Maintenance Intervals Research Articles

Cost-effective maintenance of safety and security barriers in the chemical process industries via genetic algorithm

Chemical plants face safety hazards and security threats that may induce catastrophic scenarios. Safety and security barriers are employed widely to protect chemical plants from accidental and intentional undesired events and mitigate consequences. Managing safety and security barriers effectively and economically is a research topic with practical significance. The analysis of undesired event scenarios, including both accidental and intentional adverse scenarios, and assessing associated safety and security barriers are critical regarding cost-efficient barrier maintenance. This study proposes a novel approach for optimizing safety and security barrier maintenance strategy considering economic constraints. This approach consists of three steps: scenario building and barrier identification, barrier modeling, and determining optimal barrier maintenance intervals. In the proposed approach, accident scenarios in terms of safety and physical security are constructed using the extended bow-tie diagrams. After associated safety and security barriers are identified, a system simulation model is developed to conduct barrier modeling based on MATLAB/Simulink simulations, in which the barrier maintenance, the impacts of human and organizational barriers, and the correlations between barriers caused by shared components are considered. Finally, a combination of cost-effectiveness analysis (CEA) and genetic algorithm (GA) is employed to support the decision-making on barrier maintenance optimization. An illustrative case is employed in this study to validate the feasibility of the proposed approach.

A mathematical programming–based heuristic for coordinated hydrothermal generator maintenance scheduling and long-term unit commitment

This paper studies the coordinated optimization between generator maintenance scheduling and long-term unit commitment in hydrothermal power systems, which can deal with the increased conflicts between these two tasks brought on by the extensive integration of cascade hydropower, but encounters difficulty in finding solutions when the system scale is large. A coordinated optimization model between hydrothermal generator maintenance scheduling and long-term unit commitment is proposed, in which the network security constraints and coupling characteristics between cascade hydro plants are considered. To accelerate the solution of this complex mixed-integer linear programming problem, an improved two-stage heuristic algorithm based on the objective scaling ensemble approach is proposed. The first stage is a detection process in which a maintenance interval detection strategy is used to probe the maintenance variables to be fixed. The second stage is a fix-and-solve process in which the reduced model is solved after fixing the maintenance variables detected at the first stage. Finally, simulations on a modified IEEE 30-bus and 6-unit hydrothermal power system and a real 1348-bus and 155-unit hydrothermal power system are conducted, focusing on the solution quality and the acceleration effect of the proposed algorithms, as well as the results of the proposed model. The numerical results reveal that the proposed algorithm can speed up the process of finding a near-optimal solution for large-scale systems through a detection strategy with high accuracy and efficiency. The proposed model can plan out feasible and coordinated maintenance scheduling and long-term unit commitment scheme simultaneously.

Substantiation of Passenger Elevators Maintenance Intervals Based on Studies of Modes and Conditions of Their Operation

