Maintenance Strategy For Equipment Research Articles

Optimizing Container Crane Performance: Enhancing Loading and Unloading Productivity at PT. Kaltim Kariangau Terminal

Container terminals are critical nodes in global trade, where productivity hinges on the efficiency of handling equipment like container cranes. At PT. Kaltim Kariangau, a terminal jointly managed by PT. Pelindo and the East Kalimantan provincial government, operational challenges persist despite infrastructure enhancements. In 2023, an additional crane was introduced to the terminal, increasing the total to three. However, issues such as prolonged idle times and equipment malfunctions adversely impacted crane efficiency. This research evaluates crane performance using BCH (Box/Crane/Hour) and BSH (Box/Ship/Hour) metrics to assess their effect on loading and unloading productivity. The analysis reveals that in 2023, the average crane performance was 27 boxes/hour, a level classified as good. Paradoxically, overall productivity declined post-crane addition compared to prior operations. Using regression analysis, the study identifies a strong positive correlation (R = 0.7316) between crane performance and terminal productivity, with a coefficient of determination (R²) indicating that crane efficiency accounts for 54% of productivity variations. The findings highlight that improved crane performance directly enhances operational output, where each unit increase in BCH corresponds to a 1.809-unit rise in productivity. Hypothesis testing confirms that crane performance significantly influences loading and unloading efficiency. These results underscore the need for effective equipment utilization and maintenance strategies to optimize terminal operations. The research concludes that while infrastructure upgrades are essential, addressing operational inefficiencies is critical to achieving sustainable productivity improvements.

Generation of maintenance and troubleshooting strategies for complex equipment based on Bayesian network reinforcement learning

To solve the problem of long decision-making time and high total cost of generated strategies in traditional heuristic maintenance and troubleshooting decision-making methods, a method for complex equipment maintenance and troubleshooting strategy generation based on Bayesian network combined with reinforcement learning is proposed . In order to better utilize the knowledge of complex equipment models, Bayesian network is used to express maintenance and troubleshooting knowledge, and in order to be closer to the actual situation of complex equipment, the failure probability analyzed according to the failure mode, impact and criticality is used as the prior probability of the Bayesian network after reasonable transformation; in order to generate maintenance and troubleshooting strategies using the decision-making process of reinforcement learning, a method for converting the maintenance and troubleshooting decision problem into a reinforcement learning problem is proposed ; in order to better solve the transformed reinforcement learning problem, the observation-repair action pair is introduced to reduce the problem scale, and the action mask is set to process the dynamic action space. The simulation verification results show that under the unified performance index , the proposed method obtains higher index values than the traditional method, which proves the effectiveness and superiority of the method.

Model-based tool wear detection and fault diagnosis for end mill in various cutting conditions

In recent developments in the field of manufacturing systems, there has been a growing emphasis on optimizing cutting conditions. These optimizations are primarily based on intricate parameters, such as the material removal rate (MRR), surface roughness, and position accuracy. Simultaneously, there’s an increasing focus on enhancing manufacturing efficiency through equipment maintenance strategies that consider parameters, such as corrosion, pressure, temperature, vibration, and other environmental factors. Wear is inevitable during processing, which affects productivity. It is generated in various forms, such as flank, crater, and edge wear, which reduce the tool lifespan and impact machining quality, especially by increasing the cutting forces. Various studies have been conducted to address this issue. Direct measurements using microscopes have high accuracy but require interruption during the process, which adversely affects efficiency and productivity. As a solution, the modern era has witnessed an increase in indirect methods. These methods are often sensor-based, capture data during the machining process, and employ various models, including emerging artificial intelligence techniques, for predicting tool wear. However, these methods have problems with environmental susceptibility, reduced reliability, limitations of application, and excessive costs. This paper suggests a tool wear integrated cutting load prediction model, tool wear detection, and fault diagnosis mechanism. The tool-wear-integrated cutting-load prediction model was constructed by combining the cutting-load prediction and tool-wear models. The coefficients of the model were derived from the actual cutting data extracted by the spindle load. Tool wear detection was implemented by dividing regions based on the tendency of the coefficient of the constructed tool wear integrated cutting load prediction model and the errors between the predicted and actual values. The proposed model demonstrated a performance comparable to that of the existing models in a single-cutting-condition path. However, it excelled in extracting the tool wear coefficients in paths with a mixture of various cutting conditions, which was not achievable with conventional models. Based on these coefficients, the cutting force was predicted with a maximum error of 3.3 %. Also, an accurate determination of the tool-wear regions was possible. Furthermore, the performance of the tool fault diagnosis method was validated using images of tools identified as being at risk of damage.

