Proper Maintenance Strategy Research Articles

Bayesian network approach for reliability analysis of mining trucks

Having a safe and efficient system for mineral transportation is a top priority for all mining operations. Trucks are the most widely used material transportation systems that are applied in both surface and underground mines. Any truck failure disrupts the mineral transportation process and consequently decreases the overall output. Therefore, the reliability analysis of such equipment plays a critical role in increasing the efficiency and productivity of a mining operation. This paper proposes a novel method for analyzing the reliability of a fleet of mining trucks based on the Bayesian Network modeling. Considering the reliability block diagram, the fault tree of trucks was developed according to the logical relationship between the units. Then, a dynamic Bayesian network was constructed according to the conditional probability analysis. Moreover, the relative contributions of each truck’s component to the occurrence of the fleet failure were studied by using critical analysis. The results of this paper show that the successful operation of the fleet of trucks is most sensitive to truck no. 5, which has the highest reliability level in all time intervals. The reliability of the fleet of trucks reaches 0.881 at 20 h, and the fuel injection system of the truck’s engine is the main leading cause of the trucks failure. A proper preventive maintenance strategy should be paid more attention to improve the reliability and availability of the engine system.

Evaluation of the Performance of Nature-Based Constructed Wetlands for Treating Wastewater from Various Land Uses in Korea

Land use changes, specifically the growth of impervious areas due to urbanization, exacerbate non-point-source pollutants in stormwater runoff, surpassing discharge from point sources in Korea. The application of nature-based solutions, such as constructed wetlands (CWs), is becoming popular for stormwater treatment, but challenges arise when background concentrations are overlooked, leading to reduced pollutant removal efficiency. This study aims to propose a plan for the sustainability of CWs by evaluating design appropriateness and utilizing existing monitoring results. The evaluation of 63 CWs reveals that meteorological factors, specifically antecedent dry days and rainfall depth, have significant impacts on urban stormwater runoff quality in various land uses, affecting the performance of CWs. Designing CWs considering land use is crucial due to the considerable concentration variations across different land uses. Improving CW performance requires proper maintenance strategies to ensure effective pollutant removal mechanisms, especially for poorly degradable organic substances post treatment. Rainfall characteristics play a pivotal role in CW design and operation, affecting capacity, efficiency estimation, and maintenance frequency. Considering various factors such as land use, watershed characteristics, and ease of maintenance is essential when utilizing CWs. This study’s findings contribute to the design and operation of future CWs, emphasizing the need for continuous performance analyses through long-term monitoring.

Fully Unsupervised Defect Clustering using Adversarial Autoencoder

Identifying defect types and developing proper maintenance strategies is a major concern in modern industry. Most conventional studies have been conducted primarily based on a supervised learning scheme. However, supervised learning has a critical limitation in that it requires labeled data, which is difficult and expensive to obtain in real-world industry. Considering that there are many industries that do not perform post investigations on the defects, fully unsupervised learning methods, which do not exploit any information such as label data or the number of types, need to be developed. Accordingly, in this study, we propose a fully unsupervised defect clustering method that does not exploit any information other than the data itself. The proposed method consists of two major components. The first is dimensionality reduction into latent space via adversarial autoencoder, and the second is a Bayesian mixture model for distribution estimation in latent space. The experiments on a rolling-element-bearing dataset validate the effectiveness of our method. Specifically, our method performs defect clustering without any information other than the data itself, which is promising for real industrial applications.

Opportunistic maintenance modeling for series production systems based on bottleneck by considering energy consumption and market demand

ABSTRACT Nowadays, series production systems are broadly used in various branches of industry. In these kinds of production systems, any disruption in production flow can significantly increase production cost and time; therefore, a proper maintenance strategy considerably reduces the company’s costs and enhance organizational performance. In this research, a novel bottleneck-centered opportunistic maintenance policy for a series production line is proposed. First of all, the bottleneck is detected by the Lowest Production Rate method. Then, preventive maintenance time interval, reliability threshold, and number of maintenance cycles are determined by the particle swarm optimization algorithm. Finally, a novel opportunistic maintenance strategy is proposed based on analyzing the cost savings of preventive maintenance advancement and postponement. To demonstrate the model’s effectiveness, the proposed bottleneck-based opportunistic maintenance model is compared with two other maintenance models, and the results confirmed that the proposed strategy could efficiently eliminate unnecessary production breaks and achieve cost reduction.

