System Maintenance Costs Research Articles

Hardware Failure Prediction Modes Powered by Artificial Intelligence

With the increase of the system scale and complexity of modern computer systems and electronic equipment, the physical components (hardware) in computer or electronic equipment are damaged, the performance is reduced or the normal operation of computer and electronic systems is greatly increased due to various reasons. Detecting and predicting hardware faults in time is the key to ensure system stability and reliability. Hardware failure prediction technology based on artificial intelligence (AI) can effectively predict potential hardware failures by analyzing historical data, monitoring equipment operating status in real time, and reducing system downtime and maintenance costs. This paper reviews the current hardware fault prediction methods based on artificial intelligence, discusses the common algorithms, technical challenges, and application scenarios, and looks forward to the future development direction of this field.

Improving the Economic Feasibility of Small-Scale Biogas-Solid Oxide Fuel Cell Energy Systems through a Local Ugandan Biochar Production Method

A small-scale (up to 5 kWe) biogas-solid oxide fuel cell (SOFC) energy system is an envisioned system, which can be used to meet both electrical and thermal energy demand of off-grid settlements. SOFC systems are reported to be more efficient than alternatives like internal combustion engines (ICE). In addition to energy recovery, implementation of biogas-SOFC systems can enhance sanitation among these settlements. However, the capital investment costs and the operation and maintenance costs of a biogas-SOFC energy system are currently higher than the existing alternatives. From previous works, H2S removal by biochar was proposed as a potential local cost-effective alternative. This research demonstrates the techno-economic potential of locally produced biochars made from cow manure, jackfruit leaves, and jack fruit branches in rural Uganda for purifying the biogas prior to SOFC use. Results revealed that the use of biochar from cow manure and jack fruit leaves can reduce H2S to below the desired 1 ppm and substitute alternative biogas treatments like activated carbon. These experimental results were then translated to demonstrate how this biochar would improve the economic feasibility for the implementation of biogas-SOFC systems. It is likely that the operation and maintenance cost of a biogas-SOFC energy system can in the long run be reduced by over 80%. Also, the use of internal reforming as opposed to external reforming can greatly reduce the system capital cost by over 25% and hence further increase the chances of system economic feasibility. By applying the proposed cost reduction strategies coupled with subsidies such as tax reduction or exemption, the biogas-SOFC energy system could become economically competitive with the already existing technologies for off-grid electricity generation, like solar photovoltaic systems.

Nanosecond laser cleaning method for high-capacity fast storage and data multiplexing in ARM architecture

As the application of laser cleaning technology continues to expand, various fields have put forward higher requirements for the performance and quality of the operation, and at present, laser cleaning has problems such as low efficiency in handling repetitive tasks, and the system deployment and maintenance costs are high. This paper proposes a nanosecond laser cleaning method based on ARM architecture with high-capacity fast storage and data reuse, integrating high-capacity fast storage technique and data reuse strategy to optimize the laser cleaning process. In this paper, SDRAM is used to store and record the laser scanning path, Cache is integrated to weaken the data calling delay and improve the system response speed, and the scanning cycle is finely adjusted by combining positional modulation laser frequency technology to avoid the edge erosion caused by the motor deceleration and to ensure the homogeneity and accuracy of laser cleaning. The results indicate that, under the same output point conditions, the dynamic phase difference spiral algorithm in this paper outperforms the linear filling algorithm in terms of speed and data filling efficiency, reducing the burden of repetitive task calculations. While maintaining surface erosion uniformity during cleaning, excessive erosion is prevented, leading to a comprehensive enhancement in the overall efficiency and performance of laser cleaning operations.

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.

Integration and Optimization of a Waste Heat Driven Organic Rankine Cycle for Power Generation in Wastewater Treatment Plants