Introduction. Maintenance is one of the factors of elevator systems safe operation. In regulatory documents, the influence of the loading mode of elevators on the frequency of maintenance is not taken into account and is assumed to be the same for objects operating in disparate conditions. This suggests the need to determine rational intervals between maintenance to ensure safe and economically feasible operation of the equipment in question. The work objective is to substantiate the required maintenance frequency of passenger elevators based on comprehensive assessments of their technical condition.Materials and Methods. The domestic and foreign experience of choosing the maintenance frequency of elevator units is considered. The paper evaluates the possibilities of substantiating the required maintenance intervals on the basis of established comprehensive assessments of technical condition, taking into account the conditions and operating modes. Since the problem has no direct analytical solution, an alternative approach is proposed. It is based on the hypothesis of the interrelation between the regulatory resource characteristics of assemblies and the modes and conditions of their operation. The authors obtained the necessary indicators through measurements in high-rise apartment buildings in Rostov-on-Don, experiments and adequate simulation modelling.Results. A structural and logical model has been developed to solve this problem. It consists of procedures that can be described in detail and clarified. A method for adjusting the maintenance frequency of elevators is proposed and tested. The standards of the system resource in terms of time and number of work cycles for the control time are summarized and tabulated. In this light, the gearbox, electric motor, brake, door drive and elevator ropes in 9-storey buildings are described at a cabin speed of ≤ 1 m/s. For example, the paper provides the calculated normative indicators:Kmcp норм — coefficient of net machine time (NMT);ncp норм — specific number of switching-on of the main drive and the doorAccording to the calculation results, Kmcp норм for the electric motor is 0.228; ncp норм for the brake is 1.065 per NMT minute. To assess the overall load level, the resulting technical condition of the elevator and its main components, a single generalized indicator is proposed — the load index WΣ. It is calculated as the sum of coefficients reflecting the relative level of load on the elevator assembly. It is established that as the resource is being depleted, while maintaining the value of time and power indicators, the estimated maintenance frequency will decrease. The recommended change threshold is 15–20 %.Discussion and Conclusion. The developed methodology for assessing the technical condition of the elevator unit allows us to develop a complex index by which we can judge the need to revise the regulatory intervals of the elevator unit assemblies. The proposed procedures are applicable to existing, installed and designed elevators. Simulation modeling in a specially developed computer program determines the main indicators of the technical condition of the power units of the elevator: the coefficient of net machine time, switching-on specific number, the power load and the share of the expired service life. Simulation modelling also takes into account the parameters of the building: population density and random external and internal influences. The method of adjusting the maintenance frequency allows you to quickly plan and optimize the costs of operating elevator equipment without losing the level of reliability and safety.

Optimisation of Reliability and Maintenance Plan of the High-Pressure Fuel Pump System on Marine Engine

Abstract This paper presents a method of adjusting and designing the maintenance scheme for the high-pressure fuel pumps of a slow-speed two-stroke marine engine, MAN 6S70MC-C. The maintenance database for the marine fuel system was obtained from the planned maintenance software, and covered a period of 11 years. During this period, 29 failures occurred that required corrective actions. Our methodology includes failure mode analysis, risk analysis, reliability calculation and maintenance interval adjustment. Each failure is described using a failure mode analysis, based on a combination of the mode and cause of failure. The objective of this study is to recommend a new preventive maintenance interval based on the exponential reliability results and the analysed maintenance data. The initial maintenance plan for each fuel pump was set to 8,000 running hours, whereas in the modified plan, it is recommended to set this to 4,000 hours. Our results show an increase in the system reliability from 60% to 95% when the new modified maintenance plan is applied. In addition, the results and the recommended initial maintenance schedule are validated based on three similar types of engine with the same fuel pump system. The new maintenance approach can reduce the risk of component failure, which will lead to increased reliability of the fuel pump system and the optimisation of maintenance costs.

The Pattern of Tooth Loss for Periodontally Favorable Teeth: A Retrospective Study.

To retrospectively analyze local and systemic factors that resulted in the short-term tooth loss of teeth that were previously assigned a favorable prognosis in patients who were seen and treated over an observational five-year period. This retrospective study included the records of patients who had a minimum of two dental exams at least twelve months apart over a 5-year period. This study investigated extracted teeth with an initially favorable periodontal prognosis that were then divided into one of four categories based on the reason for extraction: caries, periodontal disease, endodontic reasons, or fracture. Patient- and tooth-related factors associated with the extracted teeth were recorded: crown-to-root ratio, initial pocket depth, initial periodontal diagnosis, maintenance interval, presence of existing restoration, furcation involvement, and systemic conditions. Data analysis was performed using a linear mixed model. A total of 50 patients with 111 teeth met the inclusion criteria for this study. A higher odds ratio (OR) for tooth loss due to caries, endodontic reasons, and fracture were found in teeth with a history of root canal treatment with an OR of 3.61, 3.86, and 2.52, respectively. For tooth loss due to periodontal disease, higher ORs were found in patients who were on anti-depressants (OR = 4.28) and patients who had an initial diagnosis of Stage III/IV periodontitis (OR = 2.66). In addition, teeth with initial probing depths ≥5 mm (OR = 4.32) and with furcation involvement (OR = 1.93) showed a higher OR for tooth loss due to periodontal disease. Within the limitations of this study, previously root-canal-treated teeth present a higher OR for early loss due to caries, recurrent endodontic lesions, or fracture. In addition, patients with anti-depressant medication use, sporadic maintenance, initial probing depths ≥5 mm, and furcation involvement represent a significantly higher OR of tooth loss due to periodontal disease even for initially favorable teeth.