An optical flow-based method for condition-based maintenance and operational safety in autonomous cleaning robots

Autonomous professional cleaning robots are ubiquitous today, coexist with humans, and the demand continues to improve hygiene and productivity, especially in large indoor workplaces. Hence, a proper maintenance strategy is essential to monitor the robot’s health, assuring flawless operation and safety. Manual supervision and periodic maintenance are adopted in general, which is challenging to detect failures in advance, resulting in higher maintenance costs and operational hazards. Anomalous vibration due to system degradation or environmental factors is an early symptom of failure or potential threats. Hence, predicting the sources of such abnormal vibration will help prompt maintenance action or set a hazard-free environment. However, such condition-monitoring research studies are not common for indoor cleaning robots. To fill this gap, we proposed an automated Condition Monitoring (CM) system, predicting the abnormal vibration sources that will enhance Condition-based Maintenance (CbM) and Operational Safety. A novel vibration-based CM method is presented for mobile robots, suitable for indoor environments under typical illumination states, using a monocular camera and optical flow technique instead of their usual perception-related applications. The sources of abnormal vibration were classified as terrain, collision, loose assembly, and structural imbalance. We modelled vibration as sparse optical flow, and the change in optical flow vector displacement due to the robot’s vibration is derived from consecutive camera frames used as vibration data. A practical framework is developed adopting the One Dimensional Convolutional Neural Network (1D CNN) model to fit this vibration data and tested predicting the vibration source class with an average accuracy of 93.8%. In addition, a vibration source map (CbM map) is proposed by fusing the predicted class in the workplace environment map for real-time monitoring. The case studies conducted using our in-house-developed cleaning robot show that the proposed CM framework will help the maintenance team for CbM, operational safety, and select proper maintenance strategies.

Grouping Preventive Maintenance Strategy of Flexible Manufacturing Systems and Its Optimization Based on Reliability and Cost

A flexible manufacturing system (FMS) improves productivity and makes it more efficient. Maintaining reliability levels and reducing costs through proper maintenance strategies are key problems for the development and application of a FMS. This paper proposes a grouping preventive maintenance strategy of a FMS with optimized parameters by considering both reliability and cost. In this work, a three-layer evaluation index system is first presented to accurately estimate the reliability of the FMS; index weights of each layer were obtained by reliability importance modeling and analysis, considering maintenance strategies. An element-grouping preventive strategy is proposed based on an influencing analysis, and a parameter optimization problem (considering reliability and maintenance costs) was established. In this strategy, three maintenance methods are presented for the elements, including low-level maintenance with a large period, low-level maintenance with a small period, as well as the combination of low-level maintenance with a small period and high-level maintenance with a large period; the effects of reliability improvement of the elements on the subsystem’s reliability were analyzed to provide evidence for element grouping. Finally, the proposed method was applied to a box-part finishing FMS; the results indicate that this method can effectively reduce maintenance costs on the premise of satisfying the reliability requirements.

Damage to anthropic elements estimation due to large slope instabilities through multi-temporal A-DInSAR analysis