The study focuses on achieving energy self-sufficiency in Wastewater Treatment Plants by proposing a comprehensive model for integrating, sizing, and optimizing an Organic Rankine Cycle system. The Organic Rankine Cycle system is designed to utilize waste heat from the gensets at As Samra Wastewater Treatment Plant in Jordan, where it will contribute to the overall electrical energy supply of the plant. Real data from As Samra Wastewater Treatment Plant is used to model and calculate the available waste heat using TRNSYS® software. The Organic Rankine Cycle model is then developed using ASPEN PLUS® software to explore the impact of operational parameters and determine their optimal values for maximizing the plant's energy profile. An economic analysis is conducted to assess the feasibility of the proposed model, considering system components, installation, operation, and maintenance costs. To optimize the Organic Rankine Cycle system, the study employs the Multi-Output Support Vector Regression technique to capture nonlinear relationships between independent variables (fluid type, turbine inlet pressure, turbine inlet temperature, turbine outlet pressure, and mass flow rate) and dependent variables (pump power input, waste heat input, and turbine specific work). The Osprey optimization algorithm is used to address the multi-objective optimization problem, with the proposed Pareto-based Osprey Optimization Algorithm and the Multi-Objective Particle Swarm Optimization technique being employed to evaluate critical performance and economic parameters such as system thermal efficiency, net power output, and the levelized cost of electricity. The results of the optimization strategies indicate that the M-SVR model's prediction accuracy is significantly improved after parameter optimization, with the model returning high R2 and low Mean Square Error values of 0.991 and 0.00216, respectively. The Pareto-Based Osprey Optimization Algorithm optimizer identifies the best working fluid as Isobutane/Isopentane in a ratio of 66:34, with optimal turbine inlet pressure and temperature of 15 bars and 218 °C, respectively. The Organic Rankine Cycle model at these optimal conditions achieves a cycle efficiency of 19.93 % and an Levelized Cost of Electricity value of 0.0353 USD/kWh.

Co-optimization of the operation and energy for AGVs considering battery-swapping in automated container terminals

Since the number of containers in automated container terminals (ACTs) has been growing, the operation of ACT relies on a large amount of electric power, which comes from the terminal energy system. As the main horizontal transportation equipment in ACTs, the scheduling of automated guided vehicles (AGVs) has become increasingly important. Even though AGV scheduling has been studied, the battery-swapping procedure is often overlooked, which hinders operation efficiency and the usage of renewable energy. We propose a co-optimization problem of the operation and energy for AGVs considering battery-swapping in this paper. A multi-objective model is constructed to minimize the makespan of AGVs and the operation and maintenance cost of energy system. In addition, we create a discrete multi-objective whale optimization algorithm to address this co-optimization problem. A load-balancing-based method for population initialization is developed to make the distribution of solutions more uniform in this algorithm. With the two-dimensional matrix encoding scheme, an individual right-shifted method is proposed for decoding. The best solution is assessed and selected through a fitness evaluation mechanism based on fuzzy correlation entropy. The bubble-net attack based on opposition-based learning is implemented to enhance the capacity for local intensification. Numerous tests confirm that the proposed method can guarantee the operation efficiency, lower the total cost of the energy system, and promote sustainable development in ACTs.

ПУТИ ПОВЫШЕНИЯ ЭФФЕКТИВНОСТИ ЭКСПЛУАТАЦИИ ТРАДИЦИОН-НЫХ И ГИБРИДНЫХ РАСПРЕДЕЛИТЕЛЬНЫХ СЕТЕЙ

The main goals, development methodology and existing problems in distribution networks are con-sidered. It has been established that a promising direction for the development of electrical networks is the introduction of hybrid DC-AC networks, which combine the advantages of AC and DC net-works. Hybrid networks are promising, and already at the design stage, “smart” networks can be cre-ated on their basis, which can facilitate the work of the power system dispatcher, reduce personnel and maintenance costs

Optimal Placement and Capacity of BESS and PV in EV Integrated Distribution Systems: The Tenth Feeder of Phitsanulok Substation Case Study

Installing a battery energy storage system (BESS) and renewable energy sources can significantly improve distribution network performance in several aspects, especially in electric vehicle (EV)-integrated systems because of high load demands. With the high costs of the BESS and PV, optimal placement and capacity of them must be carefully considered. This work proposes a solution for determining the optimal placement and capacity of a BESS and photovoltaic (PV) in a distribution system by considering EV penetrations. The objective function is to reduce system costs, comprising installation, replacement, and operation and maintenance costs of the BESS and PV. The replacement cost is considered over 20 years, and the maintenance and operation costs incurred in the distribution system include transmission line loss, voltage regulation, and peak demand costs. To solve the problem, two metaheuristic algorithms consisting of particle swarm optimization (PSO) and the African vulture optimization algorithm (AVOA) are utilized. The tenth feeder of Phitsanulok substation 1 (PLA10), Thailand, which is a 91-bus distribution network, is tested to evaluate the performance of the proposed approach. The results obtained from the considered algorithms are compared based on distribution system performance enhancement, payback period, and statistical analysis. It is found from the simulation results that the installation of the BESS and PV could significantly minimize system cost, improve the voltage profile, reduce transmission line loss, and decrease peak demand. The voltage deviation could be reduced by 86%, line loss was reduced by 0.78 MW, and peak demand could be decreased by 5.706 MW compared to the case without BESS and PV installations.