Digital Twin-driven Health Operation and Maintenance Strategies for Complex Equipment with Self-healing Capabilities

With the development of the industrial level, the service-oriented transformation of the manufacturing industry has become a new growth point for the interests of the manufacturing industry recognized by all countries, and in this transformation process, the health operation and maintenance (HOM) of equipment has become a crucial link, especially for complex equipment, and its health operation and maintenance level directly affects the overall efficiency of the manufacturing industry. However, with the improvement of intelligence and complexity, a more general and complete set of methods and theories is needed for the healthy operation and maintenance of complex devices with self-healing states. Based on an in-depth analysis of the key issues of complex equipment health operation and maintenance, this paper introduces digital twin technology as a breakthrough. Digital twins provide new ideas for the healthy operation and maintenance of complex equipment. By defining a digital twin-driven PHM (Prediction and Health Management) framework, this paper not only solves the problem of mobility (i.e., virtual and real connection) of the cross-platform health operation and maintenance model, but also focuses on improving the accuracy and evaluation indicators of the model (the consistency between the virtual model and the entity). Addressing issues of strategy development and effectiveness evaluation. Based on the digital twin technology, a general model was established, and the self-healing phenomenon and different maintenance strategies were introduced to explore its impact on reliability. Based on the game idea, the reliability model is used to evaluate different maintenance strategies. So as to develop the optimal operation and maintenance strategy. This not only improves the accuracy and efficiency of O&M, but also provides a solid theoretical foundation and technical support for the intelligent and autonomous health O&M of complex equipment.

Research on Equipment Reliability Modeling and Periodic Maintenance Strategies in Dynamic Environment

Equipment along the railway has different degradation paths and reliability in different environments. It is of great significance to study the reliability and maintenance strategy of equipment in dynamic environment to ensure the safety of train operation. In this paper, a method of equipment reliability modeling and periodic maintenance decision-making in dynamic environment is proposed. A reliability model considering the degradation and impact of environmental changes and considering the characteristics of the equipment is established. A new concept of maintenance cycle is defined, and a decision model of equipment periodic maintenance is established. Finally, through numerical simulation, it is concluded that under severe environment, the maintenance time is shortened by 29508h and 31639h respectively, and the maintenance cost is increased by 41.4% and 28.2% respectively. Furthermore, the change trend and adjustment scheme of the strategy under different operating environments are analyzed, which verifies the correctness and effectiveness of the model in this paper.

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.

Joint risk-based maintenance strategy and integrated monitoring approach for manufacturing system operational process considering workpiece quality

The fluctuation of various parameters in the production process increases the risk of generating unqualified products and suffering from systematic failures and downtimes, and the preventive maintenance and control of potentially faulty machines in manufacturing systems should consider the continuous increase of operational risk. Therefore, to proactive control the manufacturing system operational risk, a joint model of risk-based maintenance (RBM) and integrated monitoring is proposed. The proposed model formulates an appropriate maintenance strategy and appropriate control charts for monitoring the manufacturing process, guaranteeing the stable operation of the manufacturing system. First, considering the co-effect of the production equipment state and workpiece quality, a novel connotation of the manufacturing system mechanism’s operational risk is proposed, and the RBM framework is constructed. Second, considering the manufacturing system operational state, an integrated maintenance decision-making model based on different risk scenarios is established using a dynamic monitoring scheme based on EWMA control charts. Third, a particle swarm optimisation algorithm is adopted to optimise the proposed model to obtain the optimal maintenance strategy for production equipment and monitoring parameter for workpiece quality. Finally, the effectiveness of the proposed approach is illustrated with a case study on a subway current receiver production line.

