Preventive Maintenance Research Articles

Assessment of the G model adopted in maintenance management within the oil sector institutions in Skikda (Sonatrach) and its role in enhancing maintenance operations

This study delves into the maintenance model G adopted by the maintenance management within the oil sector institutions in Skikda (Sonatrach). Recognized as a strategic autonomous entity that encompasses everything from planning to control, this management sector is pivotal in maximizing and leveraging available production energies based on scientific diagnostics and structured execution. This is instrumental in achieving reduced maintenance costs. Moreover, the study seeks to implement this model within a comprehensive maintenance framework that encapsulates the essential pillars of maintenance management: robust organization, effective human resource management, clear objectives and strategies, along with the support and commitment of top management. A field study examining the various phases of implementing preventive and corrective maintenance using the G model revealed that this function often lacks the requisite emphasis and care. This is attributed to the maintenance management's failure to effectively implement the G model as stipulated by the company, a deficient awareness in activating maintenance management perceived merely as a cost-reduction tool, coupled with a neglect of the human resources pillar and the lack of support and commitment from senior management.

An Optimization Model for Production Scheduling in Parallel Machine Systems

The efficiency and quality of the manufacturing industry are greatly influenced by production scheduling, which makes it a crucial aspect. A well-designed production scheduling scheme can significantly enhance manufacturing efficiency and reduce enterprise costs. This paper presents a tailored optimization model designed to address a more complex production scheduling problem that incorporates parallel machines and preventive maintenance. The proposed solutions aim to achieve a balance between job sequence and machine reliability, considering the minimum maintenance cost rate for determining maintenance cycles of deteriorating machines in real manufacturing scenarios. Furthermore, the objective of minimizing the maximum completion time guides machine assignment and job sequence based on maintenance constraints. The innovation lies in the introduction of a greedy algorithm that utilizes a water injection model to address this NP-hard integrated problem. A pre-distribution model is constructed using the water injection model, and its solution is utilized as input for constructing the production scheduling model, which aids in determining machine assignment and job sequence. This algorithm demonstrates remarkable effectiveness and efficiency, enabling the achievement of an optimal solution. A numerical example is presented to illustrate the computational process, accompanied by an extensive discussion of the results showcasing improved performance. Furthermore, the optimization model developed in this paper can be adapted to tackle the production scheduling problem with modifications tailored for parallel machines.

Studying the Effectiveness of Using Intelligent Mining Machinery Systems on Health, Safety, and Environmental Parameters and Preventive Maintenance

Introduction: The goal of this study is to investigate how the development of technology has affected the industry (especially the mining industry). For this purpose, this paper examines the impact of intelligent mining machinery systems, including tire pressure monitoring systems (TPMS), dispatching systems, and vehicle health monitoring systems (VHMS), on health, safety, and environmental parameters and preventative maintenance. Material and Methods: This study is descriptive-analytical research that was conducted between time intervals before and after employing the intelligent mining machinery systems. Initially, parameters were identified using the Delphi method. These parameters include human accidents, equipment accidents, environmental incidents, warnings and fines in the domains of health, safety, and the environment, tire usage parameters, the shelf life of the tire, oil overfill, fuel consumption, failure rate, mean time between failures, and preventive maintenance compliance schedules in the domain of preventative maintenance. The effectiveness of using these systems was then assessed by comparing the state of the specified parameters before and after the introduction of the intelligent mining machinery systems. Results: The findings of this research indicate that using intelligent mining machinery systems will decrease equipment accidents by 33.3%, extend the useful life of tires by 7.1%, reduce fuel consumption by 14.6%, cut the mean time required to repair by 25.5%, and enhance preventive maintenance compliance schedules by 5.7%. The findings showed the effectiveness of the use of intelligent systems of mining machines was obtained as follows: reduction of equipment accidents by 33.3%, increasing the useful life of tires by 7.1%, reducing fuel consumption by 14.6%, reducing the average downtime of the car for repair by 25.5% and increasing compliance with the maintenance program by 5.7%. Conclusion: Utilizing intelligent mining machinery systems might have a positive impact on the safety of machines, reduce negative environmental effects like fuel consumption, and improve the maintenance of heavy machinery, which would lead to better mining conditions and lower costs.

