Maintenance Priority Research Articles

DECISION ANALYSIS ON IMPLEMENTATION OF PRIORITY CARE AND MAINTENANCE DUE TO POTENTIAL DAMAGE AT ALFAMIDI MINIMARKET IN MALANG USING THE AHP METHOD

The decision on the priority for care and maintenance at Alfamidi Minimarket uses the AHP method to evaluate the decision for building care and maintenance to predict any potential damage to Alfamidi Minimarket. Thus, numerous complaints may be reduced, and building quality can be maintained to assure customer safety and comfort while shopping at Alfamidi Minimarket. The objectives of this research are as follows: (1) to determine the condition analysis of Alfamidi Minimarket in Malang, (2) to obtain a priority scale for implementing care and maintenance at Alfamidi Minimarket in Malang using the AHP method and Expert Choice V.11 Application modeling. This research applied a quantitative approach with a descriptive analysis technique; the sampling procedure was purposive sampling, and 13 Alfamidi Minimarkets in Malang served as the sample. The subjects of the research were Alfamidi and shop employees. The data collection method used a form about the building condition and a priority weighting questionnaire as supporting data. The Analytical Hierarchy Process (AHP) method can be utilized in making decisions since it systematically has multi-criteria properties to describe a problem. The analysis results show that: (1) The average condition of Alfamidi Minimarket in Malang based on the building condition index shows a good category with a percentage of 82.0669% with an average architectural component index (IKK) of 77.8%, a structural IKK of 97.75 %, Utilities IKK was 88.28%, and Environmental Management IKK was 63.0%. (2) According to the priority results, Alfamidi Dieng had the lowest building condition index at 78.99%, while Alfamidi Sigura-Gura had the highest at 85.82% Keywords: Priority; Care and Maintenance Building; AHP Method.

Fiscal and political determinants of local government maintenance

ABSTRACT Taking advantage of a novel data set on maintenance in Norwegian local governments, a comparison was made between norms for good maintenance and actual maintenance spending. Although a sizeable minority complies with the norm, the average maintenance spending is well below the norm. A theoretical model is developed to guide the empirical analysis of the determinants of maintenance. It emphasizes the roles of fiscal capacity, fiscal distress, and political fragmentation. The empirical analysis reveals that high fiscal capacity (measured by local government revenue) and little fiscal distress (measured by rainy-day funds) are associated with a high priority of maintenance spending. However, political fragmentation that reflects myopic behaviour is associated with low maintenance priorities. The results are robust and become stronger when outliers and small local governments are omitted.

Maintenance planning innovation: realising planning and execution efficiencies through process and digital transformation

As oil and gas facilities age, the need to allocate finite planning effort across mounting scopes of work increases. Woodside has tackled this problem head-on, seeking innovative solutions to reduce waste in deploying low-complexity asset maintenance. Through its Accelerator Programme, Woodside has employed new capabilities – such as enterprise design thinking and agile at scale delivery – conducting over 200 interviews and prototype tests to deeply understand maintenance priorities and identify opportunities to realise value, organisation wide. The result is an in-house scope management application, which supports the end-to-end maintenance planning workflow. The application considers multiple data points on thousands of work orders in seconds, using a sophisticated algorithm to incorporate industry knowledge and business priorities specific to Woodsides’ needs. The application produces a maintenance plan optimised by understanding location, equipment and process efficiency drivers to ensure the best utilisation of resources. The embedding of intelligent automation represents a cultural shift, enabling a move towards a more dynamic maintenance planning process that responds to changing asset priorities. Currently, 41% of all North-West Shelf Gas Project scopes are managed through the application. A 95% reduction in selection time and a 90% reduction in overall planning duration have been realised from this initiative. By releasing valuable planning effort back to the business to focus on complex scope, Woodside can realise and maintain safe, reliable and efficient operations in a low-cost, variable environment.

A retrospective assessment of fuel break effectiveness for containing rangeland wildfires in the sagebrush biome