Optimization of joint preventive maintenance strategy for two-dimensional warranty equipment

Optimization of joint preventive maintenance strategy for two-dimensional warranty equipment

Research and application of power equipment monitoring and fault diagnosis

Power equipment monitoring and fault diagnosis are important parts of power system operation and maintenance. By monitoring the status and performance parameters of power equipment in real time, and timely discovering and diagnosing potential fault problems, the reliability, safety and economy of the power system can be improved. At present, this research has made significant progress, but there are still some research gaps that need to be further explored and resolved. Through the research and application of power equipment monitoring and fault diagnosis, this paper aims to improve the reliability, safety and economy of power grid, reduce the risk of failure, optimize equipment operation and maintenance strategies, and improve power supply quality and user satisfaction. Condition monitoring and fault diagnosis are very important for the normal operation of the power system equipment, which can not only strengthen the fault diagnosis of the power system equipment, but also avoid the problem of large-scale paralysis caused by the failure of the power system equipment.

Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM.

Ensuring precise prediction of the remaining useful life (RUL) for bearings in rolling machinery is crucial for preventing sudden machine failures and optimizing equipment maintenance strategies. Since the significant interference encountered in real industrial environments and the high complexity of the machining process, accurate and robust RUL prediction of rolling bearings is of tremendous research importance. Hence, a novel RUL prediction model called CNN-VAE-MBiLSTM is proposed in this paper by integrating advantages of convolutional neural network (CNN), variational autoencoder (VAE), and multiple bi-directional long short-term memory (MBiLSTM). The proposed approach includes a CNN-VAE model and a MBiLSTM model. The CNN-VAE model performs well for automatically extracting low-dimensional features from time-frequency spectrum of multi-axis signals, which simplifies the construction of features and minimizes the subjective bias of designers. Based on these features, the MBiLSTM model achieves a commendable performance in the prediction of RUL for bearings, which independently captures sequential characteristics of features in each axis and further obtains differences among multi-axis features. The performance of the proposed approach is validated through an industrial case, and the result indicates that it exhibits a higher accuracy and a better anti-noise capacity in RUL predictions than comparable methods.

Risk-based approach to choosing the production asset management strategy for energy company

RELEVANCE: The possibility of a reasonable choice of an alternative strategy for the organization of maintenance and repair (M&R) of the power company's equipment is a guarantee of the successful implementation of its strategic priorities. The tasks in this direction are quite relevant.PURPOSE: To develop a unified methodology that, from the general standpoint of a risk-based approach, would allow comparing two alternative strategies for managing the assets of an energy company "by periodicity, operating time" and "by technical condition", according to a set of key criteria for the effectiveness of M&R.METHODS: When solving problems, methods of forming and adjusting schedules for repairs of electrical equipment, calculating reliability and probabilities of failure in the power supply system of consumers, taking into account its circuit features, assessing the technical condition of equipment according to non-destructive testing and diagnostics, comparing the costs of maintaining and restoring reliability and damages from its violations were used.RESULTS: A risk-oriented methodology has been developed and verified, which makes it possible to evaluate and select the optimal equipment maintenance strategy for the energy company according to uniform efficiency criteria. An algorithmic implementation of the developed methodology is obtained, which is the basis of a computing complex for choosing and making decisions. The verification of the methodology and algorithm is carried out on the example of a real object – a power supply system for oil production consumers using reliable operational and diagnostic information.CONCLUSION: The results obtained fully compensate for the lack of methodological possibilities in comparing two basic alternative strategies for electrical equipment maintenance based on a single risk-based approach. The developed methodology is a unified decision-making tool at the stage of alternative analysis. The illustration of the methodology by a real numerical example demonstrates the consistency of the solutions obtained with the results of expert assessments and operational experience.

Optimization of Preventive Maintenance Strategies for Electrical Equipment on Offshore Oil Support Vessels Based on Predictive Maintenance Algorithms in an Intelligent Platform

Optimization of Preventive Maintenance Strategies for Electrical Equipment on Offshore Oil Support Vessels Based on Predictive Maintenance Algorithms in an Intelligent Platform

Analysis of Equipment Management Methods for Pumped Storage Power Stations Under the “Dual-Carbon” Goals

Pumped-storage, as the most mature technology, economically optimal, and most suitable for largescale development, plays a crucial role in promoting the consumption of clean energy and supporting the construction of new energy systems. Pumped-storage power stations involve various types of equipment such as hydraulic and electrical devices. The frequent start-stop operation in the context of new energy system construction will pose significant challenges to the equipment. This paper proposes a reliability-centered health management method for pumped-storage equipment. It implements differentiated and hierarchical management strategies for various types of equipment and establishes differentiated equipment maintenance strategies based on this.The project takes the manhole door of a power station governor pressure oil tank with nitrile rubber sealing ring as an example to calculate the service life in actual working conditions. Through calculation, it is considered that when reliability is 99%, the service life can be extended for more than 5 years, and if the reliability is 100%, the service life can be extended for more than 1 year.