Effect of solution aging treatment on corrosion resistance and erosion resistance of laser metal deposition 17-4PHss

Slurry erosion and cavitation erosion are the primary causes of failure in pass flow components, and preventive maintenance can effectively extend their service life. In this paper, 17-4PH stainless steel was prepared using laser metal deposition (LMD) technology and aged to investigate the effect of solution treatment on its microstructural transformations. The microhardness, electrochemical corrosion behavior, slurry erosion resistance, and cavitation erosion behavior of the as-built and solution-treated, aged samples were analyzed, with traditional forged specimens used as controls. During heat treatment, the microstructure fully transformed to martensite, and NbC precipitated as a second phase. Solution treatment led to microstructural coarsening, while aging significantly refined the microstructure and produced partial reverted austenite. Microhardness analysis showed that the microhardness of the solution-treated and aged samples increased to 465.5 ± 8.6 HV0.2 due to the combined effects of finer and more uniformly distributed NbC precipitation and grain refinement. The solution aging samples exhibited the lowest corrosion current density and the highest corrosion potential, influenced by the presence of reverted austenite. Slurry erosion results indicated that the corrosive environment accelerated mass loss due to the synergistic effects of mechanical abrasion from gravel and brine corrosion. Meanwhile, the solution aging samples demonstrated superior resistance to slurry erosion, with mass losses in clear water and brine slurry of only 69 % and 63 % of those observed in the traditional forged samples. In the cavitation erosion experiment, the solution aging sample showed the lowest cumulative mass loss (3.54 ± 0.18 mg), and the work-hardening of the reverted austenite further enhanced the cavitation erosion resistance of the solution aging sample.

Optimization design of asphalt emulsion with rejuvenator towards a uniform distribution inside the damaged porous asphalt mixture for a better ravelling resistance

Porous asphalt (PA) pavements usually meet the ravelling disease after opening to traffic for several years. Spraying surface treatment (ST) emulsion to PA pavement is a feasible preventive maintenance technology to alleviate this problem. However, the void clogging, nonuniform distribution and low diffusion efficiency of ST residue might hinder its widespread application. Therefore, this study aims to optimize the ST emulsion formula to improve the ravelling resistance of damaged PA. Usages of adding rejuvenators into ST emulsions were discussed. The orthogonal design method was used to determine the appropriate parameter values for preparing rejuvenator emulsions. The viscosity, contact angle and water boiling tests were then conducted to determine the optimal dosage of rejuvenator emulsion within ST emulsion. Cantabro abrasion test was performed to verify the ravelling resistance of damaged PA mixtures treated with optimized ST. Results showed that a stable and homogeneous rejuvenator emulsion could be produced by precisely controlling the PH value, emulsified temperature and emulsifier dosage. Adding rejuvenator emulsions to ST emulsions significantly improved the wettability. The optimal dosages of aromatic oil and tall oil emulsions are 10% and 5% by mass of ST emulsions. Optimized ST containing rejuvenator emulsions showed better ravelling resistances than pure asphalt emulsion.

Real-Time Structural Health Monitoring of Bridges Using Convolutional Neural Network-Based Loss Factor Analysis for Enhanced Energy Dissipation Detection

This study introduces a novel method for real-time structural health monitoring (SHM) of bridges using a convolutional neural network (CNN) model that leverages loss factor analysis. As bridge structures deteriorate over time, often accelerated by operational loads, maintaining structural integrity and safety becomes critical. The proposed approach utilizes the concept of a loss factor, which represents the process of energy dissipation across different vibration states, as a key indicator of structural health. This factor is computed from vibration energy spectra, which include amplitude and frequency components, processed through the CNN model for high sensitivity to structural changes. The results demonstrate that the energy dissipation of the bridge during operation can be categorized into signals from three distinct sources: structural responses, defects-related indicators, and noise interference. By monitoring variations in the loss factor over time, the model effectively identifies early signs of structural deterioration, which is critical for timely maintenance interventions. The study also highlights the adaptability to different load conditions and environmental factors, ensuring robust performance in various operational scenarios. The findings underscore the potential of the CNN model to transform SHM practices by enhancing early defect detection, supporting preventive maintenance, and ultimately extending the lifespan of bridge infrastructure.

Assessing transformer health through analysis of dissolved gases in cooling oil

Gases can form within the insulation system for various reasons while a transformer is in operation. If these gases are not promptly and properly managed, they can negatively impact the transformer's performance. Using the dissolved gas analysis (DGA) method to identify and assess the type and quantity of dissolved gases in transformer oil can uncover potential issues within the transformer. This information is crucial for guiding preventive maintenance and necessary repairs. Dissolved gas analysis testing was conducted by extracting transformer oil samples to identify signs of disturbances in the transformer based on the dissolved gas content. This research was conducted at the Paiton plant operations and maintenance services division. The condition of transformers was assessed by analyzing dissolved gases using the Rogers ratio method. Results indicate that the transformer at Paiton 9 is in good condition but overheating has occurred and requires treatment. Conversely, the transformers at Paiton 1 and 2 are in poor condition, showing signs of electrical faults and excessive heat. Despite several attempts to add inhibitors and conduct frequent testing, the transformers remained in poor condition, necessitating their replacement or repair.

