Corrective Maintenance Actions Research Articles

Design and Implementation of an IoT-Based Electric Motor Vibration and Temperature Disruption Handling System

Faults in three-phase electric motors often occur in power plants. Unfortunately, the disturbances are unpredictable and reduce unit performance. This paper presents an Internet of Things (IoT) based intrusion handling system for secure and economic data communication in industrial fields. The build system can monitor and control with FLM (First Line Maintenance). FLM is a minor corrective maintenance activity carried out while the unit is operating with the provision of simple equipment. Device design and testing using the ESP8266 microcontroller module to obtain motor vibration, current, and temperature information for motor conditions supported by the application as a user interface. The result prevents further damage to the electric motor by adding an external fan and a grease pump that serves for additional engine cooling. This research contributes to improving the performance of the generator at PT. Indonesia Power UJP West Java II Palabuhanratu is one of the steam power plants in West Java, Indonesia

A multi-stage imperfect maintenance strategy for multi-state systems with variable user demands

The main purpose of this paper is to schedule an imperfect maintenance policy for a single unit multi-state system (MSS) exhibiting more than two output states in the duration of its multiple operation stages. Firstly, a nonhomogeneous continuous time Markov chain (NHCTMC) is introduced to describe the state evolution process when the number of external shocks is modeled by a nonhomogeneous Poisson process (NHPP), and state residence probabilities of the Markov process are derived via a proposed recursive algorithm (RA) in solving the Chapman-Kolmogorov (CK) differential equations. Based on those modeling approaches, reliability measures such as the survival function and mean time to first failure (MTTFF) are evaluated where the randomly varied user demands reflecting real industrial applications are taken into consideration. We subsequently put forward a multi-stage imperfect maintenance policy by dividing system output states into three subsets concerning different degrees of degradation, incorporating a condition-based maintenance (CBM) action and a corrective maintenance (CM) activity in each operation stage. It is assumed that the CBM is a minimal repair and the effectiveness of CM is imperfect. The system will not be completely replaced by a brand new one until the number of failures reaches N in light of the investigated maintenance policy, where the expected profit in per renewal cycle is maximized to seek the optimal N analytically. An illustrative example of a bulk power electric system is presented to verify the proposed idea numerically, in which three common types of demand distributions are introduced.

Expert System Detects Problems of Inkmaker Machine And Mixer With Forward Chaining

Industrial growth is characterized by the ability to progress in the field of production both in the type of industry, increasing the quality and volume of production activities. Machine is one of the factors of production which determines the smoothness of a production process. In order for the production process to run efficiently, a certain part of the company that is needed to support the maintenance and repair of the machine is called Maintenance Engineering. PT. Segwerk Ind is a multinational company engaged in printing inks for the packaging industry with imported raw materials such as dyes, varnish. The problem of how to maintain the condition of the machine so that it is always prime when production activities take place, Trouble machines are indeed one of the factors that can make a company go bankrupt, such as the production process is hampered, a long machine down time, so that customers will replace their suppliers because they do not want to risk business they are stunted. Therefore, for the success factor of a company's production process, it is required to have appropriate preventive and corrective maintenance activities as well as the problem of lack of competent human resources, therefore we need tools that can solve these problems. The results displayed by making expert system software show that the ink machine problem solving can be known based on the historical machine so that the downtime faced by PT. Segwerk Ind can be pressed as little as possible against machines in the ink industry such as ink printing machines.

Risk Analysis of Combustion Failure in Gas Turbine Start Process in PLTGU Unit PT.X

The peaker power plant is a power plant that is operated when the network is at peak load. Being a peaker power plant has an impact more often start- stop for operation of gas turbine. So, the readiness of the gas turbine to start any time is the core business of the peaker power plant. But in reality, start failure still occurs in the generating unit. Start failure can be caused by combustion failure. In 2017 and 2018 years, there was a start failure caused by combustion system failure total 10 out of 19 cases internal start failure. A lot of disadvantages of start failure for the company like very much loss of potential production revenue, loss of fuel consumption, electricity usage for supporting equipment, materials of gas turbine, failure to achieve KPI’s company, decrease in customer satisfaction and much more. So, it must be minimized or eliminated. Until now, maintenance for start failure still corrective maintenance. Corrective maintenance is an unscheduled, unpredictable, unplanned or unprogrammed maintenance action based on an event at a certain time so the potential loss of company’s income large. One effort to reduce corrective maintenance activities with preventive maintenance. However, to do preventive maintenance, it is necessary to conduct in-depth studies and analysis so that preventive maintenance is carried out effectively and on target. For this reason a risk analysis of the combustion failure with Failure Mode and Effects Analysis (FMEA) method need to do, so the preventive maintenance steps can be obtained to mitigate the risk of start failure in gas turbine power plant. From the result of FMEA, there are two extreme risk from combustor system, igniter and fuel valves. So, preventive maintenance applied to them. It can provide a large potential revenue for industry and company reputation

