Preventive Maintenance Approach Research Articles

Advanced diagnostic techniques for turbo compressors: A spectral analysis approach for preventive maintenance

This paper introduces an innovative spectral analysis control approach aimed at monitoring and diagnosing machine malfunctions to prevent potential failures. The research was conducted on a critical machine in a major industrial enterprise. The proposed method involves the use of a new indicator, called Overall Level (OL), that evaluates the machine’s condition before any operation. This study showcases practical methodologies for transitioning from time-based maintenance to predictive strategies, furnishing actionable insights into machine condition. This yields tangible advantages for the industry in terms of optimizing maintenance practices and enhancing asset productivity. Additionally, various methods, including vibration analysis, performance monitoring, and data analysis, are employed to identify the causes of issues and recommend solutions to enhance the reliability of the turbo compressor. The results provide a clear representation of the machine’s vibration state for diagnostic purposes. This noteworthy intervention underscores the potential of incorporating the measured and calculated values of the OL indicator across three specifically chosen frequency bands. To achieve this objective, the average value is employed as an indicator, contributing to the enhancement of reliability and longevity of critical industrial machinery. In this context, the novelty of the findings resides in the advanced diagnostic capabilities of the turbocompressor, thereby augmenting the efficacy of condition-based preventive maintenance for the BP 103 J turbine. The ultimate goal is to extend the equipment’s lifespan, improve the efficiency of the rotating machinery fleet, reduce maintenance costs, and enhance parameters such as availability and reliability through the support of an electronic maintenance system.

Effectiveness of Different Neurocognitive Intervention Approaches on Functionality in Healthy Older Adults: A Systematic Review.

Subtle loss of functionality in healthy older adults is considered one of the most important predictors of cognitive decline. Neurocognitive interventions are increasingly being used, from a preventive maintenance approach to functional capacity. This study evaluates the effectiveness of different neurocognitive approaches on the functionality of healthy older adults. In this systematic review (CRD42023473944), an extensive search was conducted for articles published in the last 10 years (2013-2023) in the following databases: Medline, Scopus, and Web of Science. A total of 809 trials were identified, of which 18 were considered to be eligible for inclusion in the review. The data revealed heterogeneity in sample size, measures of functional assessment, neurocognitive interventions used, number of sessions, session duration, and time. Traditional cognitive stimulation is shown to have no significant functional benefit, while other less commonly used neurocognitive interventions, such as those based on everyday cognition, are associated with more significant benefits. Moreover, it is demonstrated that although the Instrumental Activities of Daily Living scale (IADL) is the most used test in similar studies, it is not sensitive enough to detect changes in functionality in healthy elderly individuals, with other tests such as the Timed Instrumental Activities of Daily Living (TIADL) being more advantageous. Therefore, a new guideline is proposed for its use in clinical practice and research, using homogeneous study protocols and neurocognitive interventions that allow for the transfer and generalization of results in daily life.

Effect of Silane and Silicate based Penetrants against Corrosion of Steel with Partial Cover Thickness

The partial cover thickness of reinforced concrete structures near the coastline enhances the early corrosion onset, which reduces the service life. As a countermeasure under the preventive maintenance approach, to delay early corrosion onset in structures with partial cover thickness and increase durability throughout the service life, this study used silane and silicate-based surface penetrants. Mortar specimens with a partial cover thickness and embedded, specially segmented bars were prepared. Both penetrants were applied to specimens with partial cover thicknesses (20 and 7.5 mm). Further, electrochemical methods such as macrocell current, microcell current, electric resistivity, and potentiodynamic polarization curves were used to assess the corrosion resistance before and after coating. The penetration depth of silane was measured visually, and the Vickers hardness test was used for the silicate penetrant. The “equivalent cover approach” was adopted to evaluate the performance of penetrants throughout their service lives. Results revealed that the total corrosion current density decreased by 79% for specimens coated with silane and 52% for silicate penetrant, whereas no change was observed in the uncoated specimens. Based on the equivalent cover approach, the silane penetrant was determined to be most effective in delaying the corrosion onset and propagation time for cover thicknesses of 60 and 50 mm at 100 m distance against 70 mm, and for 40 and 30 mm against 50 mm at 250 m from the coastline. Further, the silicate-based penetrant was only effective for a deficient cover thickness of 5 mm against the specified cover thicknesses at a distance of 100 and 250 m from the sea coast. Doi: 10.28991/CEJ-2023-09-12-02 Full Text: PDF