Machine Maintenance Planning Using the Reliability Centered Maintenance (RCM) Method at PT Perkebunan Nusantara XIV Camming Sugar Factory in Bone Regency

Purpose: Frequent damage to the mill roll machine at the mill station and the absence of special attention to scheduling repairs, this research is expected to make scheduled maintenance intervals and maintenance strategies for mill roll machines at the mill station to reduce downtime and anticipate sudden downtime on the machine.
 Design/methodology/approach: This study uses the Reliability Centered Maintenance (RCM) method approach to create the most efficient maintenance program by eliminating unnecessary maintenance activities, reducing overhaul frequency, minimizing downtime, minimizing the risk of sudden failure, focusing maintenance on critical components, and improving component reliability.
 Findings: The results of this study show that there are two strategies used in mill roll machine maintenance, namely preventive maintenance, and corrective maintenance.
 Research limitations/implications: This study focuses on critical components that often experience damage to the mill roll machine, namely standard milk check, feeder roll, scrapper, and chute components, and does not consider the cost aspect.
 Practical implications: The results of this study provide input to the company's interval time scheduling and machine maintenance strategy.
 Originality/value: This research was conducted in the production department of the milling station at PT Perkebunan Nusantara XIV Camming Sugar Factory in Bone Regency, where the production results include white crystal sugar, blotong, and molasses. Research to analyze machine reliability, minimize downtime and propose maintenance schedules and SOPs for maintaining production machinery at Camming Sugar Factory.

Optimization Model for Maintenance Planning of Loading Equipment in Open Pit Mines

Maintenance plays a significant role in operating costs in the mining industry. Improving this matter controls maintenance costs and enhances productivity and production effectively. Shovels are one of the most widely used loading machines in non-continuous activities. Thus, evaluating and optimizing their availability is one of the essential solutions to achieving high productivity and cost reduction. This paper presents a mathematical programming model to maximize availability and minimize the total expected costs. We programmed the proposed nonlinear planning model using the Symbiotic Organisms Search (SOS) meta-heuristic algorithm in Matlab software. It determines the optimal maintenance intervals for different parts of the shovel. The maintenance benefit analysis approach selects various maintenance activities in optimal maintenance intervals. The model is implemented in a practical case study, Chadormalu Iron Mine, to evaluate its performance. The failure distribution matches the Weibull distribution function. The computational results show the efficiency of the presented approach.

A two-stage Genetic Algorithm for joint coordination of spare parts inventory and planned maintenance under uncertain failures

The main problem of spare parts management is to maintain the minimal requirement of stock keeping units kept. This research proposes a joint optimization model of multi-item multi-period spare parts inventory management and planned maintenance under uncertain failures in order to balance inventory cost and spare parts availability. This paper presents a Mixed Integer Non-linear Programming formulation of the inventory optimization model under a minimum and maximum inventory policy with stock review intervals. Some studies in the literature have considered aggregating spare parts inventory management as they assume that it will reduce the ordering cost. We consider both independent and aggregate spare parts inventory policies and then combine the formulation with the predictive maintenance interval, which is a replacement action due to uncertain failures under predefined distribution. Furthermore, a novel two-stage Genetic Algorithm is proposed as a sim-heuristic approach to deal with the non-linearity, combinations, and stochasticity of the problem and solve large-scale instances. In the end, we perform a computational study on some instances and a real-world case study to demonstrate the proposed approach’s effectiveness and efficiency. The computational study shows that the independent policy results in lower cost than the aggregate policy, and the proposed Genetic Algorithm performs efficiently for large-scale problems.