Usulan Interval Preventive Maintenance dan Estimasi Biaya Pemeliharaan Menggunakan Metode Reliability Centered Maintenance dan FMECA

ABC is a company engaged in the production of automotive spare parts and accessories, especially motorcycles. The problems faced by the company, there is frequent damage to the CNC Milling A. The company applies preventive maintenance and corrective maintenance activities, but these maintenance activities have not yet run optimally. Therefore, a maintenance system is developed to improve machine reliability. The method used is Reliability Centered Maintenance (RCM), with the aim of determining the optimal maintenance interval and estimation of efficient maintenance costs. RCM is done by analyzing the Failure Modes Effect and Critical Analysis (FMECA). The results of the FMECA analysis are in the form of a Risk Priority Number (RPN) that shows the components of rail bearings, spindle bearings, and hoses as critical components of the system. Through the RCM method, the maintenance policy is generated in the form of 2 scheduled on-condition tasks, 2 scheduled restorations, and 3 scheduled discard tasks with maintenance time intervals in accordance with the task category and can save maintenance costs of IDR 175.602.300.

Joint optimisation of operation and maintenance policies in an urban ropeway transport systems context

PurposeThe purpose of this paper is to propose a stochastic optimisation model for integrating service and maintenance policies in order to solve the queuing problem and the cost of maintenance activities for public transport services, with a particular focus on urban ropeway system.Design/methodology/approachThe authors adopt the following approaches: a discrete-event model that uses a set of interrelated queues for the formulation of the service problem using a cost-based expression; and a maintenance model consisting of preventive and corrective maintenance actions, which considers two different maintenance policies (periodic block-type and age-based).FindingsThe work shows that neither periodic block-type maintenance nor an age-based maintenance is necessarily the best maintenance strategy over a long system lifecycle; the optimal strategy must consider both policies.Practical implicationsThe maintenance policies are then evaluated for their impact on the service and operation of the transport system. The authors conclude by applying the proposed optimisation model using an example concerning ropeway systems.Originality/valueThis is the first study to simultaneously consider maintenance policy and operational policy in an urban aerial ropeway system, taking up the problem of queuing with particular attention to the unique requirements public transport services.

A new approach to maintenance optimisation of repairable parallel systems subject to hidden failures

Maintenance policies are developed for decision-making about repair and maintenance of deteriorating parallel systems consisting identical components whose failures are detected only by inspections. Inspections at periodic times reveal the true state of components and preventive and corrective maintenance actions are carried out in response to the observed components state. The decision process is driven by the excursion of a state process regulated through an age reduction model. The modelling approach allows a wide class of models to be considered. Assuming a threshold-type policy, the article aims at minimising the long-run average maintenance cost per unit time by determining appropriate inspection intervals and a maintenance threshold. Using the renewal-reward theorem, the expected cost per cycle and expected cycle length emerge as solutions of equations, and a recursive scheme is devised to solve them. We illustrate the procedure for the case when the components’ lifetime conforms to the Weibull distribution. Furthermore, a sensitivity analysis is performed to examine the effect of model’s parameters. The unified structure developed allows different scenarios to be explored.

