Proper Preventive Maintenance 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.

Oxygen needs to be met in chad: Resilience, lessons learned, challenges and perspectives

Lack of medical oxygen has contributed to a large number of avoidable fatalities globally, which have escalated as a result of the COVID-19 pandemic. At the start of the pandemic, one of the major challenges that almost all countries faced was access to oxygen to treat their patients, who were often doomed to certain death in the absence of this product. Chad had a total of two oxygen production plants, all installed in Ndjamena, with low production capacity to meet high demand. Oxygen concentrators, which are an appropriate and attractive means of administering oxygen in healthcare facilities where there is a shortage of cylinders or a distribution network, were also not sufficient, and most of them were out of use due to the lack of proper preventive maintenance. To overcome this situation, Chad is improving its capacity for medical oxygen through the installation of new Oxygen plants, maintenance and repair of broken Oxygen concentrators as well as the training of maintenance technicians. This paper shares the experience of Chad in its efforts to improve the availability of oxygen for the management of respiratory diseases, as well as the benefits and challenges to consider when planning for increased access to medical oxygen in similar settings.

Application of unsupervised learning for post-earthquake assessment of the Z24 benchmark bridge

Due to the significant number of ageing infrastructures, earthquake-induced damage assessment plays a fundamental role to mitigate the seismic risk, while enabling proper preventive maintenance and providing bridge serviceability and safety. In the wake of recent advancements in Artificial Intelligence and computational power, this paper presents a fully-automated Machine Learning (ML)-based technique for unsupervised damage detection in monitored reinforced concrete (RC) bridges. The innovative methodology is based on the autoencoder, a feed-forward neural network with the ability to reconstruct a given input from its low-dimensional representation. Considering this assumption, healthy acceleration sequences of user-defined length are firstly used to train the implemented ML model and specific damage-sensitive features are afterwards selected to quantify the differences between the original and the reconstructed input. Therefore, unknown testing data can be conceivably classified as damaged when the reconstruction loss notably deviates from the established reference conditions. The proposed approach is validated on the Z24 benchmark bridge by using pseudo-monitoring data stemming from a Finite Element (FE) model, properly calibrated based on the available field information. Specifically, the developed autoencoder is firstly trained with the acceleration responses simulated during operational conditions and collected by different sensors. Then, a realistic earthquake-induced damage scenario is introduced into the FE model in order to generate damaged acceleration time-histories to test the trained ML model. Results prove the capability of the proposed technique to detect seismic-induced damage, making it suitable for a prompt post-earthquake diagnosis.

Reliability-Centered Maintenance for Overhead Transmission Lines in Composite Power System

Reliable overhead transmission lines have a vital role in maintaining the reliability of power systems at a desirable level and ensuring the continuity of electrical energy. In this regard, a proper preventive maintenance program can ensure the reliable functioning of transmission lines. Based on the concept of reliability-centered maintenance, this paper proposes a preventive maintenance strategy that can be implemented in real power systems due to the limited data requirements. The priority of each line for preventive maintenance was obtained by two indices revealing the technical condition and importance of each line in the network. The proposed method was applied to a real transmission network in Khorasan province of Iran. In order to extract the condition index of lines, failure statistics during six recent years as well as technical data of lines were investigated. Moreover, this paper presents a two-stage method to determine the importance index of each line by evaluating the consequences of line failures on the network. The presented results show the efficiency of the proposed approach.

Condition‐based preventive maintenance with a yield rate threshold for deteriorating repairable systems

AbstractRepairable systems deteriorate with age and usage. In order to maintain acceptable reliability and prevent sudden failures, preventive maintenance (PM) is often applied to such systems. Scheduled PM actions can improve system availability and minimize losses due to breakdowns and failures. However, condition‐based PM is considered more relevant to system status than age‐based PM for deteriorating repairable systems. In this study, we consider the production yield rate as the condition variable when determining the optimal PM schedule. A non‐homogeneous Poisson process is used to describe the system deterioration, and the concept of system effective (virtual) age is also considered to make the proposed model more realistic. Three different condition‐based PM strategies are presented to provide more diverse choices for decision makers in terms of solving problems based on the situation at hand. The results show that the optimal yield rate threshold can reduce system failure while maintaining acceptable system availability and product quality with affordable costs due to a proper PM schedule. Furthermore, the results of the numerical application showed that the maintenance and penalty costs are the most sensitive to the total cost, where the adoption of the proposed PM strategies are more profitable when related costs are low.