Innovation and Application of Reliability-Centered Maintenance Technology for Pumped Storage Power Plant

With the increasing capacity and scale of pumped storage power plant, the maintenance management and resource input of system equipment are continuously increasing. Exploring and establishing a system engineering method that balances safe production and maintenance resource input has become an important issue that enterprises urgently need to solve. Based on the reliability-centered maintenance (RCM) technology, this paper combines the characteristics of pumped storage unit operation and maintenance management and years of maintenance practice experience to improve and innovate the RCM technology method, forming a “reliability-centered pumped storage unit maintenance strategy optimization technology method” applicable to pumped storage power generation units. This technology method has been applied on the system equipment of pumped storage power generation unit. It shows the application of this technology method has made significant contribution to optimizing equipment maintenance strategies, reducing maintenance workload and the overhaul time.

Operation and maintenance strategy of radio transmission equipment based on big data

AbstractIn recent years, the operation and maintenance of radio transmission equipment have faced challenges such as equipment aging, high maintenance costs, long response time, and technological updates. It is necessary to adapt to new technologies and improve maintenance efficiency. The operation and maintenance strategy of radio transmitting equipment based on big data (BD) aims to optimize the reliability and operating efficiency of equipment through data analysis and intelligent algorithms, and reduce failure risk and maintenance costs. By analyzing the operation data and maintenance records of a large number of radio transmission equipment, it revealed the patterns and trends of equipment failures, in order to timely identify and predict possible faults, and take corresponding maintenance measures. This article adopted methods such as data collection and preprocessing, fault prediction and diagnosis, and maintenance optimization to achieve accurate monitoring of equipment operation status, fault prediction and diagnosis, optimized maintenance, and decision support. This can improve the operational efficiency and reliability of radio transmission equipment, which is used for fault diagnosis of radio transmission equipment through SVM (Support Vector Machine) algorithms. By training SVM classifiers, this article associated different types of faults with their feature data, thereby quickly and accurately determining the type of equipment fault based on real‐time collected device data, providing a basis for maintenance decision‐making. The experimental results show that the failure frequency of the radio transmitting equipment in the control group was between 7% and 11%, while the failure frequency of the radio transmitting equipment in the experimental group was between 3% and 7%. Experiments show that BD technology can effectively reduce the failure frequency of radio transmitting equipment. The empirical results indicate that this strategy can improve the reliability of equipment and reduce operation and maintenance costs, which has important guiding significance for the operation and maintenance management of radio equipment. This would bring about more efficient and reliable operation and maintenance management of radio equipment, making important contributions to the smooth progress of broadcasting business and the improvement of user experience.

Nonlinear characterization of enhanced and generalized Hjorth’s feature space for bearing condition monitoring

The degradation assessment of rolling bearings provides a reasonable maintenance plan for the safe operation of mechanical equipment. The general strategy for bearing condition monitoring is to construct a health indicator (HI) to characterize different degradation stages. A preferable HI that can sensitively detect initial faults and track machine degradation is crucial to developing timely maintenance strategies for mechanical equipment to avoid catastrophic accidents. However, many developed and reported HIs are still insensitive to early faults, resulting in delayed maintenance schedules. To identify the incipient defects as early as possible, a novel HI constructed by nonlinear characterization of enhanced and generalized Hjorth’s feature space based on extended probability entropy is proposed in this paper. Firstly, the time-frequency spectral amplitude modulation helps to enhance the characteristics of the original signal with the amplitude editing in the time-frequency domain. Then, three new features of generalized Hjorth’s parameter combinations are designed and combined with other similar feature combinations to construct a high-dimensional enhanced and generalized Hjorth’s feature space. On this basis, a set of low-dimensional sensitive features is obtained by nonlinearly characterizing high-dimensional features through extended probability entropy after these features are standardized. Finally, a novel HI is developed by calculating the distance between the minimum volume ellipse (MVE) center of the low-dimensional feature subspace based on nonlinear characterization and the low-dimensional feature vector of the real-time monitoring signal. The performance of the proposed approach is verified in three cases, whose experimental results indicate that the proposed HI is more sensitive and significant in detecting early faults compared to some current HIs.