A self-imitation learning approach for scheduling evaporation and encapsulation stages of OLED display manufacturing systems

In modern organic light-emitting diode (OLED) manufacturing systems, scheduling is a key decision-making problem to improve productivity. In particular, the scheduling of evaporation and encapsulation stages has been confronted with complicated constraints such as job-splitting property, preventive maintenance, machine eligibility, family setups, and heterogeneous release time of jobs. To efficiently solve such complicated scheduling problems, reinforcement learning (RL) has drawn increasing attention as an alternative in recent years. Unfortunately, the performance of the RL-based scheduling methods might not be satisfactory since unexpected correlations between actions are caused by machine eligibility restrictions, making it more challenging to address the credit assignment problem. To minimize the total tardiness, this article proposes a self-imitation learning-based scheduling method in which an agent utilizes past good experiences to exploit efficient exploration. Furthermore, a novel return design is introduced to overcome the credit assignment problem by considering machine eligibility restrictions. To prove the effectiveness and efficiency of the proposed method, numerical experiments are carried out by using the datasets that simulated the real-world OLED display manufacturing systems. Experiment results demonstrate that the proposed method outperforms other baselines, including rule-based and meta-heuristics, as well as the other DRL-based method in terms of the total tardiness while reducing computation time compared to meta-heuristics.

Towards a More Reliable Identification of Non-Conformities in Railway Cars: Experiments with Mask R-CNN, U-NET, and Ensembles on Unbalanced and Balanced Datasets

In every business, equipment requires repair services. Over time, equipment wears out; however, with well-conducted and guided maintenance, this degradation can be controlled, and failed equipment can be restored to operational status. Preventive maintenance allows this concept to be applied, given the great advantages for large companies in reusing equipment and machinery, always putting the worker’s health and safety first. Rail transport has several pieces of equipment that can be reused if they are in a regular and well-defined maintenance cycle. In this sense, this article sought to create a method using real data for identifying cracks in wagons. Through the use of computer vision algorithms to prepare the data, along with several machine learning classification algorithms to locate cracks in train cars, the classification used properly annotated images and obtained great results, with a best case 98.10% hit-rate when wagons had a crack problem.

A learning-based memetic algorithm for energy-efficient distributed flow-shop scheduling with preventive maintenance

In manufacturing systems, implementing preventive maintenance (PM) is essential for ensuring sustainable production since the inevitable wear and tear of machines can significantly affect production efficiency. In today’s decentralized economy, distributed shop scheduling has emerged within the framework of distributed manufacturing to reduce costs, enhance efficiency, and strengthen competitiveness. Thus, this paper proposes a learning-based memetic algorithm (LMA) for addressing the energy-efficient distributed flow-shop scheduling problem with preventive maintenance (EDFSP-PM) to minimize both makespan and total energy consumption simultaneously. First, a mathematical model is formulated and encoding and decoding methods are developed to map solutions to schedules with consideration of PM operations. Second, two heuristics are employed to generate high-quality solutions and various problem-specific operators are designed for different sub-problems and objectives. Third, a hierarchical learning mechanism is proposed via employing multi-layer Q-learning to select appropriate operators for solutions with diverse characteristics. Fourth, a feedback learning mechanism with solution pool is devised to reintegrate solutions from the pool into the search process to enhance search efficiency. Finally, numerical experiments are conducted to verify the effectiveness of the designed mechanisms. The comparative results demonstrate superior performance of the proposed LMA in terms of convergence and diversity.

Navigating uncertainty with cybernetics principles: A scoping review of interdisciplinary resilience strategies for rail systems

AbstractCommon difficulties across industries are discovered in data management, where handling the volume, variety, and quality of data is crucial for informed decisions in uncertain environments. In this context, rail management must navigate complex decision‐making to ensure safety, service continuity, and cost‐effectiveness. The 2020 Stonehaven derailment is an example of the increasing vulnerability of rail infrastructure to environmental factors and systemic failures. It emphasizes the need for resilient systems, proficient at preventative maintenance and adaptable to escalating challenges. These matters further accentuate the need for context‐dependent strategies that bridge theoretical insights and practical applications. This scoping review explores strategies for decision‐making under uncertainty across sectors such as civil infrastructure, agriculture, water management, and emergency response. It unfolds a selection of procedures addressing the impacts of extreme weather and other unexpected disruptions. It also sets a foundation for future research to support rail infrastructure adaptation to climate change by advocating the use of cybernetic principles and artificial intelligence (AI) to enhance decision‐making processes. Cybernetics enables collaborative human‐AI methods, improving adaptability and resilience. However, balancing and incorporating diverse stakeholder viewpoints into decision chains remains difficult. While promising, substantial research and system improvements are needed to fully harness the potential of AI.