Average height of surrounding buildings and district age are the main predictors of tree failure on the streets of São Paulo/Brazil

Tree failure is an increasingly frequent issue in cities worldwide leading to the risk of property damage, financial loss, citizen injury, and death. Assessing tree failure is a challenging task since early signs are often not visible and require a detailed evaluation of each tree, which is limiting considering the management of trees across the whole city. We used Regression Trees and Bagging to assess tree failure on the streets of São Paulo / Brazil using parameters from the gray and green infrastructure that could be easily estimated in the field to support the proper preventive maintenance of street trees. We characterized the districts’ age, average building height, tree height, canopy cover, sidewalk width, sidewalk slope, and terrain slope of 26,616 fallen trees. The Regression Tree shows 82% accuracy and reveals that building height is the main predictor of tree failure, followed by district age, sidewalk width, and tree height. The proportion of tree failure in the most verticalized areas, with on average five stories buildings or taller, is twice that observed in the entire city. Tree failure also increases in districts older than 42 years. The proportion of tree failure is 37% lower than the city’s average in relatively newer districts with low building height, where trees taller than 9.58 m are more prone to failure. These results point to possible roles of wind tunneling, shading, pollution, canopy conflicts with service cables in the urban canyons, and the natural senescence of trees in the oldest districts. The present study establishes comprehensive guidelines for effective preventive maintenance of the street trees in São Paulo.

Профілактіка пожеж в українських селах на початку становлення тоталітарного режиму в УСРР (1928-1931 рр.)

Introduction. It is a well-known fact that fi re prevention is the best way to reduce fi res and casualtiesamong the population. Not in vain that the motto of the State Emergency Service of Ukraine is – «Prevent. Save.Help», where in the fi rst place is the prevention of accidents. During 2020, 101,279 fi res were registered in Ukraine,as a result of which 1,728 people died, and material losses from fi res amounted to 12 billions 606 millions 904thousands uah. That is why turning scientifi c interest to retrospective problems of fi re prevention in Ukrainianvillages is relevant and socially signifi cant, because taking into account domestic historical experience, in myopinion, could help improve the current system of preventive fi re prevention in rural areas.Purpose. The purpose of this scientifi c publication is researching the features of fi re prevention in the villagesof the Ukrainian Socialist Soviet Republic at the beginning of the period of becoming of the totalitarian regime inthe USSR (1928-1931).Methods. The methodological basis of the article is a combination of general scientifi c methods of cognitionwith the using of scientifi c and critical analysis of sources. The subject of research determines the application ofmethods of analysis and synthesis, methods of empirical research, historical-comparative and historical-systemicmethods.Results. The author substantiate that the from 1928 to 1931, fi refi ghting in rural areas of Ukraine washandled by district and village bodies of state fi re supervision (SFS). The fi re communities of volunteers alsofought with fi res. The fi re communities of volunteers included fi re brigades of volunteers-fi refi ghters. The districtbodies of the SFS dealt with the following matters: management, control and care of fi re safety in all organizations(collective farms, state farms), institutions, warehouses, etc., which were located within a particular district. Thevillage bodies of the SFS were accountable to the district administration. Village fi re instructors and fi re chiefsas employees of rural fi re-fi ghting organizations monitored the state of fi re safety in settlements, collective farms,state farms and communes. They supervised the maintenance of fi re stations and rural fi refi ghting equipment. Theytook care of the condition of the reservoirs for the needs of fi re protection, and the entrances to them. In case ofany problems, the employees of the village bodies of the SFS appealed to the village council in order to quicklyeliminate the shortcomings.The author study that the state fi re control bodies of the USSR in the late 1920s - in the early 1930s, werecreated and approved the fi re safety rules for collective farms, state farms, machine-tractor stations, machinetractor workshops, etc. The members of rural fi re brigades and organizations relied on these instructions in thematter of proper preventive maintenance of the state of fi re safety in the villages of the USSR in the historicalperiod, which is investigating.The author substantiate that the any collective farm, state farm in the USSR had to be provided with adequatewater supply in accordance with the rules and instructions of industrial construction, approved in 1929. If ruralorganizations were located near water bodies, regardless of the water supply system, they had to equip a site withfi re barrels and pumps, to which a good paved road was to lead. It was forbidden to clutter up playgrounds anddriveways, in winter the peasants had to clear them of snow. This was followed by fi re village elders.Originality. The problems of fi re prevention in Ukrainian villages in this historical period have not beeninvestigated by ukrainian and foreign scientists. Therefore, the scientifi c novelty of this work, according to theauthor, does not cause objections.Conclusion. Despite certain problems, coordinated activities between district and village bodies of the SFSand offi cials responsible for fi re prevention in collective farms, state farms, machine-tractor stations, machinetractor workshops in the USSR in 1928-1931 led to gradual reduction of the level of «fi re» in Ukrainian villages.Scientifi c research on fi re prevention in Ukrainian villages during the heyday of the totalitarian regime in theUSSR, as well as in the postwar period, is promising