Markov-chain-driven optimization of inspection-based maintenance, Part I: Models and methods

System reliability and life-cycle cost are significantly influenced by preventive maintenance (PM). PM is commonly classified into two categories: time-based maintenance (TBM) and condition-based maintenance. Another PM approach referred to as inspection-based maintenance (IBM) incorporates condition-based activities within periodic inspections and tests. Determining the optimal maintenance rate for both TBM and IBM plans, and scheduling them effectively, poses a crucial challenge. Traditional methods assess equipment importance and condition within a reliability-centered maintenance framework. Mathematical models are commonly employed to devise robust plans, but accurate modeling based on practical conditions of the system equipment remains a key challenge. This two-part paper series introduces a novel model based on the Markov chain theory, that establishes the relationship between equipment status transition rates and the risk indices. The proposed model can be used alone or in combination with risk analysis outcomes to overcome data and budget limitations. To demonstrate its efficacy, the proposed method is implemented to optimize the PM rate of high-voltage equipment within a real-world sub-transmission substation, where the results can aid in cost estimation, equipment replacement decisions, and inventory management.

The effect of corrosion on the capability of asphalt mortar to induce healing via microwave heating system

In the serviceability state of asphalt pavement, several pavement distresses are encountered; among them is the micro-cracks resulting from increasing traffic volume. Several maintenance and rehabilitation methods employed, such as patching and structural overlay are expensive and they consume a high amount of non-renewable resources. They also contribute significantly to the emission of greenhouse gases. It has been of a serious concern and interest among researchers to reduce the maintenance cost with a sustainable solution by introducing an induction healing technology via microwave heating system as a preventive maintenance approach. The microwave heating system requires a conductive particle in the asphalt pavement to accelerate the healing process. However, the steel fiber often used as the conductive particle is susceptible to corrosion under several environmental factors. Therefore, in this research, the effect of corrosion on the crack-healing potential was assessed using the water immersion method where the samples being introduced to sodium chloride (NaCl) solution to accelerate the corrosion event on copper-coated steel fiber. The formation of an oxide layer in the presence of moisture and oxygen on the surface of a copper-coated steel fiber shows a significant reverse impact on the ability of asphalt mortar to induce healing. A higher formation of rust can be detected when a higher volume of steel fiber was used. The study shows that the best performance of the copper-coated steel fiber is 3% (by volume of asphalt binder) based on the crack-healing potential, resistivity, and temperature distribution.

How to close the gap between applied strategies for infrastructure maintenance planning and the current state of research?

AbstractBridges and tunnels are the critical parts of the transport infrastructure. The efficient maintenance of their structural performance during service life requires strategies for scheduling inspections and possible structural and/or organization interventions over time. Infrastructure owners have developed such strategies that are characterized by a corrective (failure based) and predetermined preventive (e.g. time‐based or use‐based) maintenance approach. It is broadly recognized that the currently applied strategies do not provide the basis for the optimal allocation of resources into maintenance of bridges and tunnels of the traffic infrastructure. The complexity of defining efficient and proactive maintenance strategies of structures is also well recognized by the academia, and promising solutions for maintenance decision‐making based on structural reliability methods and Bayesian Decision Theory have been developed and broadly discussed in the last decades. The suggested methods have, in principle, the potential to become the basis for efficient decision making in bridge and tunnel maintenance. However, they are not yet well implemented in a practical context.In the current contribution we identify the gap between the technological developments in research and the corresponding applied knowledge in practice. Furthermore, we discuss how this gap might be closed. Special emphasis is directed to the corresponding role of standardization and education.

DATA‐INFORMED SAFETY ASSESSMENT OF EXISTING TRANSPORT INFRASTRUCTURES

AbstractBridges and tunnels are critical elements of the transport infrastructures network as they have often reached their design service life and are subject to unforeseen effects due to accidental actions, climate change or degradation processes. A thorough assessment process of the existing structures, aimed to support a preventive maintenance approach, is crucial at both European and national level. The assessment of existing concrete structures differs from the design of new assets, as the resisting models given are no longer valid with ongoing deterioration mechanisms. It is recommended to formulate the limit states considering the suspected damage mechanisms, with respect to adjusted target reliability levels in verifications. Existing assets also benefits from the availability of information related to the structural condition and the action effect on structures. As such, the EU Commission opened in 2019 the CSA call “Monitoring and safety of transport infrastructure”, granted to the IM‐SAFE project consortium and addressed in this paper, which specifically focuses on the proposal for further amendments to the existing EU standards on reliability assessment considering inspections, monitoring and testing and a new standard for preventive maintenance of transport infrastructure.