Research on the capacity allocation of composite energy storage systems for hybrid ships

Abstract Aiming at the problem of unstable output power fluctuation of the dredger during operation, a composite energy storage system consisting of lithium iron phosphate battery and supercapacitor is introduced to smooth out the power fluctuation. Considering the configuration cost, operation and maintenance cost of the energy storage system, the capacity optimization model of the composite energy storage system is established with the average daily cost as the objective function, and with the system power balance, the charge state of the energy storage system, and the rated power as the constraints. The optimal capacity is solved by using the modified artificial fish swarm algorithm (MAFSA). The final results show that MAFSA reduces the capacity of the battery and the supercapacitor by 6.9% and 9.9%, respectively, and reduces the average daily cost of the system by 13.5%. Meanwhile, the capacity-optimized energy storage system enables the generator set to work in the optimal operating range, which reduces its output power fluctuation, improves its efficiency, and reduces fuel consumption and pollutant emission.

A Review on Crack Detection in Wind Turbine Blade

Abstract: It was observed that there is a need to reduce of maintenance and operational cost of Wind Energy conversion System. The most efficient way of reducing this cost would be to carry out Non-destructive techniques (NDT) and continuously monitor the condition of the system. This allows for early detection of the blade Cracking or damage, minimizing downtime, and maximizing productivity. The importance of condition monitoring and fault diagnosis in WECS (blades) and keeping in mind the need for future research, this paper is intended as a brief status describing different type of faults, methods of detection and future aspects

Coupling and Quantifying Sustainability and Resilience in Intelligent Buildings

Over recent years, the sustainability and resilience concept has increased its significance in the construction industry. Sustainability is associated with implementing best practices in the construction industry, while resilience is the adaptability and tolerance of systems in harsh conditions. The concepts are learned in the construction process. Moreover, building automation is growing rapidly, and buildings are becoming increasingly dependent on complex systems and technology and susceptible to unanticipated failure. Though sustainability and resilience concepts are interlinked, limited research quantifies their combination, resulting in a limited comprehension of how both concepts interact during application by developers in a smart building. Therefore, this study has established a financial model that employs Net Present Value (NPV) in studying the inference and clampdown of investment in both concepts. NPV was estimated using indirect and direct costs and benefits derived from the continuous integration of sustainability and resilience in a smart building. To quantify sustainability, its three components had to be quantified. Reduced energy expenditure and government environmental incentives were used to calculate the environmental component. Workers’ cost savings, fire insurance cost savings, and additional system maintenance costs were used to calculate the economic component. The social component of sustainability measured hard-to-quantify attributes like productivity, indoor environment quality, reputation, extra profit, services, and safety. To quantify them, a survey and RII method were used. The two concepts were then coupled by estimating the benefits and costs of installing and keeping resilience tools in design that are sustainable in the smart building and the impact study on the NPV outcome. Application of the design model was also carried out on four smart buildings that were selected in Dubai. The result indicated that coupling sustainable approaches and resilience yields higher NPV by at least 22%. Nevertheless, for NPV to be maintained positively and reduce the cost of failure, faulty detection tools should be assimilated while designing sustainable and smart buildings. The findings of this study will contribute to the benefit of other researchers, developers, investors, managers, engineers, and anyone who is involved in the design or construction process of intelligent buildings.

State of the Art of Modelling and Design Approaches for Ejectors in Proton Exchange Membrane Fuel Cell

Proton exchange membrane fuel cell (PEMFC) has a promising future in the power generation and transportation fields. Recirculation of unused anodic gases is fundamental to achieve a high-performance energy system, and this is usually attained employing ejectors or pumps. With respect to the latter, ejectors present no moving parts, thus resulting in both higher overall efficiency of the system and lower maintenance cost. Their main drawback is represented by the narrow optimal operative range: the entrainment ratio (ER) greatly depends on primary pressure, working pressure, and operative condition in general. In the last decade, numerous authors focused their efforts on fully comprehending and correctly simulating their working principles and analyzing how geometrical parameters influence ER and design different geometries to enlarge the operative range. The aim of this paper is to present in an ordered and clear manner the state of the art of ejector design, both from simulative (turbulence model, single or multiphase stream, etc.) and empirical (commonly used “rule of thumb”) points of view.