Optimisation of two-dimensional warranty region under preventive maintenance over product lifetime by considering both manufacture and consumer's point of views

An appropriate warranty period plays an essential role in consumer satisfaction and manufacturer costs. Manufacturer and consumer are two critical parts of any transaction; therefore, inattention to each of them causes dissatisfaction and financial loss. Therefore, this research proposes a model to determine an optimal two-dimensional warranty period to minimise both manufacturer's warranty servicing cost and consumer's post-warranty cost. Furthermore, to reduce the frequency of product failures, a preventive maintenance program is implemented during the warranty and post-warranty periods, respectively, by the manufacturer and the consumer. Finally, two comparisons are made to show the model effectiveness. The first one shows the superiority of the joint minimisation of the manufacturer's warranty cost and the consumer's post-warranty cost over the single minimisation. The second one shows that a proper preventive maintenance program can effectively reduce the costs of both the manufacturer and the consumer.

Optimal multi-type inspection policy for systems with imperfect online monitoring

Both online monitoring and manual inspection are widely used in identifying a system’s health state, based on which proper preventive maintenance (PM) can be carried out. The existing maintenance optimization models typically consider only online monitoring or a single type of inspection and assume they can perfectly reveal the system’s state. However, the information provided by online monitoring is never perfect, and it needs to be combined with the inspection to identify the system’s health state. Besides, there are usually many types of inspections with different costs and detecting capabilities in real applications. It is hence equally important to decide on when operators should make an inspection and which kind of inspection should be performed based on the information from online monitoring. In this paper, we formulate the multi-type inspection decision-making problem within the partially observable Markov decision process (POMDP) framework. We establish some structural properties for the optimal multi-type inspection policy. The value iteration and the λ-minimization algorithm are combined to obtain the optimal solution. A case study is provided to illustrate the optimal inspection policy and its advantage over some existing policies.

Maintenance downtime evaluation in a process bottling plant

PurposeDowntime is a process parameter that substantially impacts on the operating hours and results in production losses, thus motivating maintenance engineers to control process plants. Notwithstanding, the impacting nature of process equipment failure on the operating hours in bottling plants remains inadequately examined. In this paper, the cause-and-effect analysis was used to establish the root cause of the downtime problem and Pareto analysis employed to justify the greatest opportunities for improvement in reducing downtime and increasing reliability levels. Weibull analysis is then conducted on the industrial setting. Novel aspect ratios are proposed.Design/methodology/approachUsing the Weibull failure function of machines as a principal facilitator to produce failure predictions, the downtime behaviour of a process plant was modelled and tested with practical data from a bottling process plant. This research was conducted in a Nigerian process bottling plant where historical data were examined.FindingsThe analysis of the results shows the following principal outcome: First, the machines with the highest and least downtime values are 2 and 5, respectively, with correspondingly mean values of 22.83 and 4.39 h monthly. Second, the total downtime 92.05 and 142.14 h for the observed and target downtime, with a coefficient of determination of 0.5848 was recorded. Third, as month 1 was taken as the base period (target), all the machines, except M5 had accepted performance, indicating proper preventive maintenance plan execution for the bottling process plant. Availability shows a direct relationship between the failure and uptime of the machines and the downtime impacts on production. Two machines had random failure pattern and five machines exhibited a wear-out failure pattern and probably due to old age and wear of components in the machines.Originality/valueThe major contribution of the paper is the Weibull modelling in a unique application to a bottling plant to avoid current practices that use reliability software that is not easily accessible.