Escalated wildfire activity within the western U.S. has widespread societal impacts and long-term consequences for the imperiled sagebrush (Artemisia spp.) biome. Shifts from historical fire regimes and the interplay between frequent disturbance and invasive annual grasses may initiate permanent state transitions as wildfire frequency outpaces sagebrush communities’ innate capacity to recover. Therefore, wildfire management is at the core of conservation plans for sagebrush ecosystems, especially critical habitat for species of conservation concern such as the greater sage-grouse (Centrocercus urophasianus; hereafter sage-grouse). Fuel breaks help facilitate wildfire suppression by modifying behavior through fuels modification and allowing safe access points for containment by firefighters. The Bureau of Land Management has proposed to roughly double the existing fuel break network in the western U.S., centered on the Great Basin. To our knowledge, no broad-scale examination of fuel break effectiveness or the environmental conditions under which fuel breaks are expected to be most effective has been conducted. We performed a retrospective assessment of probability of fuel break contributing to wildfire containment on recorded wildfire and fuel break interactions from 1985 to 2018 within the western U.S. We characterized environmental, fuels, and weather conditions within 500 m of wildfire contact, and within 5 km of the approaching wildfire. We used a binomial mixed model within a Bayesian framework to identify relationships between these variables and fuel break success. Fuel breaks were least successful in areas classified as having low resilience to disturbance and low resistance to invasion, in areas composed of primarily woody fuels, and when operating in high temperature and low precipitation conditions. Fuel breaks were most effective in areas where fine fuels dominated and in areas that were readily accessible. Maintenance history and fuel break type also contributed to the probability of containment. Overall results indicate a complex and sometimes paradoxical relationship between landscape characteristics that promote wildfire spread and those that impact fuel break effectiveness. Finally, we developed predictive maps of fuel break effectiveness by fuel break type to further elucidate these complex relationships and to inform urgently needed fuel break placement and maintenance priorities across the sagebrush biome.

Evaluating Pavement Lane Markings in Metropolitan Road Networks with a Vehicle-Mounted Retroreflectometer and AI-Based Image Processing Techniques

The objectives of this study were to evaluate pavement lane markings in a metropolitan road network and to develop a maintenance strategy for safe daytime and night-time driving. To achieve this, data on the retroreflectivity and physical defect ratio of lane markings were collected remotely using a vehicle-mounted retroreflectometer and high-resolution camera. The retroreflectivity was measured and analyzed by road type (city freeways, arterial roads, and collector roads) and by lane color (yellow, white, and blue) over a total length of 6790.34 km. The results indicate that the retroreflective performance deteriorates the most in the case of white lanes, regardless of the road classification, especially in the case of the first white lane. Additionally, the physical defects of lane markings were investigated over a total length of 502.82 km and categorized by road classification and lane color. Mask R-CNN and the Otsu Threshold method were used to automatically calculate the ratios of the defects. The results indicate that city freeways show a lower defect ratio than arterial and collector roads for all colors. Moreover, there is no significant difference between the white lanes for all types of roads. The distribution trends and relationship between retroreflectivity and the defect ratios were discussed according to the road type and lane color, and a method for selecting maintenance priority was suggested. The results show that the number of lanes requiring the restoration of retroreflectivity increases as the defect ratio increases. Therefore, we suggest prioritizing maintenance work on the lanes with a higher ratio of defects, covering a higher proportion of low-retroreflectivity sections. In addition, the unit length for data averaging can be adjusted to improve the work efficiency.

Priority of Maintenance of Structural and Architectural Elements Using the Analytical Hierarchy Process (AHP) Method

The building construction will decrease with an increased service life of the structure. The school building for SMP Negeri 2 Meulaboh West Aceh is one of the school buildings that need attention from the government. Much of the building damage is due to age and improper maintenance methods. This study aimed to determine the maintenance priorities of the structural and architectural elements of the SMP Negeri 2 Meulaboh school building. This study used the descriptive qualitative method. Primary data was collected through surveys, interviews, documentation, and distributing questionnaires, while secondary data was collected from related institutions or agencies. Data analysis was processed using the Analytical Hierarchy Process (AHP) method. The selection of sample respondents is not random/nonrandom sampling but is carried out using purposive sampling. The analysis results of the order of priority for the maintenance of structural elements the priority, namely, the column, the type of damage to the chipped concrete cover gets the highest priority with a priority value of 0.359. While the second priority is for architectural elements, namely ceilings, walls, doors, windows, and floors, the damage to fading ceiling paint gets the lowest priority with a value of 0.016.

Evaluation of Bridge Maintenance Priorities in Megacities

Bridges are key social overhead capital facilities with a direct impact on citizen safety and the socioeconomic development of a country. Many bridges in South Korea were completed during the nation’s period of rapid economic growth in the 1970s and 1980s, meaning that bridge aging is now accelerating and should be of real concern. Based on records of other highly developed countries not having adequate maintenance systems for old bridges and consequently experiencing a number of tragic bridge collapses, Korea is at risk of following suit. Being able to measure the efficiency of bridge management considering economic and social values is therefore critical. In this work, a qualitative bridge maintenance priority evaluation model using open-source bridge information was developed. The model was evaluated by applying it to eight actual bridges located in Daegu Metropolitan City, Korea. It is concluded that aging bridge maintenance and management can be performed more efficiently and reliably if the model proposed in this work is used.