A Cleaner Pavement Preventive Maintenance Approach: Performance and Emission Impact Analysis of Slurry Surfacing Containing Electric Arc Furnace Slag

Incorporating Electric Arc Furnace Slag (EAFS) into construction materials such as hot-mix asphalt and Portland cement concrete has shown increasing wear and polishing resistance while reducing solid waste and mitigating the carbon footprint of construction engineering. Pavement surface treatment is one of the most used pavement preservation approaches. The slurry surfacing (SS) technique provides the most hot-mix asphalt-like surface texture among the surface treatment techniques. This study aimed to investigate the feasibility of using EAFS for SS by incorporating various amounts of EAFS and then analyzing the treatment's performance and the resulting carbon footprint reduction. Performance tests of SS, including the wet track abrasion test and loaded wheel test, were performed to evaluate SS's abrasion and deformation resistance. Aggregate packing degree and bonding strength based on the surface charge were also considered to understand the mechanism affecting the performance. The corresponding reduction in carbon footprint is then analyzed using the tiered hybrid life cycle assessment method to understand further the benefits of using EAFS in SS. The results revealed that mixtures incorporating EAFS could be carefully designed to perform better by increasing the packing degree and bonding strength. Also, adding EAFS into SS increased abrasion resistance by up to 18.2%, rutting resistance by up to 72.8%, and reduced the carbon footprint by up to 39.6%.

Penerapan Preventive Maintenance dan Pemasangan Emergency Lamp Dalam Minimasi Product Defect Akibat Heater Terhadap Produk Kemasan

Plant printing is a company unit tasked with fulfilling product packaging needs. In the production of printing plants from July to December, it was found that an average of 4% of products had defects (defects) exceeding the allowable tolerance limit of 2% per month, the biggest cause of problems with one of the machine components, namely the heater component. In these components found downtime of 3900 minutes for 3 months. Based on these problems, it is necessary to take preventive measures with a preventive maintenance approach by determining the schedule which is obtained from the calculation of the mean time to failure (MTTF) that the recommended maintenance interval is once every 5 days. Plus another improvement with the installation of an emergency lamp if the heater is indicated to be experiencing a mismatch. After the two approaches and improvements were carried out, a decrease in the number of product defects was obtained where the defect in the previous 6 months averaged 4% and in the following month after implementation the number of defects decreased to only 1.83%.

Pendekatan Metode ISMO Dalam Menyusun Penjadwalan Perawatan Gearbox dan Rantai Pengangkut Ore Departemen Circular Sintering PT. IRNC

PT. IRNC is a company that operates in the smelter industry. One of the company's production processes is to produce and distribute raw materials in the form of wet Ore into iron and steel products. The distribution of raw materials in this company is continuously ongoing, leading to frequent failures in the functions of the gearbox and the chain of transportation of mineral raw materials. The company has a bustling machine activity but is not associated with the preventive maintenance activities of the machine. One of the activities that can solve the maintenance problem is scheduling preventative maintenance activities and periodic repairs to the gearbox and chain carrier of raw materials. Preventive maintenance is the maintenance activity on the machine which has the purpose of preventing the occurrence of damage, or the maintenance method for the machine planned for the prevention to extend the life of the machine as well as to avoid the failure of function on the machinery that may occur during the production process. This research aims to provide recommendations for scheduled maintenance of the machine with a preventive maintenance approach with the ISMO Method. The results of the research carried out based on the type of work performed inspection (I) and small repair (S) obtained results respectively for gearbox 13 times inspections (I), and 21 times small repairs (S), while for the chain of transportation of Ore 8 times inspection and four times small reparations, and the number of workers involved 21 people, with the total labor costs of Rp—863,820 million per year.

Adoption of prescriptive analytics for naval vessels risk-based maintenance:A conceptual framework

Prescriptive analytics is a novel maintenance approach by recommending the maintainer, the right maintenance action at the right time through prioritization of maintenance based on the risk. It assists in improving the existing preventive maintenance approach by offering a dynamic maintenance plan based on risk or Risk-based Maintenance Scheduling. To date, there is no clear definition of Naval Vessels Risk-based Maintenance as well as its framework, and it has not been applied extensively in naval vessel maintenance application which is peculiar compared to its application in plant maintenance. This paper reviews Risk-based Maintenance application in industries in an approach to develop prescriptive analytics for a comprehensive and dynamic naval vessels Risk-based Maintenance decision-making framework. It aims to establish an optimal maintenance scheduling to improve naval vessels availability and reduce maintenance costs while meeting stakeholders’ expectations. Objectives and considerations are also defined for peculiarities application of naval vessels. The framework is then evaluated to improve the effectiveness of the current Preventive Maintenance practice and the inconsistency of the existing maintenance decision-making model using the expert judgement in Reliability Centered Maintenance.