Deep-seated gravitational slope deformations (DsGSDs) are widespread phenomena in the Alpine environment. Their dynamics, although very slow, endanger human settlements and connecting infrastructures. Monitoring such phenomena is mandatory to evaluate the impact on infrastructure networks and inhabited areas. Nowadays, the implementation of a tool useful to define and manage the interactions of DsGSDs evolution and the anthropic element remains a challenge, particularly in land use planning. Apart from on-site monitoring, which is commonly poorly used for DsGSDs observation, satellite-based interferometry represents the most comprehensive instrument for an effective spatial and temporal characterization of these phenomena. This paper provides a dedicated procedure to assess the usability of Advanced Differential Interferometric SAR (A-DInSAR) techniques to explore the DsGSDs behaviour and investigate their local interaction along anthropic elements. Combining multi-temporal A-DInSAR data, ERS-1/2 (1992–2000), Radarsat-1/2 (2003–2010), COSMO-SkyMed (2011–2018) and Sentinel-1 (2014–2018), over the Motta de Pletè and Champlas du Col DsGSDs, north-western Italy, a line-of-sight displacement investigation over a long-time span is implemented. Multi-temporal deformation maps are generated to define the deformation pattern and DsGSDs evolution over time. Subsequently, a local-scale analysis along the main anthropic elements is performed, exploiting Vslope values and ground deformation time series, integrated with ground-based ones, where available. This local analysis is aimed to recognize the most critical sections of the anthropic elements along with an higher level of damage, and potential risk is expected. Moreover, the obtained results are compared with a survey damage of the anthropic elements for a local cross-check and to strengthen the A-DInSAR methodology. Overall, the presented methodology provides a powerful tool to better define the DsGSDs local dynamics in correspondence of the main strategic infrastructures and inhabited areas, for a proper infrastructure maintenance and territorial planning strategy.

An optimum budget‐based development in maintenance for electric power transmission networks

On the topic of power systems, the most critical assignment would be providing facilities to make electricity available at the lowest price with a fulfilled level of reliability for all consumers. Today, power transmission reliability has received more concentration due to the increasing essence of providing high-quality energy to customers and operating competitive conditions in electricity markets. In this regard, this paper scrutinizes the reliability-centred maintenance (RCM) issue in terms of considering two optimal maintenance strategies for transmission assets to decrease their forced outage rate (FOR). This techno-economic budget-based maintenance is a probabilistic approach to help asset managers and experts in the decision-making process: to determine between maintenance strategies with associated costs and provide the most economical resource allocation for these elements, which leads to an improvement in the reliability of the whole network. Decreasing the average probability of Energy Not-Supplied (ENS), the simulation determined the proper maintenance strategy for transmission components. After that, the repercussions of the fallen ENS will be analyzed in total maintenance cost (MC), operation cost (OC), and the system's total cost (TC). Also, the IEEE 24-bus reliability test system is considered as a case study demonstrating the proposed technique's practicality.

A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis

Gas path fault diagnosis of a gas turbine is a complex task involving field data analysis and knowledge-based reasoning. In this paper, a data-knowledge hybrid driven method for gas path fault diagnosis is proposed by integrating a physical model-based gas path analysis (GPA) method with a fault diagnosis ontology model. Firstly, a physical model-based GPA method is used to extract the fault features from the field data. Secondly, a virtual distance mapping algorithm is developed to map the GPA result to a specific fault feature criteria individual described in the ontology model. Finally, a fault diagnosis ontology model is built to support the automatic reasoning of the maintenance strategy from the mapped fault feature criteria individual. To enhance the ability of selecting a proper maintenance strategy, the ontology model represents more abundant knowledge from several sources, such as fault criteria analysis, physical structure analysis, FMECA (failure mode, effects, and criticality analysis), and the maintenance logic decision tool. The availability of the proposed hybrid driven method is verified by the field fault data from a real GE LM2500 PLUS gas turbine unit. The results indicate that the hybrid driven method is effective in detecting the path fault in advance. Furthermore, diversified fault information, such as fault effects, fault criticality, fault consequence, and fault detectability, could be provided to support selecting a proper maintenance strategy. It is proven that the data-knowledge hybrid driven method can improve the capability of the gas path fault detection, fault analysis, and maintenance strategy selection.

Self-Adaptive Risk-Based Inspection Planning in Petrochemical industry by evolutionary algorithms

This research proposes a multi-objective mathematical model for Self-Adaptive Risk-Based Inspection Planning (SARBIP). Risk management is essential to organizations to ensure that a proper maintenance strategy is in place. An inspection plan is required to improve the overall safety and achieve optimal configurations for the investment, repair, and maintenance, in order to minimize the total expenses. To this end, a SARBIP model is proposed to obtain optimal solutions. The proposed model serves a dual purpose by reducing both the expenses and risk level. The proposed algorithm, which is used as the model's solution, is validated by being applied to a real case study in the Iranian petrochemical industry. The model is executed through two algorithms, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-Objective Particle Swarm Optimization (MOPSO). The outcomes indicate that MOPSO outperforms other algorithms in large-scale problems.