Oil Change Interval Evaluation of Gearbox Used in Heavy-Duty Truck E-Axle with Oil Analysis Data

Regular lubricating oil change in the gearbox is desirable for improving vehicle reliability and reducing operating costs. To achieve this objective, evaluating the oil change interval is necessary. However, due to the complex and dynamic properties of oil degradation, oil change interval evaluation has been a bottleneck in practice. Therefore, a solution strategy is proposed in this paper that utilizes the oil physicochemical properties derived from oil analysis data to determine the optimal oil change interval. With a large amount of oil analysis data collected, the iron (Fe) debris, kinematic viscosity (100 °C), and total acid number (TAN) are considered to be the oil change indicators of lubricating oil. By monitoring the changes in the selected oil change indicators, linear regression is firstly applied to the original oil analysis data to reveal the dynamic degradation process. Then, the Wiener-based stochastic process is used to describe the first hitting time and the increasing trends of the selected oil change indicator. Finally, the oil change interval can be obtained under the concept of the first hitting time. Compared with the planned maintenance time, the proposed method seems reasonable considering the dynamic property of oil degradation. The effectiveness of the proposed method is evaluated using a case study with an oil analysis dataset from an E-axle with a two-shift gearbox. The results show that the oil change interval increased by approximately 10,000 km (50%) compared with the planned maintenance interval. This will reduce vehicle maintenance time and save maintenance costs.

Mitigating life cycle GHG emissions of roads to be built through 2030: Case study of a Chinese province.

The expansion of road networks in emerging economies such as China causes significant greenhouse gas (GHG) emissions. This development is conflicting with China's commitment to achieve carbon neutrality. Thus, there is a need to better understand life cycle emissions of road infrastructure and opportunities to mitigate these emissions. Existing impact studies of roads in developing countries do not address recycled materials, improved pavement maintenance, or pavement-vehicle interaction and electric vehicle (EV) adoption. Combining firsthand information from Chinese road construction engineers with publicly available data, this paper estimates a comprehensive account of GHG emissions of the road pavement network to be constructed in the next ten years in the Shandong province in Northern China. Further, we estimate the potential of GHG emission reductions achievable under three scenario sets: maintenance optimization, alternative pavement material replacement, and EV adoption. Results show that the life cycle GHG emissions of highways and Class 1-4 roads to be constructed in the next 10 years amount to 147Mt CO2-eq. Considering the use phase in our model reveals that it is the dominant stage in terms of emissions, largely due to pavement-vehicle interaction. Vehicle electrification can only moderately mitigate these emissions. Other stages, such as materials production and road maintenance and rehabilitation, contribute substantially to GHG emissions as well, highlighting the importance of optimizing the management of these stages. Surprisingly, longer, not shorter maintenance intervals, yield significant emission reductions. Another counter-intuitive finding is that thicker and more material-intensive pavement surfaces cause lower emissions overall. Taken together, optimal maintenance and rehabilitation schedules, alternative material use, and vehicle electrification provide GHG reduction potentials of 11%, 4%-16% and 2%-6%, respectively.

Collaborative production and predictive maintenance scheduling for flexible flow shop with stochastic interruptions and monitoring data