Priority Determination of Maintenance Sediment Control Bengawan River Basin, Indonesia

The volume of sedimentation in the Bengawan Solo river is 592,936.05 m3 per year. Several check dams have been built to overcome the sedimentation problem. However, this check dam building has experienced a decrease in performance due to sedimentation. Therefore, the research in this thesis aims to determine the priority of maintenance and repair of sediment control buildings (check dams). Determining priorities due to lack of budget for buildings processed by the government. Use guidelines for classifying check dam damage and building maintenance. Determining maintenance priorities based on the condition of the building using the analytical hierarchical process method. The location of the case study is a check dam building on the Bengawan Solo River and the results of the analysis show that the one with maintenance and repair priorities is the ploso check dam with severe damage (42.5%) and the maintenance carried out is rehabilitative (performance <60%).

Data-driven maintenance priority recommendations for civil aircraft engine fleets using reliability-based bivariate cluster analysis

The modern civil aircraft engine is a type of highly complex engineering system in design, manufacturing, and life-cycle management. They are constantly operated under extreme and critical conditions, and yet, high reliability and safety are top priorities in the civil aviation industry. To ensure top performance and efficiency in operations, engines follow a modular design. This article intends to apply the data-driven cluster analysis to real-life operation data for aircraft engine fleets, which provides a module maintenance priority recommendation solution to increase the efficiency of operations and best use of the engine values.

Mathematical modeling and reliability analysis for pellet manufacturing unit of a polymer section

The study has been performed to know the availability of operating equipment in the industrial unit with mathematical modeling. The pellet manufacturing unit is a repairable system that can work satisfactorily after getting proper maintenance of a polymer section at naphtha cracker unit. It consists of various subsystems, namely steamer agitator, dryer, rotary feeders, feeders and screw feeder machine. The number of failures of a subsystem and its repair rate with respect to time are assumed to be constant. In current paper, system behaviour was analysed for knowing the actual availability in the unit with available maintenance data. The maintenance department’s history sheets were used to compile the data for all five subsystems. The probability-based approach of Markov process used for developing partial differential equations which are further solved by putting the sum of all the events equal to one. The steady state availability with known parameters were calculated to know the system availability. A critical analysis of subsystems has been carried out to enhance the performance of above said unit. It was observed that feeder subsystem was affecting the system availability more as compare to other subsystems. If failure rate increases, system availability decreases up to 7.0% and repair rate increases, availability increases up to 9.4%. That’s why maintenance priority should be given to this subsystem. The calculated results are communicated to the relevant authorities so that the right maintenance choices can be carried out on time.

Comprehensive performance ranking and impact analysis using the best approximation matter-element model and combined weights

The evaluation of pavement conditions determines the need for pavement maintenance. To ensure that maintenance funds are allocated fairly, it is necessary to prioritize the overall performance of pavement sections based on multidimensional state data and determine maintenance priority. In this study, the pavement functional performance, structural performance, and material performance were among seven indexes chosen to evaluate to overcome the limitation of only considering the functional index in evaluating the performance of asphalt pavement. The extended matter-element model was combined with the technique for order preference by similarity to an ideal solution (TOPSIS) based on determining the weight of the index combination and applied to the Hubei section of the Beijing-Hong Kong-Macao Expressway to calculate the matter-element of the best closeness of the pavement performance and determine the priority of maintenance. The case study demonstrates that the proposed method can obtain an accurate ranking of the overall performance of the alternative road sections, and its superiority was demonstrated through comparative analysis. Furthermore, the degree of influence of each performance index on overall performance was determined and discussed. Among them, dynamic modulus (|E⁎|), which represents the mechanical strength of asphalt mixture material, contributed the most influence, while the rut depth index (RDI), which represents the permanent deformation level of road surface, contributed the least. This recommended evaluation method can be used to develop a pavement repair plan.