Maintenance Strategy Selection using Fuzzy Delphi Method in Royal Malaysian Air Force

The proper maintenance strategy is significant in extending assets and equipment, thus saving maintenance within an organization. Currently, there are three types of maintenance strategies implemented in the Royal Malaysian Air Force (RMAF), namely Reactive Maintenance (RM), Preventive Maintenance (PM), and Condition Based Maintenance (CBM). Due to the constraints in terms of maintenance costs by RMAF, choosing the right maintenance strategy is important to ensure that the maintenance provision can be optimized. In this research study, the Fuzzy Delphi Method has been used as a tool in determining the most effective maintenance strategies to be adopted by the RMAF. The output of agreement and opinion from experts in the related field has been used to select the appropriate maintenance strategy. In choosing this maintenance strategy, goals are set first in line with RMAF maintenance’s objectives. The specified maintenance goals are as follows; low maintenance cost, reducing the chance of a breakdown, safety, feasibility on the acceptance by labor, and response time starting from failure. Later, the result showed that the fuzzy score for RM, PM, and CBM was 0.747, 0.789, and 0.767, respectively. The highest fuzzy score showed the most accepted method chosen by the expert. Based on the result and maintenance goals that have been outlined, experts have agreed to choose PM as a maintenance method that should be given priority to be implemented in RMAF compared to other maintenance methods due to the highest fuzzy score.

Advanced corrosion-rate model for comprehensive seismic fragility assessment of chloride affected RC bridges located in the coastal region of india

In this study, a methodology is being proposed for performing seismic fragility analysis of corrosion affected bridges located in the coastal region of India. The study performed adopting this advanced corrosion rate model capturing nonlinearity that necessitates pitting corrosion as a realistic corrosion degradation mechanism rather than conventional uniform corrosion. With the application of advanced corrosion model, a distinct improvement over seismic fragility assessment achieved due to its capability to capture nonlinearity in the overall process of corrosion transport. The proposed model exhibits the corrosion rate accelerates after initial cracking of the concrete cover until at a phase, when severe cracking occurs leading to spalling of concrete. Based on the current degradation mechanism, the study estimates the capacity limit states of deteriorating reinforced concrete (RC) T-girder bridges as well as the impact of bridge degradation on the seismic response assessed using nonlinear time history analysis (NLTHA). However, the NLTHA of the finite element bridge models performed considering erraticism about material properties, geometries, ground motions and corrosion degradation parameters such that the uncertainties associated with the bridge population could be realistically taken into account as per the basic corrosion mechanism. Comparisons are drawn with respect to general and pitting corrosion as well as the adverse effect of pitting corrosion on the bridge service life has been identified. Thus, the output of the seismic fragility analysis would indicate some useful information in understanding the nonlinear crack growth of bridge vulnerability in marine environment, such that proper maintenance strategies could be framed for adequate safety of the highway infrastructure system for its enhanced design life.

Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study

Pavement markings constitute an effective way of conveying regulations and guidance to drivers. They constitute the most fundamental way to communicate with road users, thus, greatly contributing to ensuring safety and order on roads. However, due to the increasingly extensive traffic demand, pavement markings are subject to a series of deterioration issues (e.g., wear and tear). Markings in poor condition typically manifest as being blurred or even missing in certain places. The need for proper maintenance strategies on roadway markings, such as repainting, can only be determined based on a comprehensive understanding of their as-is worn condition. Given the fact that an efficient, automated and accurate approach to collect such condition information is lacking in practice, this study proposes a vision-based framework for pavement marking detection and condition assessment. A hybrid feature detector and a threshold-based method were used for line marking identification and classification. For each identified line marking, its worn/blurred severity level was then quantified in terms of worn percentage at a pixel level. The damage estimation results were compared to manual measurements for evaluation, indicating that the proposed method is capable of providing indicative knowledge about the as-is condition of pavement markings. This paper demonstrates the promising potential of computer vision in the infrastructure sector, in terms of implementing a wider range of managerial operations for roadway management.

