Breakdown Maintenance Research Articles

Condition monitoring of TPU/UPM Fiat tractors using ICP and OES techniques

The use of agricultural tractors for various operations within a day calls for adoption of condition monitoring techniques in maintenance management strategies especially regarding engine lubricating oil change interval and predicting components replacement time before premature failure. Six Fiat tractors from TPU/UPM were monitored over three oil change intervals and oil samples taken for analysis using inductively coupled plasma and optical emission spectroscopy (ICP & OES). Twenty-three elements including wearing elements from engine, additive elements from oil manufacturers, and contaminating elements from the environment were determined from 39 samples collected. Results indicated significant differences for 15 elements (Al, B, Ca, Cr, Cu, Pb, Mg, Mo, P, Ti, V, Zn, Si, K, and Na) belonging to wear, additive, and contaminating sources. This necessitated the use of condition monitoring for individual tractors instead of the general rule of preventive maintenance or even breakdown maintenance performed after the occurrence of failure.

Adaptive and noncyclic preventive maintenance to augment production activities

PurposeProduction schedules, if not met as per timelines may result in heavy losses to a company in terms of its standing and the overall profit. Production scheduling is generally planned by not taking preventive maintenance schedules into consideration. Most of the plants allocate discrete hours/time for preventive maintenance activities. These hours allocated for preventive maintenance will be in addition to the hours which would be lost during breakdown maintenance. These lost hours may be reduced if production scheduling and preventive maintenance activities are integrated. This advocates that we need to devise a methodology which can take care of lost hours.Design/methodology/approachAdaptive and noncyclic maintenance strategy describes the modification of existing maintenance practices, policies and procedures to meet new dynamic tasks/opportunities. It demands a high degree of flexibility and mental agility from maintenance staff members. The maintenance team has to be on a lookout for an opportunity message received from the central server and has to act promptly. The moment an opportunity arises, a message is forwarded to a central maintenance server (opportunity is captured). The central server then assigns individuals/team, based on their expertise and the maintenance task due on that machine/equipment. This action is completely automated and implemented without delay.FindingsThe total man-hours saved by executing adaptive and noncyclic preventive maintenance methodology comes to 705 h during 15 days on 30 machines installed in three different sections. There was a contribution of 71 innovative ideas from the repair teams. Out of these 71 innovative ideas, 16 were found suitable for execution. A quantum jump in the morale and motivation of the maintenance team was noticed from the feedback forms. Mutual understanding and respect for each other among employees has been enhanced. The optimization of resources and infrastructure including tools, gauges, testing equipment, etc. could truly be attained.Practical implicationsThe developed adaptive and noncyclic preventive maintenance model assists in capturing lost hours and make the system proactive and lean. The suggested model optimizes the preventive and predictive maintenance activities and results in substantial saving of efforts, manpower, resources and allocated budget.Originality/valueThe adaptive and noncyclic preventive maintenance model discussed in the article is a novel approach for the optimization of resources. The technique assists in capturing lost hours and utilization of these hours for preventive maintenance tasks. The model will also encourage creative and innovative ideas from employees and take the organization toward Continual Maintenance Optimization.

Design of an intelligent decision-making system for maintenance practices using fuzzy inference system in northern Indian SMEs

PurposeThe paper aims to analyze the significance of key maintenance practices in effective implementation of the performance parameters in Northern Indian small and medium-sized enterprises (SMEs).Design/methodology/approachThe present study deploys a fuzzy inference approach (fuzzy logic toolbox) to test the effective implementation of maintenance practices in SMEs. The significant maintenance factors are defined from applicable literature for this reason and validated by industry experts.FindingsA model built into a fuzzy rule viewer and a surface view tool built into a fuzzy toolbox in MATLAB has highlighted the successful implementation of maintenance practices in Indian SMEs. The study highlights that corrective maintenance, general maintenance, preventive maintenance and breakdown maintenance problems have emerged as important indicator variables for effective synergistic implementation of maintenance practices in northern Indian SMEs.Originality/valueAn output assessment model is developed using a fuzzy set theory for maintenance practices, where performance evaluation is significant to the overall performance of SMEs.