Performance evaluation of continuous miner based underground mine operation system: An OEE based approach

Abstract CM is a globally renowned machine, designed to work as a mass production technology for underground coal. Different major coal producers across the globe are using this technology for decades to produce underground coal efficiently. India is also one of the major players globally in the arena of coal production and adopted this cutting edge technology since last decade by implementing at. few of the selective underground coal mining projects. Performance of CM technology is influenced by the geo-mining condition, fleets of other ancillary units and reliability of subsystems while implementation of this system depends largely on the extent of reserve. These aspects generate a scope of large scale research and development in this field. Overall Equipment Effectiveness (OEE) is the parameter to benchmark the equipment performance globally. OEE is the product of equipment availability, performance and product quality. This mining machine based paper focuses on the Overall Equipment Effectiveness (OEE) of the complete CM based operation to identify the vulnerable systems, which helps to design proper preventive maintenance programme. The CM based system is divided into few subsystems, such as; electrical, cutter, gathering arrangement, traction, hydraulic, chassis, feeder breaker, shuttle car, CM conveyor and out-bye conveyor. The downtime data used for this analysis is collected from an underground coal mine situated in the central part of India, belongs to one leading coal producing company of the country. From analysis it was found that, electrical systems and conveyors are among most vulnerable systems and deserves more care during maintenance. On the basis of these results recommendations are made to redesign the Preventive Maintenance Programme, in order to avoid the lower availability as well as lower OEE.

Reliability and Maintenance Analysis of Unmanned Aerial Vehicles.

This paper focuses on the development of a new logistic approach based on reliability and maintenance assessment, with the final aim of establishing a more efficient interval for the maintenance activities for Unmanned Aerial Vehicles (UAV). In the first part, we develop an architectural philosophy to obtain a more detailed reliability evaluation; then, we study the intrinsic reliability at the design stage in order to avoid severe critical issues in the UAV. In the second part, we compare different maintenance philosophies for UAVs and develop the concepts of preventive and corrective maintenance that consider the system subjected (until real “hard failure”) to partial performance degradation (“soft failure”). Finally, by evaluation of the uncertainty through the confidence interval, we determine the new soft failure limits, taking into account the general knowledge of the systems and subsystems in order to guarantee the proper preventive maintenance interval.

Design of Fractal Features-Based Partial Discharge Pattern Recognition using Multi Support Vector Machine Method

Partial Discharge (PD) is one of the causes of insulation deteriorisation mode and impacts on the reliability of high voltage equipment. Therefore, PD measurement is used for diagnostic technique of high voltage equipment. Diagnostic output of high voltage equipment contain information about PD type, PD cause, PD location and PD severity. after identification, a proper preventive maintenance pattern can be performed. Therefore PD pattern recognition system is very important on PD diagnostic system to recognize the PD pattern and determine the level of hazard that occurs in specimen object or high voltage equipment‥ In this paper, PD pattern recognition system is designed with fractal geometry approach and support vector machine (SVM) algorithm. The coding and programming of graphical user interface of the application is done. Each PD type and hazard level on various insulating materials (solid, liquid and gas) have the dimensions of the fractal and the lacunarity. The type of PD (void, corona) and its danger level (bad, fair and good) can be identified with the support vector machine (SVM)

Reliability analysis comparison on punching tool sets due to different maintenance decisions: a case study from the pulp manufacturing industry

This paper presents a case study describing reliability analysis comparisons due to different maintenance decisions for punching tool sets that are used for perforating in a line-making process. Field maintenance data for punching tools are classified according to different maintenance decisions. The data were analyzed further to identify the best fit distribution models. The related reliability characteristics are then revealed and discussed. The reliability analysis results of four different major maintenance decisions shows that the short-term view indicates no significant differences of mean-time-to-failure (MTTF) for the equipment. However, the range (lower and upper limit values) for MTTF for each replacement decision package can be used by a maintenance engineer for equipment spare parts planning. The results found that the failure rate of the equipment was due to replacement decisions packages that are in a deteriorating state. Thus, the maintenance engineering team noticed that the determination of optimum preventive maintenance time is beneficial and cost effective. This study concludes the importance of reliability analysis to better understand the probability nature of the subject under consideration, i.e., it helps a technical team to utilize proper preventive maintenance and spare parts planning.

JOINT MAINTENANCE AND SPARE COMPONENT PROVISIONING POLICY FOR k-OUT-OF-n SYSTEMS

This study develops an analytic framework for joint modeling of age-based preventive maintenance and (s, Q) spare component provisioning policy for k-out-of-n:G systems with i.i.d. nonrepairable components. We assume that during system maintenance, instead of discarding the whole system, only failed components are replaced by spares if the on-handspares are sufficient. The objective is to determine a proper preventive maintenance interval and the ordering parameters to minimize the undiscounted long run average cost under a system availability constraint. Our study sheds light on the lumpy demand scenario in the problems of joint modeling of preventive maintenance and spare provisioning.