Wind turbine inspection with drone: Advantages and disadvantages

The facilities on wind energy generation are increasingly finding usage areas in line with the ecologically friendly energy generation approach. One of the important activities of wind power generation facilities, which have high investment cost, low operating cost and low environmental impact is the maintenance and repair of wind turbines. A preventive maintenance approach is dominant to reduce maintenance times and eliminate lost time in wind turbines. Damage inspection of turbines has been evolved from tower crane access, rope access, camera viewing, and other applications to image with manual drones over the years. However, when these methods are evaluated within the framework of criteria such as cost, performance, occupational safety and data reliability, they are still insufficient and the need for inspection with autonomous drones arises. The advantages and disadvantages of autonomous drones used in the determination of damage in wind turbines are analyzed and the results are considered to contribute to the practitioners operating in the sector and academicians working in the field.

USULAN PERAWATAN MESIN BENDING 90 DENGAN PENDEKATAN PREVENTIVE MAINTENANCE BERDASAR METODE KEANDALAN DAN FMEA DI PT. RINNAI INDONESIA-CIKUPA

Based on sales data of PT. Rinnai Indonesia-Cikupa, there is an increase in sales of 15% stoves in 2021. Conditions that often occur are damage to production equipment including end forming, rolling, bending 90°, drilling, buffing, and stamping machines. This study aims to determine the level of performance of the bending machine 90° and proposed improvements with a preventive maintenance approach. The research was conducted during January-December 2021 using reliability analysis method and failure mode and effect analysis (FMEA) methods. Based on the reliability analysis. The overall availability (Ao) value of 88% is still below the world standard (≥90%). And from the failure mode and effect analysis, the RPN value is 352. This is due to the high downtime of the dominant component. Namely the encoder with a frequency of 86 times of damage and a downtime of 415 hours

Investigation on the stability and interaction of asphalt emulsion with waterborne acrylate as pavement maintenance coating

As a kind of organic binder, the asphalt emulsion was encouraged to be applied on the highway and airport pavement surface as a preventive maintenance approach. However, the colloid stability and interaction of the thermodynamically unstable coating was not fully investigated and thus resulted in limited application. This research attempted the waterborne acrylate as a novel modifier for emulsified asphalt coating, and investigated the colloid stability and interaction of emulsified asphalt during the phase separation. Firstly, the viscous flow behavior of the asphalt emulsion with waterborne acrylate was evaluated with viscosity test, with the consideration of the droplets interactions between waterborne acrylate and asphalt. Then laser particle size analyzer was employed to evaluate the particle size of dispersed asphalt particles in the dispersion system. Spectrophotometric analysis was conducted to evaluate the sedimentation rate of asphalt particles with a three-dimensional sedimentation kinetics model of absorbance area. The thin film of emulsified asphalt supernatant after demulsification was observed by the digital microscope. Finally, a three-stage model of curing progress in asphalt emulsion was proposed and statistical analyses were conducted to reveal the correlation between various indices. This study proved that the curing process of different emulsion systems were inconsistent. It was promoted that stability and compatibility investigation of waterborne polymer in asphalt emulsion should be taken into consideration and a substantial incorporation would cause colloid coalescence and droplet aggregation, which ultimately affected the properties and microstructure of asphalt coating.

Review of predictive maintenance algorithms applied to HVAC systems

Predictive maintenance is a preventive maintenance approach that is performed based on an online health assessment and allows for timely pre-failure interventions. It can diminish the cost of maintenance by reducing the frequency of maintenance as much as possible to avoid unplanned reactive maintenance, without incurring the costs associated with too frequent preventive maintenance. The main objective of predictive maintenance of heating, ventilation, and air conditioning (HVAC) systems is to predict when the HVAC equipment failure may occur. The benefits are numerous: planning of maintenance before the failure occurs, reduction of maintenance costs, and increased reliability. For this, the predictive maintenance of the HVAC systems is based on the historical data of the system for predicting the state of health of the system. The process of predictive maintenance application is composed of the Internet of Things (IoT) sensors that are installed inside the HVAC system, then the IoT platforms that help in collecting the signals coming from the sensors and converting them to existing databases. Afterward, the algorithms of application of predictive maintenance could be either knowledge-based approaches, physics-based approaches, or even data-driven-based approaches. A systematic literature review on the existing algorithms of HVAC predictive maintenance application is conducted in this paper to summarize the most used approach for predicting future failures in HVAC systems and to explain the benefits and limits of these algorithms.