Integrating Digitalization and Asset Health Index for Strategic Life Cycle Cost Analysis of Power Converters

In the context of energy storage systems, optimizing the life cycle of power converters is crucial for reducing costs, making informed decisions, and ensuring sustainability. This study presents a comprehensive methodology for calculating the life cycle cost (LCC) of power converters, employing a nine-step process that integrates digitalization, Internet of Things (IoT) technologies, and the Asset Health Index (AHI). The methodology adapts the Woodward model to provide a detailed cost analysis, encompassing the acquisition, operation, maintenance, and end-of-life phases. Our findings reveal significant insights into asset management, highlighting the importance of preventive and major maintenance in controlling failure rates and extending asset life. This study concludes that adopting sustainable business models and leveraging advanced technologies can enhance the reliability and maintainability of power converters, ultimately leading to more competitive and environmentally friendly energy storage solutions.

A Study of Flexible Job-Shop Scheduling with Dual Constraints

The flexible production job shop was selected as the research object, with the goal of minimizing the maximum completion time. A novel production scheduling model was devised by taking into account the sequence of occurrence of two distinct perturbations, namely preventive maintenance and emergency order insertion, within the same production scheduling plan. This was solved by an improved three-body crossover operator genetic algorithm. Finally, the superiority and effectiveness of the new production scheduling were demonstrated through an illustrative analysis.

Reducing Sachet Noodle Defects by Applying the Seven Tools and the FMEA Method

The ICBP is a company that produces the noodle’s sachet on a large scale for domestic and international markets. The sachet’s defect of the Regular Special Fried Noodle for the Saudi Arabia market from January up to December 2023 is 2.39%, which is above the company's standard limit. The aim of this research is to determine the types of defects, and to find out the factors that caused them and find out the solutions to minimize the number of defects. Analysis using the seven tools identified that the most dominant defect is swallen laminate, with a percentage of 51% of total defects. By using the Failure Mode and Effect Analysis (FMEA) method, the highest RPN value was obtained at 280 for the machine factor. The recommended solution is to clean up the die lip thoroughly to ensure the condition of the die lip remains clean, to recalculate the need for a Preventive Maintenance schedule and calibration of the die lips based on machine age and machine usage working hours, and at the same time provide SOP and scheduled training for employees to make sure they know how to do the job.

Joint optimization of preventive maintenance and product quality improvement policies for deteriorating manufacturing systems with quality-reliability dependency

Machine reliability and product quality are usually investigated independently in most traditional models. However, in the actual manufacturing process, machine reliability and product quality are commonly interdependent due to strong coupling between the machine and the product. In this paper, three joint optimization models for preventive maintenance (PM) and product quality improvement are developed for deteriorating manufacturing systems considering quality-reliability dependency. First, the degradation process of quality-related components (QRCs) is depicted by Gamma process, and product quality deviation is obtained by considering the quantitative relationship between the degradation of QRCs and product quality. Second, joint optimization policy of age-based maintenance and product quality improvement is proposed for deteriorating manufacturing systems with quality-reliability dependency. Third, above maintenance policy is extended to manufacturing systems processing jobs with random processing time, and then maintenance first (MF) and maintenance last (ML) policies are developed, respectively. Furthermore, we compare all of the maintenance models to determine which policy should be selected for manufacturing systems under specific conditions, and all theoretical discussions in this paper are analytically made. Finally, the effectiveness of the proposed joint policies is verified by a case study on a cylindrical gear housing machining process.