Reconstruction and Maintenance Priority Buildings in Aceh Besar Regency

The Aceh Besar Regency has 4 operational permanent market buildings, namely Indrapuri Lama Market, Krueng Raya, Lambada, and Lambaro Pilot. However, it is necessary to determine the priority scale for these 4 buildings in terms of reconstruction and maintenance for the public interest due to the limited budget of the regency’s government. Therefore, this research aims to determine the dominant criteria and order of priority in maintaining and caring for the permanent market buildings belonging to the Aceh Besar Regional Government. This is a quantitative research with the purposive sampling method used to collect data from 6 relevant stakeholders and policymakers through questionnaires. The criteria reviewed are the building condition, age, and area, as well as the total number of stalls, traders, transactions, and population. The data analysis technique used descriptive statistics and Multi-Criteria Analysis (MCA). These results showed that the dominant criteria that need to be considered in the maintenance and care of permanent market buildings are the building conditions with an average criterion weight value of 0.35. Additionally, the first, second, third and fourth priorities were given to Lambaro Pilot Market, Indrapuri Lama, Krueng Raya, and Lambada with alternative performance (Pi) values of 9.00, 5.69, 5.63, and 5.10, respectively.

Recovery Analysis and Maintenance Priority of Metro Networks Based on Importance Measure

The metro network plays a vital role in the urban transportation system. However, the metro network is easily damaged by humans and natural disturbances. This can cause serious economic damage, such as the suspension of metro station operations and line disruptions. Therefore, we conducted this study in order to minimize the loss caused by the damage to the metro network and improve the performance of the network after recovery. Based on the cascading failures of metro networks, this paper proposes a recovery model for metro networks considering the value of time. Then, considering the time value, a new node importance measure is proposed using the determination of maintenance priorities. The maintenance priorities of nodes with different importance values are investigated to minimize network losses. Lastly, the applicability of the method is verified by a metro network in Zhengzhou city.

A resilient network recovery framework against cascading failures with deep graph learning

Because of the increasing importance and dependencies of infrastructure networks and the potential for massive cascading failures in real-world network systems, maintenance optimization to effectively reduce system performance loss caused by diverse disruptions is of significant interest among researchers and practitioners. In this work, a new recovery framework was developed to rapidly identify important system components for maintenance to improve network resilience against cascading failures. This work provides distinct advantages to determine an optimal maintenance priority by combining real-time network structure importance with other maintenance prioritization based on customer preference. This approach adopts structural graph embedding and deep reinforcement learning to extract real-time network topology information (such as minimum vertex cover) to update the maintenance priority during the recovery process. Based on the case studies on synthetic networks and a US airport network, the proposed recovery framework with real-time network topology awareness shows better performance than other maintenance prioritization strategies regarding resilience enhancement. This work improves the understanding of how the changing network structure influences maintenance effects. It also provides insights of the practical usefulness of advanced deep learning on helping optimal maintenance prioritization to effectively reduce the intensity and extent of cascading failures.

Optimization of Snowplow Routes for Real-World Conditions

During the winter season, snowplowing has a significant effect on road users as it is critical to winter road maintenance and operations. The main goal of this study is to generate optimal routes for snowplowing trucks for efficient road maintenance. In addition to the conventional problem of reducing travel time and distance, this study also incorporates actual operational constraints, such as minimum maintenance standards and driver safety, to improve the overall efficiency of operations. To achieve the objectives, we first implemented the Chinese Postman Problem (CPP) to create Euler circuits from the initial routes and then identified the shortest paths by applying Dijkstra’s algorithm. Then, the Tabu search algorithm was chosen as a metaheuristic algorithm for the optimization process that finds near-optimal solutions by considering operational constraints for snowplow routes. Unsafe turning conditions and minimum maintenance standards were taken into account in the objective function defined for the optimization process. In simulations, the route obtained by our approach was compared to one with the application of CPP only in terms of travel distance, time, turning conditions, and road maintenance priority.

Evaluation methodology for preventive maintenance in multi-state manufacturing systems considering different costs

ABSTRACT In a multi-state manufacturing system, preventive maintenance is performed on other machines once the performance of a machine falls below a threshold state. Many investigations have been performed on the maintenance of manufacturing systems. However, studies on preventive maintenance for manufacturing systems that account for the different costs involved are limited. This paper focuses on the analysis of multi-state machines in a manufacturing system considering different costs. Subsequently, a cost-based preventive maintenance prioritisation method for multi-state machines is proposed. Based on the buffer capacity, three maintenance policies on the machines are discussed. Different machine failures lead to different maintenance priorities. The real-time buffer capacity greatly influences the selection of machines for preventive maintenance. The set of the optimal machines for preventive maintenance is determined based on the constraints of machine running time and different preventive maintenance costs. Finally, a numerical example is used to demonstrate the effectiveness of the proposed method.