Multi-phase preventive maintenance strategy for leased equipment considering usage rate variation

Lessors are responsible for equipment maintenance during the lease period. The usage rate of these equipment affects their failures, reliability and decision-making on maintenance strategy. For some leased equipment, such as those producing seasonal products, the usage rate varies. However, conventional approaches usually set usage rate as a constant. Different from the existing literature, this work considers the variation of usage rate, and proposes a multi-phase preventive maintenance (PM) decision model to determine the PM times and degrees at different phases. The goal is to minimize lessors’ lease service cost by balancing corrective maintenance (CM) cost, PM cost and penalty cost during the lease period. In this model, an age correspondence framework is applied to characterize the variation of usage rate at different phases. An accelerated failure time model and an age reduction model are adopted to capture the effect of variable usage rate and imperfect PM on the equipment’s reliability. To verify the effectiveness of the proposed model, it is contrasted with a PM decision model with average usage rate. The impact of unit PM cost, CM cost, and penalty cost on maintenance strategy is discussed by numerical examples. Managerial insights for maintenance strategy are provided for lessors. The results indicate that the proposed approach considering varied usage rate can change the optimal PM times and degrees, and consequently lead to a lower lease service cost than conventional approaches using average usage rate; lessors need to enhance PM capability and reduce PM cost.

Joint Optimization of Maintenance and Spare Parts Inventory Strategies for Emergency Engineering Equipment Considering Demand Priorities

To respond to emergencies in a timely manner, emergency engineering equipment has been an important tool to implement emergency strategies. However, random failures of the equipment may occur during operation. Therefore, appropriate maintenance and spare parts inventory strategies are crucial to ensure the smooth operation of the equipment. Furthermore, the urgency degree of emergencies varies in practice. Nevertheless, existing studies rarely consider the impact of urgency degree and demand priorities on the service order of the equipment. To bridge the research gaps, this paper establishes a joint optimization model of maintenance and spare parts inventory strategies for emergency engineering equipment considering demand priorities. The proposed model includes two types of emergency engineering equipment with different service rates. The more urgent demand can be fulfilled by the equipment with a higher priority. Corrective maintenance and spare parts inventory policies are simultaneously performed for the equipment. The Markov process imbedding method is utilized to derive the probabilistic indexes of the system. To maximize the system availability, the number of maintenance engineers and the spare parts inventory strategy is optimized via the construction of the joint optimization model. The optimal solution for the optimization problem is obtained using the branch-and-bound method. Finally, this study presents practical examples to verify the effectiveness of the model and methods.

Exploration of mechanical engineering equipment management and maintenance strategies

The management and maintenance of mechanical engineering equipment is critical to the proper operation and productivity of a business. The purpose of this paper is to discuss strategies for the management and maintenance of mechanical engineering equipment, including aspects such as preventive maintenance, breakdown repair and performance monitoring. Through reasonable management and maintenance methods, the reliability and extended service life of equipment can be improved, failure rates and maintenance costs can be reduced, production efficiency can be improved and sustainable development of enterprises can be supported.

Predictive Maintenance Strategies for Electrical Equipment: A Literature Review

This research paper offered a broad literature review explores predictive maintenance strategies for electrical equipment, encompassing methodologies, applications, and challenges. Given the critical role of electrical systems in modern society, the transition from reactive to proactive maintenance is crucial for ensuring reliability and efficiency. The review systematically categorizes and analyzes a range of predictive techniques, from vibration analysis to machine learning, emphasizing the integration of data-driven approaches driven by sensor technology and computational advancements. Highlighting real-world applications across industries like power generation and manufacturing, the review underscores the tangible benefits of optimized maintenance schedules and reduced costs. While challenges persist, such as data quality and model complexity, the review underscores the need for interdisciplinary collaboration and innovative solutions. Ultimately, the review contributes to advancing predictive maintenance, envisioning a future where operational excellence is achieved through anticipation and prevention of equipment failures