Maintenance Costs Optimization Of Light Rail Transit (LRT) Facilities And Infrastructure Based On Life Cycle Cost Analysis (Case Study: South Sumatra LRT)

The construction of the South Sumatra Light Rail Transit (LRT) requires a huge development cost, besides that it has quite a large annual operational and maintenance cost. In order for South Sumatra LRT maintenance costs to run efficiently and effectively, it is necessary to optimize maintenance costs for South Sumatra LRT facilities and infrastructure based on Life Cycle Cost Analysis. Life Cycle Cost is an economic analysis method that refers to all costs associated with development, operating and maintaining a construction project over a certain period of time (Heralova, 2017). The research objectives were to analyze the physical condition of Facilities and Infrastructure South Sumatra LRT, evaluate South Sumatra LRT maintenance strategies and develop maintenance cost scenarios based on Life Cycle Cost. Based on data processing using the combined condition index method, the total value of the South Sumatra LRT infrastructure components reached 5,718. The physical condition of the South Sumatra LRT infrastructure reviewed is on a good scale with minor damage so that the option of preventive or time-based maintenance activities can be used. For the Life Cycle Cost of South Sumatra LRT infrastructure, the value obtained is IDR 2,080,482,508,541,480.

Analysis of Preventive Maintenance on Forklifts and Reach Trucks by Calculating Mean Time Between Failure and Mean Time to Repair at PT. DSV Solutions Indonesia

The aim of this research is to analyze preventive maintenance for material handling equipment, specifically Forklifts and reach trucks, at PT. DSV Solutions Indonesia over a one-year period (January-December 2023). The analysis employs the Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR), and Availability methods. The findings reveal that the Forklift has an MTBF of 35,926 minutes, MTTR of 85 minutes/breakdown, and an availability of 77.83%, while the reach truck has an MTBF of 26,865 minutes, MTTR of 95 minutes/breakdown, and an availability of 90.14%. Based on this analysis, a recommended preventive maintenance schedule is devised, suggesting maintenance every 25 working days for Forklifts and 19 working days for reach trucks. The evaluation of the improvement in preventive maintenance indicates that Forklifts require maintenance 12 times and reach trucks 16 times in a year, resulting in a significant reduction in downtime and repair costs, ultimately enhancing overall operational efficiency. In conclusion, the enhancement of preventive maintenance schedules proves beneficial in increasing the operational uptime of Forklifts and reach trucks within the operational environment over the course of one year.

RISC Fault of Non‐Salient Wound‐Rotor Synchronous Generator: A Detection Method and Effect Analysis of Insulation

Rotor interturn short circuit (RISC) fault is one of the most prevalent fault types in the non‐salient wound‐rotor synchronous generator (WRSG). In the paper, the theoretical mechanism of the RISC fault is studied in detail. Building upon this understanding, from the fault detection and harm consequences, the further work of this article includes proposing a RISC detection method and analyzing the influence of RISC on the insulation thermal damage. On the one hand, a new detection for RISC fault is first proposed based on the phase voltage. The proposed detection method innovatively allows for accurate determination of both the severity and location of RISC faults. On the other hand, the loss and the temperature of the stator core‐winding system are comprehensively investigated with varied RISC degrees and positions. The thermal responses of the winding insulation are obtained under the joint action of the external magnetic heat source and the internal electric heat source. The study identifies critical points of insulation vulnerability, particularly noting areas prone to noise and connection joints. This research contributes to the timely online identification of RISC faults and supports preventive maintenance strategies for protecting winding insulation. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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.

Crack Detection and Cost Analyzer Using AI

Crack Vision is an innovative AI-powered Android application developed to detect and analyze wall cracks, providing users with real-time, automated repair cost estimates. By leveraging advanced object detection models such as YOLOv7 and Mask R-CNN, Crack Vision enhances both the accuracy and efficiency of crack detection, enabling precise measurement of crack dimensions, severity assessment, and quick identification of structural issues. This application simplifies the inspection and budgeting process for wall repairs, making it an essential tool for homeowners, contractors, and structural engineers who seek to minimize reliance on manual inspection methods. The core functionality of Crack Vision combines high-resolution image processing with state-of-the-art machine learning algorithms, ensuring reliable detection across various wall types and crack textures. Using computer vision and deep learning techniques, the app can differentiate between structural and non-structural cracks, thereby offering a comprehensive solution for repair prioritization. Furthermore, Crack Vision integrates with a robust backend that stores user data, allowing users to track the condition of their walls over time. This feature aids in preventive maintenance by identifying trends in crack expansion and deterioration, potentially mitigating costly repairs. Additionally, the application provides a detailed breakdown of estimated repair costs based on crack size, depth, and type, empowering users to make informed decisions about necessary repairs. Crack Vision is designed with user-friendliness in mind, featuring an intuitive interface that guides users through the inspection process. The app also supports multilingual options and customizable settings to cater to diverse user needs. In sum, Crack Vision redefines wall crack inspection, offering a rapid, accurate, and accessible solution for both casual users and industry professionals, ultimately contributing to improved building safety and maintenance efficiency