Road Surface Damage Analysis Using GIS Based Bina Marga Method

The function of Primary arterial street is to serve as road users to connect to the national activity center and for regional activity, which is long-distance travel. On that road, there are so many roads damaged, damaged due to heavy vehicle loads and some high frequencies. The study aims to identify the type of damage and to help in how the maintenance management of this street through system will be managed and systematically stored in the form of databases. The research used Bina Marga methods, assessments of road conditions refer to the Organizing of Urban Road Maintenance Programs issued by the Directorate General of Bina Marga. The survey was divided into 20 segments per 100 meters, the data were obtained from field surveys, with damage data and field photos, and then input data of the street table that use the file formats in ArcGIS software, and later data can be further analyzed. Research shows that the dominant damage happens in grains and patching. Research results on the road of Soekarno-Hatta KM 8-10 have a pretty low rate of damage, shown by the maintenance priorities listed in a regular maintenance program, which means maintenance or repair simply top layer without repairing road structures

Prioritizing CBs Maintenance and Identifying Mandatory Maintenance at Higher Priorities

Preventive maintenance (PM) based on condition monitoring of circuit breakers (CBs) provides proper and timely maintenance of CBs and reduces their failure rate as well as network costs. Condition monitoring of CB considers functional age (not natural age) of CB. This functional age depends on CB erosion. In this paper, CB condition assessment is obtained by monitoring the coil current and its duty cycles. Then, the maintenance priority between CBs of a power network is achieved using self-evaluation decision-making algorithm (SEDMA). This algorithm works in such a way that both the performance of CB is tracked by itself over time as well as compared to other CBs. First, this means the condition of the CB is measured by itself, and if the condition of the CB deteriorates over time, the CB will be given priority for repair. Second, the CB is compared offline with other CBs, and owing to the CB failures compared to other CBs, the priority of CB repair will be finalized. Then, using sensitivity analysis, it is determined whether the CB with the highest priority needs to be repaired or not, and the failure level of each CB will be specified. The case study in this paper concerns about two similar CBs. Also, the effect of improving the condition of the CB will be determined by performing maintenance operations. Eventually, it will be clear which CB has a higher priority for maintenance and whether this higher priority justifies the need for maintenance or not.

On-Device IoT-Based Predictive Maintenance Analytics Model: Comparing TinyLSTM and TinyModel from Edge Impulse.

A precise prediction of the health status of industrial equipment is of significant importance to determine its reliability and lifespan. This prediction provides users information that is useful in determining when to service, repair, or replace the unhealthy equipment’s components. In the last decades, many works have been conducted on data-driven prognostic models to estimate the asset’s remaining useful life. These models require updates on the novel happenings from regular diagnostics, otherwise, failure may happen before the estimated time due to different facts that may oblige rapid maintenance actions, including unexpected replacement. Adding to offline prognostic models, the continuous monitoring and prediction of remaining useful life can prevent failures, increase the useful lifespan through on-time maintenance actions, and reduce the unnecessary preventive maintenance and associated costs. This paper presents the ability of the two real-time tiny predictive analytics models: tiny long short-term memory (TinyLSTM) and sequential dense neural network (DNN). The model (TinyModel) from Edge Impulse is used to predict the remaining useful life of the equipment by considering the status of its different components. The equipment degradation insights were assessed through the real-time data gathered from operating equipment. To label our dataset, fuzzy logic based on the maintainer’s expertise is used to generate maintenance priorities, which are later used to compute the actual remaining useful life. The predictive analytic models were developed and performed well, with an evaluation loss of 0.01 and 0.11, respectively, for the LSTM and model from Edge Impulse. Both models were converted into TinyModels for on-device deployment. Unseen data were used to simulate the deployment of both TinyModels. Conferring to the evaluation and deployment results, both TinyLSTM and TinyModel from Edge Impulse are powerful in real-time predictive maintenance, but the model from Edge Impulse is much easier in terms of development, conversion to Tiny version, and deployment.

Development of a New Procedure for Assessing Road Signs Maintenance Priorities for the Tuscan Road Network

Road network safety management is a challenging process for Road Administrations (RAs) in Italy. It requires planning their activities, identifying operational actions, and implementing maintenance activities. For an effective road management system, all these activities should be based on a replicable monitoring procedure which relies on technical criteria to identify critical sites requiring attention. To develop and implement a reliable and effective maintenance strategy, Road Administrations need inexpensive and user-friendly procedures that can be applied to the entire road network. Road signs and road markings plays an important role in providing visual guidance for road users, which in turn influences road safety. This paper proposes and discusses a standardized and systematic monitoring procedure for the condition assessment of road signs and markings. The procedure will be applied to identify damaged, old, or unreadable signs and markings and a section of a two-way two-lane rural road has been used to test and calibrate the proposed procedure.