Online-monitoring of motor operated valves

Abstract Traditionally, evaluating the running behaviour of motor operated valves (MOV) is based upon a non-continuous and demand oriented method. Gundremmingen Nuclear Power Plants has improved this strategy over the last 3 years. At MOVs within the safety system, diagnostics modules were installed for an online monitoring of active power during every run on demand. In the near future, functionality will be further enhanced in order to increase the particular knowledge, as a basis for both a safety oriented, but optimised, condition-based preventive maintenance approach and a plant life management strategy. Furthermore, during spring outage of unit B in 2002, a benchmark was implemented at four MOVs, which includes both comparison between two different kinds of power monitoring as well as active control of one of the systems.

A dynamic “predict, then optimize” preventive maintenance approach using operational intervention data

We investigate whether historical machine failures and maintenance records may be used to derive future machine failure estimates and, in turn, prescribe advancements of scheduled preventive maintenance interventions. We model the problem using a sequential predict, then optimize approach. In our prescriptive optimization model, we use a finite horizon Markov decision process with a variable order Markov chain, in which the chain length varies depending on the time since the last preventive maintenance action was performed. The model therefore captures the dependency of a machine’s failures on both recent failures as well as preventive maintenance actions, via our prediction model. We validate our model using an original equipment manufacturer data set and obtain policies that prescribe when to deviate from the planned periodic maintenance schedule. To improve our predictions for machine failure behavior with limited to no past data, we pool our data set over different machine classes by means of a Poisson generalized linear model. We find that our policies can supplement and improve on those currently applied by 5%, on average.

Stochastic Model for Integrated Preventive Maintenance Planning

Preventive maintenance planning management is modelled with stochastic approach. It is aimed to prevent stoppages and quality disorders due to disturbances, carriage and maintenance processes in production. Different maintenance policy alternatives were considered in order to develop and sustain more effective maintenance policies. The preventive maintenance process includes the cost of inspection and maintenance status, the cost of repair and other losses in the accidents with operator injuries and damage to the possible value of the situation. The stochastic model approach is applied for determine the possible period intervals of the machinery and equipment and the maintenance process analyzed and discussed in detail. The preventive maintenance approach in the maintenance planning process is aimed to develop a sustainable maintenance policy without any disturbance in the quality of production from any disturbance and disruption of the system in the long term. However, it is aimed to take preventive maintenance measures as well as to analyze the current system condition and predict future situations.

Explorative hybrid digital twin framework for predictive maintenance in steel industry

Manufacturing systems in steel industries are characterized by high complexity and high temperature and pressure conditions during production. Industries have to speed up their production to meet the market's demand for products in a fast changing economy. To prevent breakdowns in the manufacturing lines and further economic loss, steel industries utilize preventive maintenance approach and early replacement of equipment, which is expensive and not optimal. Preventive maintenance can be beneficial in the steel industry and reduce costs, if it is supported by information gathered from previous breakdowns in the production line, such as condition of equipment, environment and further data that can be collected. In this work, historical data and data collected from a digital twin representation of the manufacturing line from Pittini, a steel making industry in Italy, were utilized to gain information on the conditions before a breakdown in the production line. Furthermore, we present a cloud based framework created by utilizing the information and data for optimization and real-time driven preventive maintenance approach and remote control.

SIMULTANEOUS JOB-SHOP SCHEDULING AND MAINTENANCE PLANNING WITH ENERGY CONSIDERATION

This simulation study integrates the reliability-centered preventive maintenance approach and job-shop scheduling in a stochastic environment with energy consideration. Five reliability levels are considered ranging from 0.74 to 0.90 in the step of 0.04. The shop consists of six job types and ten distinct machines with dynamic job arrival. The system performance was measured by maximum flow-time, maximum-tardiness, mean-consumed operational energy, and mean-consumed ideal energy. The results indicate that each performance measure's value increases as the reliability levels increase. However, from a maintenance planning viewpoint, lower levels of reliability (i.e., 0.74, 0.78, and 0.82) are recommended for all performance metrics.