Predictive Maintenance using Machine Learning Based Classification Models

Machine learning facilitates predictive maintenance due to the advantages it holds over traditional methods of maintaining semi-conductor devices such as preventive and breakdown maintenance. Several predictive models using machine learning on the Semiconductor Manufacturing process dataset (SECOM) will be applied in this paper. The dataset contains the information related to semiconductor manufacturing process, with the attributes corresponding to signals collected from semiconductor devices. Due to the high-dimensionality of the data and class imbalance problem in the SECOM dataset, it poses several challenges related to data pre-processing, which is an essential step incorporated in this work while applying various machine learning models. Comparison and analysis of various predictive machine learning classification models were carried out based on the performance metrics like, accuracy and Receiver Operating Characteristic (ROC) curve.

Analisis Maintenance Quayside Container Crane Dengan Metode Failure Mode And Effect Analysis ( Fmea)

Quayside container crane is a type of crane that is placed on the dock at a container terminal that handles loading and unloading of containers from ship to dock or from dock to ship. Quayside container crane is composed of structural parts, mechanical components, electrical components and control systems. This research was conducted to find the root of the problem in the Quayside container crane, by analyzing the system that was experiencing breakdown maintenance. The data used was a breakdown history Quayside container crane No. 01 in 2018-2019 at the Patchouli Terminal of Tanjung Perak Port. The analysis methods used are Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA). From the results of the analysis carried out, it was found that a list of failures that caused the breakdown in 2018-2019 consisted of supply electrical power systems, main hoisting systems, spreader systems, gantry traveling systems, maintrolley traveling systems, and boom hoisting systems. The solution to reduce breakdowns that have occurred is by implementation of predictive maintenance, preventive maintenance, and repair of SOPs and annual maintenance contracts. By using quantitative analysis using Failure Mode and Effect Analysis (FMEA), the largest Risk Priority Number (RPN) is obtained on the main trolley travel system of 450, then continued by the gantry traveling system. 360, the main hoisting system is 280, the suplying electrical power system is 294 and the boom hoist system is 42. Based on the results of the Pareto diagram, the cumulative percentage of priority breakdown calculations is 83% of the main trolley travel system damage,

Ranking of implementation dimensions for maintenance practices in Northern Indian SMEs using integrated AHP-TOPSIS approach

Maintenance practices have been undeniably established as an integral part of an organization and become major facilitators for realizing efficient production systems. The maintenance function must ensure that the production system should always operate at rated capacity. Maintenance practices assume crucial role in Small and Medium Enterprises (SMEs) because SMEs are backbone to efficient and smooth operation of large industries. The present study uses an integrated Analytical Hierarchy process (AHP) and Technique for Order Preference by similarity to Ideal Solution (TOPSIS) approach to evaluate the characteristics of effective maintenance practice implementation in Northern Indian SMEs. AHP approach has been deployed to calculate the weights, which serves as a primary input to TOPSIS for prioritizing the implementation dimensions for the successful implementation of maintenance practices. The present study has highlighted that maintenance implementation dimensions, Corrective Maintenance, General Maintenance, Preventive Maintenance, Predictive Maintenance and Breakdown Maintenance, have been observed to be the most significant factors for realizing sustainable manufacturing performance in Northern Indian SMEs. The significant implementation dimensions outlined in this study should be adopted by top management and concentrated on as crucial areas to ensure the successful implementation of maintenance practices in manufacturing organizations.