Allocation of effective maintenance limit for railway track geometry

The objective of this study has been to develop an approach to the allocation of an effective maintenance limit for track geometry maintenance that leads to a minimisation of the total annual maintenance cost. A cost model was developed by considering the cost associated with inspection, preventive maintenance, normal corrective maintenance and emergency corrective maintenance. The standard deviation and extreme values of isolated defects of the longitudinal level were used as quality indicators for preventive and corrective maintenance activities. The Monte Carlo technique was used to simulate the track geometry behaviour under different maintenance limit scenarios and the effective limit was determined which minimises the total maintenance cost. The applicability of the model was tested in a case study on the Main Western Line in Sweden. Finally, a sensitivity analysis was carried out on the inspection intervals, the emergency corrective maintenance cost and the maintenance response time. The results show that there is an optimal region for selecting an effective limit. However, by considering the safety aspects in track geometry maintenance planning, it is suggested that the lower bound of the optimal region should be selected.

Detection of Faults and Drifts in the Energy Performance of a Building Using Bayesian Networks

Despite improved commissioning practices, malfunctions or degradation of building systems still contribute to increase up to 20% the energy consumption. During operation and maintenance stage, project and building technical managers need appropriate methods for the detection and diagnosis of faults and drifts of energy performances in order to establish effective preventive maintenance strategies. This paper proposes a hybrid and multilevel fault detections and diagnosis (FDD) tool dedicated to the identification and prioritization of corrective maintenance actions helping to ensure the energy performance of buildings. For this purpose, we use dynamic Bayesian networks (DBN) to monitor the energy consumption and detect malfunctions of building equipment and systems by considering both measured occupancy and the weather conditions (number of persons on site, temperature, relative humidity (RH), etc.). The hybrid FDD approach developed makes possible the use of both measured and simulated data. The training of the Bayesian network for functional operating mode relies on on-site measurements. As far as dysfunctional operating modes are concerned, they rely mainly on knowledge extracted from dynamic thermal analysis simulating various operational faults and drifts. The methodology is applied to a real building and demonstrates the way in which the prioritization of most probable causes can be set for a fault affecting energy performance. The results have been obtained for a variety of simulated situations with faults deliberately injected, such as increase in heating preset temperature and deterioration of the transmission coefficient of the building's glazing. The limitations of the methodology are discussed and are translated in terms of the ability to optimize the experiment design, control period, or threshold adjustment on the control charts used.

Optimal Research on Equipment Maintenance Unequal Time-Interval Based on Simulation

The paper deals with systems suffered from gradual deterioration. To avoid functional failures and disastrous consequences, such systems should be monitored and preventively maintained. And to promote the maintenance effectiveness, an unequal time-interval maintenance policy was brought forward to substitute fixed-interval preventive maintenance strategy. At the same time, preventive and corrective maintenance actions are both assumed to be imperfect, and their impact on the system is expressed by the increment of failure rate. The case study shown that the unequal time-interval maintenance policy proposed in the paper is of help to promote the availability of the system.

Optimal condition-based and age-based opportunistic maintenance policy for a two-unit series system

A new optimal maintenance policy considering the condition monitoring and age information is considered for a two-unit repairable system. Unit 1 is the core component of the system, which is subject to soft failure observed only through inspections. Unit 2 is subject to hard failure and the age information representing usage of unit 2 is available. Unlike the previous maintenance models where maintenance actions can be initiated only at the inspection epochs, we assume that corrective maintenance action would be carried out immediately when unit 2 fails. Furthermore, a preventive maintenance is performed on the units if the deterioration level of unit 1 or the usage of unit 2 exceeds certain maintenance threshold for that unit. Under this maintenance strategy, when one of the two units is correctively or preventively maintained, there is an opportunity to maintain the other unit. The maintenance problem is formulated and solved in the semi-Markov decision process framework. A formula for the average maintenance cost is derived and the optimal thresholds for preventive and opportunistic maintenance of the two units minimizing the long run expected average cost are determined. A practical example of a two-unit wind turbine system is provided, and a comparison analysis with other maintenance policies shows an outstanding performance of the proposed policy.

Automated groove identification and measurement using long short-term memory unit

Transverse grooves have been widely used on airport runways to improve their drainage capacity and skid resistance. Therefore, regular measurement and evaluation of groove dimensions on airport runways are significant for runways to improve skid resistance and eliminate hydroplaning risks. However, there are few effective methods that are able to automatically and accurately measure groove dimension. This study introduces a new method for automated groove identification and measurement using Long Short-term Memory (LSTM). The GrooveNet is designed to identify the potential dip and determine two endpoints of the identified dip. Subsequently, dip dimension is calculated according to FAA AC No. 150/5320-12C. Finally, the modified Naïve Bayes classifier is proposed to distinguish grooves and slab joints. Results indicate that the proposed methodology (including GrooveNet and modified Naïve Bayes classifier) is more robust and accurate in runway groove identification and measurement. With the proposed approach, operators of airfield runway pavements have a robust tool to conduct groove safety evaluation and further provide corrective maintenance actions for the unsafe runway grooves.