Random-time component reallocation and system replacement policy with minimal repair

The maintenance policies based on random working time guarantee the continuity of system functionality. This paper studies a random-time component reallocation and system replacement (RCS) policy, in which the random-time system replacement is to replace the system at the planned preventive time or the random working time, whichever comes first, the random-time component reallocation is to balance the degradation of components by reallocating components at random working time before system replacement, and minimal repair is implemented for failed components. To improve the efficiency and flexibility of RCS policy, we first establish an optimization model to minimize the expected system maintenance cost per unit time based on a given interval for random-time component reallocation, and analytical results are derived by comparing the proposed policy with the preventive component reallocation and random-time system replacement policy, and the simple random-time system replacement policy. Next, we construct another optimization model to maximize the interval length for the random-time component reallocation constrained by the threshold of the efficiency level of RCS policy. Numerical experiments demonstrate the performance of the RCS policy and provide guidance for the interval for the random-time component reallocation.

Study on the Alkali-Free Three-Component Flooding System in the Daqing Oilfield.

ASP flooding is an important method to further improve oil recovery by a large margin. At present, it has entered the stage of industrial application, but there are still problems of scaling in the injection production system and high production maintenance costs. Based on the industrialized mature technology of weak alkali ASP flooding, sodium chloride is used to replace sodium carbonate, and the alkali-free three-component flooding (TC) system in the Daqing Oilfield is developed by mixing with petroleum sulfonate and partially hydrolyzed polyacrylamide. Based on the experiments of viscosity increasing, interface performance, stability, adsorption, and oil displacement effect, the differences between the alkali-free TC system and the weak alkali ASP system are compared and analyzed. The laboratory research results show that both systems are basically the same in terms of viscosity, viscoelasticity, shear resistance, interfacial activity, stability, and flowability. Due to the lack of alkaline water, the adsorption, emulsification, and oil displacement performance of the alkali-free TC system is slightly lower than that of the weak alkali ASP system. The recovery factor of core flooding can be increased by 27.31% over water flooding, which is 2.56 percentage points lower than that of the weak alkali ASP system. On the premise of the same 1% EOR effect, the agent cost of the alkali-free system is 17.02% lower than that of the alkali ASP system. This article innovatively verifies the feasibility of using NaCl instead of Na2CO3 and explains the mechanism of significantly improving oil recovery in composite systems under alkali-free conditions from the ion level. However, the emulsification effect of the alkali-free TC system is relatively weak. The next step of research would be to consider adding an E-surfactant to enhance the emulsification performance of the composite system. By improving the system composition, technical references are provided for the efficient development of other terrestrial sandstone oilfields.

Bi-level corrected residual life-based maintenance for deteriorating systems under competing risks

The accurate prediction of residual life in deteriorating systems is a challenging task due to uncertainties and idealized assumptions during data acquisition and model construction. To address this challenge, the paper proposes a bi-level maintenance model that incorporates a corrected residual life (CRL) approach for a deteriorating system under competing risks. Specifically, the degradation failure is modeled using a Gamma process, while the competing sudden failure is described by a proportional hazards (PH) model. To reduce inconsistencies in residual life prediction, a CRL model is presented and continuously updated with a dynamic corrective factor at each monitoring epoch. Based on the updated CRL, a bi-level maintenance model is developed, which considers both system availability and maintenance cost objectives, enabling dynamic monitoring of the system's degradation state and residual life. The optimal decision variables in the maintenance model are determined through a multi-objective optimization algorithm formulated within a semi-Markov decision process (SMDP) framework. The unique aspect of this work lies in the consideration of prediction errors in residual life and the incorporation of multi-attribute optimization for maintenance design. The proposed method is validated through a case study on light-emitting diodes, confirming its effectiveness in improving residual life prediction and optimizing maintenance decisions.

Research on modeling and control of PEMFC cooling system

The structure of the stack cooling system has been redesigned to combine the high heat dissipation efficiency of the large-small cycle system and the low maintenance cost of the internal-external cycle system. Four temperature control strategies for the cooling system were proposed and tested on the stack test platform. The results show that the strategy of controlling the pump flow rate through feedforward with flow following current and Model Predictive Control(MPC) algorithm feedback, and controlling the opening of the radiator fan through Proportion Integration Differentiation(PID) algorithm feedback, is the most effective. This strategy can control the maximum temperature difference between the coolant inlet and outlet at approximately 1.14 °C. It also allows for quicker adjustment of the coolant temperature to the target value when the load changes, with an adjustment time reduced to about 9 s and a steady state error lower than 0.51 °C.

