normal and anomalous states. The framework is integrated into a decision support platform designed for real-time operations in maritime settings, offering a sophisticated system architecture that aims to align advanced damage detection methodologies with the maritime industry’s need for real-time, user-friendly solutions.

Multi-objective preventive maintenance strategy and optimization considering unavailability and cost: A case study on VOBC

The Vehicle On-Board Controller (VOBC) is a core component of urban rail transit trains, with high maintenance costs and serious consequences in case of failure. Ensuring long-term VOBC operation while minimizing maintenance costs is of paramount importance. This paper proposes a multi-objective preventive maintenance (PM) strategy and optimization considering system unavailability and maintenance costs. Firstly, we provide a statement of the problem and analyze the impact of different maintenance actions on VOBC. Next, the decreasing process parameter is introduced to adjust the PM interval dynamically. The mathematical models of system unavailability and maintenance costs are derived to construct a multi-objective PM strategy. Then, we develop a hybrid heuristic algorithm including multi-objective particle swarm optimization and genetic algorithm (MOPSO-GA), which is used to obtain the optimal Pareto frontier for maintenance strategies. In the study, we select maintenance strategies by introducing an aversion factor to fulfill the preferences of different decision-makers. A comprehensive sensitivity analysis is performed to illustrate the impact of several critical parameters on the multi-objective PM strategy. The case study on Beijing subway’s VOBC is also executed. The outcomes indicate that the proposed method can dynamically adjust to match the preferences of diverse decision-makers and the MOPSO-GA algorithm has excellent robustness on most parameters. This approach allows for creating field maintenance strategies catering to the varied maintenance requirements of urban rail transit VOBC.

A Transformer Maintenance Interval Optimization Method Considering Imperfect Maintenance and Dynamic Maintenance Costs

As one of the most critical components of the power grid system, transformer maintenance strategy planning significantly influences the safe, economical, and sustainable operation of the power system. Periodic imperfect maintenance strategies have become a research focus in preventive maintenance strategies for large power equipment due to their ease of implementation and better alignment with engineering realities. However, power transformers are characterized by long lifespans, high reliability, and limited defect samples. Existing maintenance methods have not accounted for the dynamic changes in maintenance costs over a transformer’s operational lifetime. Therefore, we propose a maintenance interval optimization method that considers imperfect maintenance and dynamic maintenance costs. Utilizing defect and maintenance cost data from 400 220 KV oil-immersed transformers in northern China, we employed Bayesian estimation for the first time to address the distribution fitting of defect data under small sample conditions. Subsequently, we introduced imperfect maintenance improvement factors to influence the number of defects occurring in each maintenance cycle, resulting in more realistic maintenance cost estimations. Finally, we established an optimization model for transformer maintenance cycles, aiming to minimize maintenance costs throughout the transformer’s entire lifespan while maintaining reliability constraints. Taking a transformer’s strong oil circulation cooling system as an example, our method demonstrates that while meeting the reliability threshold recognized by the power grid company, the system’s maintenance cost can be reduced by 41.443% over the transformer’s entire life cycle. Through parameter analysis of the optimization model, we conclude that as the maintenance cycle increases, the factors dominating maintenance costs shift from corrective maintenance to preventive maintenance.

Preventive Maintenance in the 3D Atomic IV Printer Machine using the Analytic Hierarchy Process

Preventive maintenance (PM) of equipment is a crucial aspect of effective manufacturing systems, as inadequate PM can lead to various negative issues, such as quality, productivity cost, customer cost, and maintenance costs. This paper presents a case study approach using the Analytic Hierarchy Process (AHP) and ranking of critical elements for the PM equipment of a 3D Atomic IV printer machine at The Instituto Tecnológico de Ciudad Juárez. AHP was used to find the equipment problem sensors and determine all the criteria weights and rank elements. The principal methodology is applied to a 3D Atomic IV printer to formulate a critical PM strategy.

Enhancing Ship Maintenance Management in Maritime Engineering Education: Insights from Vocational Internships

This research investigates ship maintenance management within the context of vocational education in maritime engineering, focusing on the experiences of senior students during internships in port and shipping management industries. Utilising qualitative methods, including semi-structured interviews and focus group discussions, the study explores the practical application of theoretical knowledge in preventive, corrective, and proactive maintenance strategies. Seven participants from a shipping vocational school in Jakarta provided insights into methodologies, challenges, and educational outcomes in ship maintenance. Key findings highlight the effectiveness of preventive maintenance in enhancing operational reliability and reducing downtime, despite challenges such as resource constraints and operational disruptions. Recommendations include curriculum enhancements to integrate advanced training in predictive maintenance technologies and promote sustainable practices. The research underscores the importance of vocational education in preparing future maritime engineers for industry demands and advancing sustainable practices in maritime transportation.

Is it wise for manufacturers to implement preventive maintenance in the sharing business model?

In the sharing business model, manufacturers typically increase customer usage through product design but contend with escalating failure costs stemming from heightened customer usage and moral hazard concerns. The preventive maintenance (PM) typically serves as a means for manufacturers to tackle the growing failure cost problem. However, manufacturers face the challenge of how to make PM strategies in intricate operational environments. We formulate a theoretical model to investigate when a manufacturer should implement PM, taking into account customer usage and product design. Our results suggest that when the fixed cost of PM is low and the customer moral hazard is not sufficiently high, or when both the fixed cost of PM and the customer moral hazard are at moderate levels, it is wise for the manufacturer to implement PM. We also find that the PM can lead to higher consumer surplus by increasing customer utility, achieving a win-win situation. In addition, our findings reveal a positive correlation between the PM effort and product quality, with higher PM effort leading to higher product quality and thus increasing customer usage. In extension models, we further explore the issues of maximising social welfare, non-zero maintenance time and endogenous maintenance frequency

Predicting bridge condition index using an improved back-propagation neural network

The predicted Bridge Condition Index (BCI) is a guiding indicator for formulating preventive maintenance strategies for bridges and is hardly available even if using the most advanced neural network prediction model, owing to the few measured BCIs and neural network model defects. In this paper, an improved Back-Propagation Neural Network (BPNN) model named CPSO-BP-MC is proposed to predict BCI, which has a BPNN as its core, combined with the Coordinated Particle Swarm Optimization (CPSO) algorithm aims to faster convergence and avoid local optimal solution even with less training sample, and the Markov Chain (MC) algorithm aims to further revise the fluctuation of CPSO-BP prediction output due to maintenance and reinforcement. A case study is presented to demonstrate the efficiency of CPSO-BP-MC predicting BCI with fewer training samples. Comparing the predicted BCI of CPSO-BP-MC with the measured BCI and other models, the results show that CPSO-BP-MC converges faster than other models, and can predict the BCI more stably and accurately, and the error between prediction and measured BCI is less than 3 %.