Experimental Study on Online Measurement of Unburned Carbon in Pulverized Fuel Boilers by Thermal Oxidation of Ash

The unburned carbon (UC) in the ash in Indian power plants is presently measured offline by manually collecting the samples and analyzing it by loss on ignition or thermogravimetric analysis methods. The manual collection and offline analysis of samples take 6-8 h. By this time, the existing operating conditions of the boiler may be different from the conditions when the samples were collected. The combustion optimization and corrective maintenance activities can be performed immediately by plant operation and maintenance team if the UC values are available online along with furnace flame intensity and the percentage of CO in flue gases at economizer outlet. The presence of high amounts of the UC in ash decreases the boiler efficiency and reduces the utilization of ash in cement industries. In this paper, 20 ash samples were collected from five Indian thermal power plants of range 150-500 MW. An experimental setup has been developed for the measurement of the UC using the percentages of CO2 and CO obtained by thermal oxidation of ash. The experimental results were compared with the laboratory results. The maximum deviation between experimental and laboratory results was within the range of ± 0.30. The percentages of the UC is more accurate when CO2 and CO both are considered in comparison to the present existing online measurement based on CO2 only.

Log-based predictive maintenance in discrete parts manufacturing

The performance of discrete parts manufacturing systems is heavily influenced by unplanned machine breakdowns. Predictive maintenance allows for the conversation of unplanned machine breakdowns to scheduled corrective maintenance actions. We present a data-driven approach for estimating the probability of machine breakdown during specified time interval in the future. Machine learning algorithms are utilized for a specific use-case which is based on real-world data-sets including machine log messages, event logs and operational information. The paper describes applied data-mining, feature-extraction and machine learning methods and concludes with results indicating that machine failures can be reliably predicted up to 168 hours in advance.

Integrated production, statistical process control, and maintenance policy for unreliable manufacturing systems

The objective of this paper is to develop an integrated approach for the joint control of production, maintenance and quality for batch manufacturing systems. We consider such systems that are subject to degradation which is at the origin of the production of defective units. The quality control of lots produced is performed using an ‘x-bar’ control chart. This graphical tool will allow estimating the quality of the batch being produced and possibly undertake perfect preventive or corrective maintenance actions on the production system. A buffer stock is built to maintain continuity of supply during maintenance actions. The incurred total cost includes setup cost, inventory costs, the cost of unused products, maintenance costs and quality costs. Decision variables include the buffer stock size, the sample size, the sampling interval, the surveillance and the control limits of the control chart. Numerical experiments and sensitivity analyses are provided to evaluate the effectiveness of the proposed control policy and the robustness of the solving approach.

Optimal maintenance plan for two-level assembly system and risk study of machine failure

This paper deals with the optimisation of two-levels assembly system planning. This system is composed of a single machine, inventories at levels 1 and 2 for stock keeping components to assembly and final assembled product. It assumed that the machine processes all assembly operations and is subject to random failure. A mathematical model is developed to incorporate a supply planning for two-level assembly systems under stochastic lead times and breakdowns machine. A preventive maintenance plan is carried out to reduce the frequency of the corrective maintenance actions. This work has double goals. The first one is to find the optimal order release dates for the different components at level 2 and the optimal preventive maintenance plan. The second one is to quantify the risk due to machine failures which have an impact on the lead-time of the finished product. To consider the maintenance actions, preventive maintenance actions are perfectly performed to restore the machine to state “as good as new”, minimal repair is considered at failure. The model minimises the total cost, which is the sum of inventory holding cost for components at levels 1 and 2, backlogging and inventory holding cost for the finished products and maintenance costs. To illustrate the effectiveness of the proposed model, different meta-heuristics are applied; the genetic algorithm shows the most suited to our analytical model, the optimal release date founded by this algorithm allows finding the optimal preventive maintenance plan. The obtained optimal maintenance planning is used in the risk assessment in order to find the threshold repair period that avoids lost profit.