Optimization of safety instrumented system performance and maintenance costs in Algerian oil and gas facilities

Gas processing industry is associated with high risks which have the potential to cause catastrophic accidents. Safety Instrumented Systems (SISs) are considered the most effective safety barriers in this sector aiming to prevent undesired events and mitigate their consequences. However, several factors can affect their performance including maintenance strategy in place. In this paper, a Stochastic Petri Net (SPN) model is proposed for evaluating maintenance strategies related to SIS. The model is applied to analyze the performance of an Emergency Shutdown System (ESD) in a Flared Gases Recovery Unit located at south Algerian field. Furthermore, the paper investigates the financial impact of proof tests, including direct costs (such as manpower, equipment, and transportation) and indirect costs (such as production losses and gas flaring tax). These costs can be effectively managed and reduced by optimizing proof test intervals and scheduling tests during planned plant shutdowns. The results demonstrate that the proposed Stochastic Petri Net (SPN) model successfully analyzes the impact of imperfect full and partial proof tests on PFD average values of Safety Instrumented Functions (SIFs). In addition, it has been shown through reliability analysis that the proposed model is able to minimize spare parts expenses leading to significant cost savings while maintaining the required safety integrity levels (SILs), about 60% benefit achieved within two years compared to the actual procurement process over same period.

Joint component-system maintenance planning over entire system task profile

Engineering systems have become increasingly complex, which demands the study of system-level maintenance strategy to be integrated with a realistic prediction of component-level performance. Most previous research of system maintenance models has focused only on the relationship between component failure rate and maintenance cost. For a system with degrading components, however, it is more intuitive to model the system’s health state together with component degradation processes. In this paper we propose a preventive maintenance strategy based on the simulation of component degradation processes and lifetime predictions. In addition, the proposed strategy utilizes the maintenance opportunities between tasks assigned by system. Specifically, the unit-time system maintenance cost is minimized with the consideration of interactions between component-level and system-level decision-makings. The approach we propose is more practical due to the simulation of the component degradation processes according to the real task profile of the system and the initial maintenance schedule can be re-adjusted in the event of an unexpected component failure. Therefore, it ensures an overall low maintenance cost and high system reliability over time. A case study of unmanned aerial vehicle illustrates the proposed method.

Reliability analysis of floating offshore wind turbine generator based on failure prediction and preventive maintenance

Aiming at the failure problems of a floating offshore wind turbine (FOWT) generator, a preventive maintenance model based on failure prediction is established. Firstly, the fault tree method is adopted to calculate the generator reliability value under run or failure states. Secondly, quantitative analysis of the generator in a maintainable state is carried out by the Markov method. Finally, a system optimization model is established based on the failure prediction and preventive maintenance (FPPM) strategy. The single-objective teaching-learning-based optimization (TLBO) algorithm and multi-objective genetic algorithm (MOGA) are respectively used to solve the optimization problem. The results show that FPPM strategy can greatly improve the availability and mean time between failure (MTBF) value, which increase the system working time and efficiency. Multi-objective analysis can get the best combination of the system maintenance cost, availability, and risk than single-objective. The time interval of failure prediction is calculated as 19788 h and the system is in a safe state if the remaining life is predicted to be more than 101 h. Sensitivity analysis of the model is carried out to determine that the optimization objectives are less sensitive to variables, which verifies the rationality and accuracy of the proposed model.

Development of a cloud intelligent machine monitoring and control system

Facing the trend of Industry 4.0, the cloud-based supervisory control and data acquisition (SCADA) system employing cloud computing and IoT technology can help the manufacturing industry reduce software investment and system maintenance costs. However, manufacturers may need to install new sensors and controllers, the connection of SCADA system and shop floor machine controller, monitoring dashboard design and implementation usually need to outsource to an experienced system integration company, which may impede medium-sized manufacturing enterprises (SMEs). This paper aims to develop a cloud-based intelligent machine monitoring and control system (CIM-MCS) framework, the service structure, and approach to deploying the CIM-MCS in a public cloud infrastructure platform and service provider. The package diagram is proposed for building the CIM-MCS’s virtual factory model to improve modeling efficiency and data stability. CIM-MCS and its SCADA application in a leading automatic filling and packaging production line show that the CIM-MCS is easy to implement. The transmission time is short and acceptable for practical application. The integration of CIM-MCS with a cloud-based advanced planning scheduling system has the advantage of real-time monitoring, production progress reporting, scheduling, and dispatching and achieves the goal of anytime, anywhere, anyone, and any platform operating an intelligent factory.