Reaction behaviour of drivers to marked and unmarked road: Ghana perspective

Africa is the leading continent globally in the rate of road traffic fatalities, yet it is the least motorized compared to the other five continents. This predicament is said to be one of the leading cause of death among youth and generally, rated as one of the ten causes of death in the world. Exclusively, Ghana’s rate of traffic fatalities is growing despite the efforts invested in reducing it. Nevertheless, more focus needs to be invested in the traffic control systems such as traffic signals, signs or road markings. As this system tends to considerably reduce the number of conflicts and minimize road user’s errors. Furthermore, this system creates drivers’ expectations of the conditions which they will meet ahead and the driving tasks required. If misleading information is provided, or none is available, hazardous situations can result. Overall, this traffic system is inadequate or lacking in most developing countries as there are no proper maintenance strategies in place. Thus, this study investigates and evaluates the reaction of drivers to the marked and unmarked roads. Using random quantitative sampling methods, Ghanaian drivers were interviewed on their experiences when driving on the marked and unmarked road. Overall, this study will highlight the necessity of road markings in reducing traffic fatality rate and the psychological effect of the unavailability of road marking on drivers’ expectation and consequently, the effect on their behaviour in most developing countries.

Reliability and Maintainability Investigation of Generator in Steam Turbine Power Plant using RAMD analysis

Generator is key unit of steam turbine power plant which is used to convert mechanical energy into electrical energy. This energy is further used for completing the requirement generated by various sectors of country. Generator is one of the most important component of steam turbine power plant configured with seven subsystems in series configuration. So, it is necessary to ensure its smooth functioning which is attain by the proper functioning of its subcomponents. It is important to increase operational availability of subsystem. Hence, present study is proposed to investigate various reliability measures of generator used in STP through RAMD approach at component level. For this purpose, mathematical models using Markovian birth death process have been developed for all subsystems of generator. These models are very helpful in reliability, maintainability, and availability analysis of generators. RAMD analysis will play significant role in this direction. This will help to find critical component of system so that proper maintenance strategies can be proposed. It is assumed that all failure and repair rates associated with each component followed exponential distribution and adequate repair facility always remains available with system. Various system performance measures like reliability function, maintainability function, dependability function and steady state availability for different components as well as system have been evaluated. The numerical results have been derived to highlight the importance of study.

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

An increase in unplanned downtime of machines disrupts and degrades the industrial business, which results in substantial credibility damage and monetary loss. The cutting tool is a critical asset of the milling machine; the failure of the cutting tool causes a loss in industrial productivity due to unplanned downtime. In such cases, a proper predictive maintenance strategy by real-time health monitoring of cutting tools becomes essential. Accurately predicting the useful life of equipment plays a vital role in the predictive maintenance arena of industry 4.0. Many active research efforts have been done to estimate tool life in varied directions. However, the consolidated study of the implemented techniques and future pathways is still missing. So, the purpose of this paper is to provide a systematic and comprehensive literature survey on the data-driven approach of Remaining Useful Life (RUL) estimation of cutting tools during the milling process. The authors have summarized different monitoring techniques, feature extraction methods, decision-making models, and available sensors currently used in the data-driven model. The authors have also presented publicly available datasets related to milling under various operating conditions to compare the accuracy of the prediction model for tool wear estimation. Finally, the article concluded with the challenges, limitations, recent advancements in RUL prognostics techniques using Artificial Intelligence (AI), and future research scope to explore more in this area.

A hybrid reliability-centered maintenance approach for mining transportation machines: a real case in Esfahan