Assess the effects of different operations policies on warehousing reliability

In this study, an assessment model for analysing the reliability of a warehouse system, focusing on resource capability, under given combinations of storage, routing, batching and zoning process policies, is proposed. A reliability assessment model is developed in order to evaluate the effect of different combinations of operations policies on warehouse reliability. To better reflect the real order pick up operations at the warehouse, a simulation model based on the operation scenarios of a Hong Kong logistics service company is developed. The simulation results are used for supporting the evaluation of warehouse reliability through using the proposed reliability assessment model. The most important finding indicates that order batching together with zoning policies yields greater warehouse reliability particularly when the daily order volume is large. In addition, another important research finding shows that warehouse performance is not constant and can drop when certain combinations of operations policies are adopted. The degrading rate of the resource (forklift) when used according to a given combination of polices is also defined by the proposed assessment method. Hence, the result helps logistics service providers enhance resource durability through modifying operations policies and by implementing proper preventive maintenance policies within a dynamic operations environment.

Simulation Analysis of the Effects of Maintenance Policies on Manufacturing Line Productivity

A manufacturing line consists of a series of machines, which are subject to failures due to component deterioration and other random causes. While failures due to random causes are inevitable and need corrective maintenance (CM), failures due to component deterioration can be avoided by preventive maintenance (PM). Both types of maintenances can cause significant production losses, particularly if a proper PM policy is not practiced. Productivity calculations of multi-stage lines with storage units and maintenance operations can be quite complex. Furthermore, closed form solutions in equation forms are not possible when both preventive and corrective maintenances are incorporated into the model. In this paper, a simulation model is presented for a multi-stage line and line productivity is evaluated under different operating conditions and maintenance policies.

BAYESIAN-NETWORK-BASED HYDRO-POWER FAULT DIAGNOSIS SYSTEM DEVELOPMENT BY FAULT TREE TRANSFORMATION

Currently, fault diagnosis of reservoir facilities relies mostly on check-list evaluation. The results and qualities of evaluation are limited by experience and ability of the evaluators, which may not achieve the goal of systematic assessment in a consistent manner. To overcome the limitation of the traditional approach, this research develops a fault diagnosis and evaluation system for reservoir facility by utilizing multi-state Fault-Tree Analysis (FTA) technique, in conjunction with Bayesian Networks (BN) which incorporate expert experiences through lateral linkages among BN nodes and weighting factors. The system has been used to analyze and verify against three hydro-power systems currently in operation. It was found that through BN analysis the fault trend is consistent to that from historical data analysis via Weibull distribution. This indicates that the transformation of a multi-state Fault-Tree (FT) and BN is reasonable and practical. Based upon the analysis of BN by inputting prior information of the hydro-power systems, the probabilities of fault occurrences are effectively computed based on which proper preventive maintenance strategies can be established.

Failure modeling and optimizing preventive maintenance strategy during two-dimensional extended warranty contracts

Offering extended warranty (EW) contracts for products such as automobiles is a good source of revenue for manufactures, insurers and third party companies. However, difficulties in the modeling of product’s failure process and assessing corrective and preventive maintenance actions’ effects on the reliability of product enforce the service providers to propose limited EW contracts with simple “minimal repair at failures” servicing strategy. In this paper for a product sold with a two-dimensional warranty, we model the failure process of product, the effect of imperfect preventive maintenance (PM) and corresponding servicing cost in terms of product’s age and usage. Then, we propose a mathematical optimization model to derive optimal number and degrees of preventive repairs to minimize the EW provider’s servicing cost. We also provide some guidelines to help the EW provider to design flexible EW contracts and determine their corresponding optimal maintenance strategies. To reproduce an illustrative numerical example, we use the failure history of a commercial vehicle produced in a plant in Iran. The provided results reveal that considering proper preventive maintenance strategy during the EW period may effectively reduces the cost of EW servicing.

Is Preventive Maintenance Worth the Time, Effort, and Training?

A common challenge for water and wastewater facilities is a lack of proper preventive maintenance (PM) being conducted or the frequency at which it's performed. This article lists maintenance activities (usually recommended by the manufacturer) to keep the equipment working properly. An important part of PM is to regularly inspect all equipment and address problems as they arise. The article provides examples of catastrophic consequences that can arise when proper PM is not conducted.