Flexible flow shop problem (FFSP) has been extensively researched in recent years. However, machine deterioration and stochastic production interruptions noticeably affect the regular job process in practice, incurring great cost. It is significant to incorporate the condition-based predictive maintenance scheduling and the production modification for interruptions with traditional FFSP. Nevertheless, this issue is rarely investigated in the existing FFSP studies. To solve the FFSP with maintenance and stochastic interruptions (FFSP-MSI), this paper proposes a collaborative optimization policy of production and maintenance (COPPM) by considering stochastic interruptions and monitoring data utilization for total cost reduction. For maintenance scheduling, a real-time prognosis updating method is leveraged based on monitoring data to capture the individual machine degradations. Assisted by the prognosis output, the optimal maintenance intervals are dynamically derived through a cost rate model. For production optimization, a variable neighborhood search (VNS) algorithm is presented to obtain the nearly- optimal initial production plan and the production modification for stochastic interruptions caused by machine failures and random jobs. This proposed COPPM comprehensively determines the cost-effective maintenance cycle, job order and machine selection for each job. Computational experiments of the wind turbine blade process are presented to certify the economic effectiveness of COPPM. Compared with the traditional production and maintenance scheduling method, the COPPM achieves 13.68 % total cost reduction in average under various problem scales.

RBM-MCDM framework for optimization of maintenance and inspection intervals of small unmanned aircrafts

PurposeUnmanned aircraft applications are quickly expanded in different fields. These systems are complex that include several subsystems with different types of technologies. Maintenance and inspection planning is necessary to obtain optimal performance and effectiveness. The failure rate in these systems is more than commercial and manned aircraft since they are usually cheaper. But maintenance and operation planning are difficult because we deal with a system that has multi-components, multi-failure models, and different dependencies between subsystems without any advanced health monitoring system. In this paper, this matter is considered and a framework to determine optimal maintenance and inspection plan for this type of system is proposed to improve system reliability and availability. The new criteria according to this field are proposed.Design/methodology/approachMaintenance of unmanned systems influences their readiness; also, according to the complexity of the system and different types of components, maintenance programming is a vital requirement. The plan should consider several criteria and disciplines; thus, multicriteria decision approaches may be useful. On another side, the reliability and safety of unmanned aircraft are the most important requirements in the design and operation phases. The authors consider these parameters and develop a framework based on risk-based maintenance to overcome the problems for unmanned systems. This framework consists of two stages: at the first stage, the critical components and failure modes are determined by FMEA, and in the second stage, the priority of maintenance tasks is determined by a fuzzy multicriteria weighted decision system. In this study, fourteen criteria with different levels of importance are developed and proposed to find the best plan for maintenance and inspection intervals. These criteria have been extracted from the literature review, the author's experience, and expert opinions.FindingsA novel framework for risk-based maintenance has been proposed. Risk determination and risk criteria are the most important factors in this framework. Risks are determined by FMEA, and new criteria are proposed that are used for decision-making. These criteria are proposed based on practical experience and experts' opinions for the maintenance process in the aeronautic industry. These are evaluated by industrial cases, and this framework capability has been demonstrated.Research limitations/implicationsThe proposed framework and criteria for small unmanned aircraft have been developed based on a practical point of view and expert opinion. Thus for implementation in other aeronautic industries, the framework may need a minor modification.Practical implicationsTwo important subsystems of an unmanned aircraft have been studied, and the capabilities of this method have been presented.Originality/valueThis research is original work to determine a maintenance program for unmanned aircraft that their application has rapidly grown up. Practical and design parameters have been considered in this work.

Residual lifetime estimation for the mining truck tires

Reliability analysis has a very important role in supporting the operation of a system in harsh working conditions such as mines. Haul trucks are considering as a main asset of an open pit mine. Dump trucks are comprised of different sub-systems and among them, tires have a significant effect on the haulage economy. This study focused on the remaining life estimation of the mining truck tires considering the environmental operating conditions at an open pit iron ore mine in Iran. To achieve this, the proportional hazard model was used to estimate the conditional reliability functions and then the mean residual life of tires at the various initial survival times. The results of this study show that the ambient temperature has a significant effect on the reliability performance. By increasing the initial life of tires from 500 to 1000 h, the mean residual life will be decreased by 21% and 30% in low (below 15°C) and high (above 15°C) ambient temperature levels. These results are helpful to keep the truck tire performance at a desirable level by considering the appropriate preventive maintenance intervals.