PurposeThe purpose of this study is to propose a hybrid reliability-centered maintenance (RCM) approach for mining transportation machines of a limestone complex, a real case in Esfahan, Iran.Design/methodology/approachCriteria for selecting critical machines were collected within literature and selected by decision-makers (DCs), and critical machines have been identified using the preference ranking organization method for enrichment of evaluations (PROMETHEE). Also, multi-criteria decision-making (MCDM) methods were used in addition to failure mode, effects and criticality analysis (FMECA) for selecting and prioritizing high-risk failures as well as optimizing the RCM performance. More specifically, the criteria of severity, detectability and frequency of occurrence were selected for risk assessment based on the previous studies, and were weighted using the analytic hierarchy process (AHP) method. Also, the technique for order of preference by similarity to ideal solution (TOPSIS) has been applied to prioritize failures' risk. Finally, the critical failures were inserted in the RCM decision-making worksheet and the required actions were determined for them.FindingsAccording to the obtained values from PROMEHTEE method, the machine with code 739-7 was selected as the first priority and the most critical equipment. Further, based on results of TOPSIS method, the failure mode of “Lubrication hole clogging in crankpin bearing due poor quality oil,” “Deformation of main bearing due to overwork” and “The piston ring hotness due to unusual increase in the temperature of cylinder” have the highest risks among failure modes, respectively.Originality/valueRCM has been deployed in various studies. However, in the current study, a hybrid MCDM-FMECA has been proposed to cope with high-risk failures. Besides, transportation machineries are one of the most critical equipment in the mining industry. Due to noticeable costs of this equipment, effective and continuous usage of this fleet requires the implementation of proper maintenance strategy. To the best of our knowledge, there is no research which has used RCM for transportation systems in the mining sector, and therefore, the innovation of this research is employment of the proposed hybrid approach for transportation machineries in the mining industry.

Importance of early fault diagnosis for marine diesel engines: a case study on efficiency management and environment

ABSTRACT The efficient management of marine vessels ensures that ships could operate smoothly and navigate at the most efficient level. Thus, the environmentally friendly voyage realises with the optimised running cost. Such a management approach could be achieved via a planned and proper maintenance strategy on the critical parts of the marine vessels’ combustion engines. Accordingly, in this study, some faults that could occur as a result of insufficient operation are combined in various scenarios and obtained results from the engine room simulator are applied in a realistic navigational route to show the results of the management performed with incorrect operations. Comparative indicator diagrams and exhaust gas outlet temperatures of the simulated scenarios are presented. As a result of the inefficient and faulty operations, in all scenarios, the amount of emission gases emitted to the atmosphere and operating expenses increase in parallel with the rise of fuel consumption.

Managing Transformers Risk through Failure Codification

The fast development and expansion of any service organizations which followed by increases in the asset numbers that’s need to have a proper maintenance strategy which should be cost effective. The aim of any strategy is to have a plan that contribute to improve asset performance by reducing downtime of asset failures.The aim of this paper is to set plan that determine the processes of creating failure code that can create a library of failure modes with its consequences and risk. This allow service provider to quickly understand the problem and any action that can be taken which have already proven by failure mode effect analysis. Also it’s identify most of the functional failures that might happened in the critical asset in the OETC’s network. In this study the transformer asset class was taken into the consideration for full failure mood and fault tree analysis.Defining failure codes can give specific instructions to complete a task to reduce the main time to wait in any failure from the total main time to repair, and any smiler failure mode from other assets the corrective action will remain consistent. The selective processes of creating a failure code give the organization a more holistic view of transformers risk which will be used to improve maintenance strategy by integrate those codes into work order system like CMMS

Detection and Segmentation of Cement Concrete Pavement Pothole Based on Image Processing Technology

Potholes are the most common form of distress on cement concrete pavements, which can compromise pavement safety and ridability. Thus, timely and accurate pothole detection is an important task in developing proper maintenance strategies and ensuring driving safety. This paper proposes a method of integrating the processing of grayscale and texture features. This method mainly combines industrial camera to realize rapid and accurate detection of pothole. Image processing techniques including texture filters, image grayscale, morphology, and extraction of the maximum connected domain are used synergistically to extract useful features from digital images. A machine learning model based on the library for support vector machine (LIBSVM) is constructed to distinguish potholes from longitudinal cracks, transverse cracks, and complex cracks. The method is validated using data collected from agricultural and pastoral areas of Inner Mongolia, China. The comprehensive experiments for recognition of potholes show that the recall, precision, and F1-Score achieved are 100%, 97.4%, and 98.7%, respectively. In addition, the overlap rate between the extracted pothole region and the original image is estimated. Images with an overlap rate greater than 90% accounted for 76.8% of the total image, and images with an overlap rate greater than 80% accounted for 94% of the total image. A comparison discloses that the proposed approach is superior to the existing method not only from the perspective of the accuracy of pothole detection but also from the perspective of the segmentation effect and processing efficiency.