NDT for bridges durability assessment on urban-industrial environment in Brazil

PurposeThe limitation of bridges’ operation can cause serious social, environmental and economic losses. Therefore, the monitoring and maintenance actions of these structures must be efficient and periodic, especially for bridges located in aggressive environments, such as urban-industrial centres, where the higher volume of carbon dioxide emissions favours carbonation induced corrosion. The purpose of this paper is to analyse the utility of including non-destructive testing (NDTs) to bridges assessment in that regions as a way of obtaining more in-depth information on the conditions of the material composing the structure.Design/methodology/approachFirst, the main bridges’ damages were detected by visual inspection. Then, based on the observations of bridges design, environment and main damages, an NDT programme was executed including surface hardness, ultrasonic pulse velocity test, pH indicator spraying, half-cell potential measurements and concrete resistivity tests.FindingsIt was observed that, for the studied cases, the carbonation did not present harmful depths, except for the structural elements where segregation and wear of the concrete were noticed. NDTs, associated with visual inspection, indicated the regions where corrective or preventive maintenance actions were actually needed, bringing greater security to the decision maker in regions where repairs are unnecessary or could be postponed.Originality/valueThis paper highlights the contribution of NDTs application in structures in urban-industrial regions where the main mechanism of deterioration is carbonation-induced corrosion, demonstrating the importance of these methods in the rational decision making of investments for maintenance.

Analysis of lubrication oil towards maintenance grouping for multiple equipment using fuzzy cluster analysis

Maintenance of similar multiple equipment is challenged by the complexities brought by respective maintenance needs and intervals for each equipment. Therefore, maintenance scheduling and planning becomes expensive and time intense, affecting productivity and profitability of the plant. Organizations are embracing the need to enhance maintenance planning by evaluating equipment characteristics which potentially offer benefits from reduction in maintenance costs and downtime to avoiding of unplanned shutdowns and efficiency maximization. To address this need, this study proposes a methodology that groups equipment with similar characteristics picked from lubricant analysis using fuzzy cluster analysis. Grouped equipment tend to require similar corrective and preventive maintenance (PM) actions enhancing maintenance planning and equipment availability. To validate this framework, lubricant analysis data for seventeen medium speed engines (MSE) of a thermal power plant is utilized where the derived clusters are subsequently used to group the engines. The framework offers benefits towards reduction of maintenance cost, improved planning and overall availability of the plant and equipment.

Long-Term Maintenance Scheduling and Budgeting in Electricity Distribution Systems Equipped With Automatic Switches

Maintenance management, as a key part of the asset management practices, plays a vital role in enhancing the reliability of the electricity distribution systems (EDS) where realizing a highly reliable EDS is being attributed higher and higher criticality in today's modern society. In this paper, a new approach is proposed to improve the reliability of EDS through optimal scheduling of preventive maintenance (PM) tasks and allocation of automatic switches. The suggested objective for optimal PM schedules and switch allocation is to minimize a combination of customer-based (system average interruption duration index and system average interruption frequency index) and cost-based reliability indices. The total reliability cost includes those associated with the corrective maintenance actions, PM tasks, and automatic switch investments. The proposed approach is implemented in three different scenarios: 1) switch placement, 2) PM tasks scheduling and budget management, as well as 3) a joint switch and PM tasks decision making. The aforementioned scenarios are applied on a standard reliability test system (RBTS4) followed by multiple sensitivity analysis to further demonstrate the efficacy and performance of the proposed framework.

Optimal imperfect maintenance cost analysis of a two-component system with failure interactions

This paper considers a two-component system with failure interactions. Component 1 is repairable and component 2 is non repairable and is subject to an increasing degradation. One considers two different shock models. In model 1, component 1 failure causes random gradual damages to component 2 and increases its degradation level. In model 2, component 1 failure may cause the failure of component 2 with a given probability while the failure of component 2 is catastrophic and induces the failure of the whole system. For each model, three maintenance policies are proposed. In each policy, component 1 undergoes imperfect corrective maintenance actions and component 2 is perfectly repaired. An explicit expression of the long run average maintenance cost is developed and the existence of the optimal policy is discussed. Numerical examples are given to illustrate the effectiveness of the proposed models.