Optimal preventive maintenance interval model with time value of money consideration

The search for a plant maintenance strategy to mitigate the impact of random factory machine failures has never been more challenging. Apart from the harsh global competition, manufacturing facilities have become more sophisticated and expensive; frequent and prolonged breakdowns may ruin a firm’s business. Consequently, many have turned to the Preventive Maintenance (PM) strategy for a solution. However, the efficacy of PM has been shown to depend on finding a maintenance time interval decision model capable of incorporating most of the relevant maintenance cost components. While past studies have considered maintenance system set-up, execution, and breakdown cost components, the literature is sparse on decision models incorporating the cost of borrowing money. Exploring the feasibility of developing such a model is the aim of this study. Accordingly, a maintenance-investment cost function was formulated in terms of PM time interval and plant reliability; a PM decision problem was then defined and solved as that of PM investment cost minimization. Using three cases from the literature, the optimal maintenance time intervals (238.11, 14695.28, and 2076.54), failure probabilities (0.71, 0.35, and 0.61), and costs for preventive maintenance (PM) (840950.87, 149352.55, and 33979.57) were found to be reasonable.

Optimal preventive maintenance interval model with time value of money consideration

Optimal preventive maintenance interval model with time value of money consideration

Is It Time for Ocrelizumab Extended Interval Dosing in Relapsing Remitting MS? Evidence from An Italian Multicenter Experience During the COVID-19 Pandemic

In the COVID-19 pandemic era, safety concerns have been raised regarding the risk of severe infection following administration of ocrelizumab (OCR), a B-cell-depleting therapy. We enrolled all relapsing remitting multiple sclerosis (RRMS) patients who received maintenance doses of OCR from January 2020 to June 2021. Data were extracted in December 2021. Standard interval dosing (SID) was defined as a regular maintenance interval of OCR infusion every 6 months, whereas extended interval dosing (EID) was defined as an OCR infusion delay of at least 4 weeks. Three infusions were considered in defining SID vs. EID (infusions A, B, and C). Infusion A was the last infusion before January 2020. The primary study outcome was a comparison of disease activity during the A-C interval, which was defined as either clinical (new relapses) or radiological (new lesions on T1-gadolinium or T2-weighted magnetic resonance imaging (MRI) sequences). Second, we aimed to assess confirmed disability progression (CDP). A total cohort of 278 patients (174 on SID and 104 on EID) was enrolled. Patients who received OCR on EID had a longer disease duration and a higher rate of vaccination against severe acute respiratory syndrome-coronavirus 2 (p < 0.05). EID was associated with an increased risk of MRI activity during the A-C interval (OR 5.373, 95% CI 1.203–24.001, p = 0.028). Being on SID or EID did not influence CDP (V-Cramer 0.47, p = 0.342). EID seemed to be associated with a higher risk of MRI activity in our cohort. EID needs to be carefully considered for OCR-treated patients.

Dynamic Preventive Maintenance Optimization of Subway Vehicle Traction System Considering Stages

The current maintenance mode of the subway vehicle traction system does not consider the change of failure rate in the operation of components, and there is an unreasonable maintenance interval and high maintenance cost. To solve this problem, a dynamic preventive maintenance strategy for subway vehicle traction systems considering stages is proposed. Service age decreasing factor and failure rate increasing factor are introduced to build a new dynamic reliability model considering the effect of preventive maintenance on component failure rate. Non-preventive failure maintenance, preventive maintenance, preventive update maintenance and opportunistic maintenance are adopted. For the components in degradation or random failure stages, a preventive maintenance optimization model with the least maintenance cost, constrained by reliability, is established. The maintenance strategy is optimized and solved based on the enumeration method to obtain single-component irregular preventive maintenance intervals and multi-component group preventive maintenance times. The results show that the proposed maintenance models are reasonable and feasible. On the premise of ensuring the system reliability, the number of vehicle outages for maintenance and the maintenance cost are reduced. Feasible and effective maintenance models and methods are provided. They can ensure the reliability of subway vehicles and reduce maintenance costs. The effectiveness and economy of maintenance work is increased.

A predictive multi-objective condition-based maintenance (CBM) policy considering ecological factors

PurposeThe purpose of this paper is to propose an improved multi-objective optimization model for the condition-based maintenance (CBM) of single-component systems which considers periodic imperfect maintenance and ecological factors.Design/methodology/approachBased on the application of non-periodic preventive CBM, two recursion models are built for the system: hazard rate and the environmental degradation factor. This paper also established an optimal multi-objective model with a normalization process. The multiple-attribute value theory is used to obtain the optimal preventive maintenance (PM) interval. The simulation and sensitivity analyses are applied to obtain further rules.FindingsAn increase in the number of the occurrences could shorten the duration of a maintenance cycle. The maintenance techniques and maintenance efficiency could be improved by increasing system availability, reducing cost rate and improving degraded condition.Practical implicationsIn reality, a variety of environmental situations may occur subsequent to the operations of an advanced manufacturing system. This model could be applied in real cases to help the manufacturers better discover the optimal maintenance cycle with minimized cost and degraded condition of the environment, helping the corporations better fulfill their CSR as well.Originality/valuePrevious research on single-component condition-based predictive maintenance usually focused on the maintenance costs and availability of a system, while ignoring the possible pollution from system operations. This paper proposed a modified multi-objective optimization model considering environment influence which could more comprehensively analyze the factors affecting PM interval.

OPTIMIZATION OF DETERMINING MAINTENANCE INTERVALS WITH THE MARKOV CHAIN METHOD TO MINIMIZE MAINTENANCE COSTS IN PT. BBI

PT BBI is a company engaged in producing machinery, industrial equipment, and foundry. PT BBI has overgrown as a company that focuses on manufacturing and delivering products such as oil and gas/refinery/petrochemical industries, power plans, iron castings, precision machinery centers, etc. PT BBI uses various kinds of machines to support its production process. Some devices have often experienced damage and caused downtime in recent years, namely radial drilling machines and overhead cranes. In addition, machine damage also causes cost overruns due to damage that can harm the company. Engine damage caused is due to the company's poor maintenance policy. Judging from these problems, planning a maintenance system with a suitable method will help the machines and production equipment owned by PT. BBI is protected from damage that occurs periodically. The Markov Chain method was chosen because it has advantages over other maintenance methods because it produces more optimal maintenance costs with a regular maintenance scheduling system. The processing results obtained a total savings of 33% by applying the Markov Chain proposed maintenance method with maintenance intervals every 5 months for radial drilling machines and once every 3 months for 10T overhead cranes

A Stator Slot Wedge Loosening Offline Detection System Based on an Intelligent Maintenance Robot of a Large Hydro Generator

Hydropower is at the backbone of low-carbon clean energy, while large hydroelectric generators play a key role in hydropower systems. In this paper, the tightness detection of stator trough wedges in the offline state of large hydroelectric generators is mainly studied. The offline state means that the generator stops running but does not need to move the stator and rotor. The traditional detection of generator stator wedges has problems, such as long maintenance intervals and low work efficiency. According to the structural characteristics of the generator, a generator maintenance robot device based on the track mechanism is designed. The device can simultaneously visualize the internal of the generator and detect the tightness of the stator slot wedge, effectively improving the maintenance efficiency. According to the electromagnetic magnitude of the stator rod in the alternating magnetic field, different stator slot wedge models are built for tightening, slightly tightening and loosening. For the conventional slot wedge loosen detection, there is a problem that the characteristic parameter is single, and the state of the slot wedge cannot be fully fed back. In this paper, a method for extracting the Linear Prediction Cepstrum Coefficient (LPCC) and Mel Frequency Cepstrum Coefficient (MFCC) of percussion sound signal is proposed, and the tightness recognition of the stator slot wedge is realized by combining the BP neural network algorithm. Experimental results show that the proposed method can effectively identify stator slot wedges in different states.