Data-driven fleet management using MOORA: a perspective of risk management

Purpose The purpose of this study is to investigate the best fleet for a new purchase based on multi-objective optimization on the basis of ratio (MOORA), reference point and multi-MOORA methods. This study further identifies critical parameters for fleet performance monitoring and exploring optimum range of critical parameters using Monte Carlo simulation. At the end of this study, fleet maintenance management and operations have been discussed in the perspectives of risk management. Design/methodology/approach Fleet categories and fleet performance monitoring parameters have been identified using the literature survey and Delphi method. Further, real-time data has been analyzed using MOORA, reference point and multi-MOORA methods. Taguchi and full factorial design of experiment (DOE) are used to investigate critical parameters for fleet performance monitoring. Findings Fleet performance monitoring is done based on fuel consumption (FC), CO2 emission (CE), coolant temperature (CT), fleet rating, revenue generation (RG), fleet utilization, total weight and ambient temperature. MOORA, reference point and multi-MOORA methods suggested the common best alternative for a particular category of the fleet (compact, hatchback and sedan). FC and RG are the critical parameters for monitoring the fleet performance. Research limitations/implications The geographical aspects have not been considered for this study. Practical implications A pilot run of 300 fleets shows saving of Rs. 2,611,013/- (US$36,264.065), which comprises total maintenance cost [Rs. 1,749,033/- (US$24,292.125)] and FC cost [Rs. 861,980/- (US$11,971.94)] annually. Social implications Reduction in CE (4.83%) creates a positive impact on human health. The reduction in the breakdown maintenance of fleet improves the reliability of fleet services. Originality/value This study investigates the most useful parameters for fleet management are FC, CE, CT. Taguchi DOE and full factorial DOE have identified FC and RG as a most critical parameters for fleet health/performance monitoring.

Predictive Maintenance Analysis Overhead Crane Machine in PT Bromo Steel Indonesia

Problems that often occur in the production process at PT. Bromo Steel Indonesia is damage to the engine overhead crane. The damage that occurs to the engine, has a negative impact on the company, where the production process stops. The purpose of this research is to design maintenance schedules, reduce engine overhead crane downtime, improve Standard Operating Procedures. Based on the results of the research, maintenance intervals on overhead crane engine components are brake systems for 95 days, carbon brush for 27 days, electric hoist for 46 days, limit device for 47 days, clutch for 56 days, pull motor for 31 days, drum roll electric for 44 days, wire rope for 29 days. Changes to the maintenance system from breakdown maintenance to predictive maintenance resulted in saving cost of 27,62% on overhead crane machines component with a nominal value of IDR 1.051.031.511 and result of the analysis of the calculation overall equipment effectiveness at PT Bromo Steel Indonesia before predictive maintenance of 90,19% and after predictive maintenance of 92,06% an increase 1,87 %.

Linear Sliding Stem Control Valve Hunting Analysis and Extrapolation of Valve Monitoring Features

Control Valve is one of the major final control elements used in industries and plays a vital role in enhancing the productivity of a plant. Optimization of control valve depends on the effective preventive maintenance schedule. Control valves may be overhauled from the pipeline when there is a suspicion of fault. Almost 80% of the control valve maintenance relates to the diagnosis of valve in service to detect any abnormality. When a fault is identified the preventive maintenance process shifts to the breakdown maintenance process. During this process the control valve components are tested to determine the cause of the problem and to establish a corrective course of action. With the developments in microprocessor based control valve instruments along with their diagnostics capabilities has allowed engineers to simplify the maintenance activities. These digital devices can identify problems related with instrument air quality, leakage, supply pressure restriction, excessive friction, dead band, and calibration shift. Troubleshooting control valves after installing in the field is a tedious process as it is difficult to discriminate whether the problem is in the control valve or in the controller tuning. This article presents a novel method using sensors to identify the problems related with the control valve hunting, to pinpoint the source of error and present the performance results to end user. The objective of this research is to reduce manual intervention in troubleshooting of control valves and to enhance the predictive maintenance resulting in cost effective production.

Random machine breakdown and stochastic corrective maintenance period on a production inventory system with safe period

This study deals with a production inventory model with random machine breakdown and corrective maintenance. Here the concept of the 'safe period' is introduced in which the machine is running uninterruptedly. 'Safe period' of the production system begins when production starts/commences either at the beginning of the inventory cycle or after corrective maintenance. When a mechanical breakdown occurs, it will be brought under corrective maintenance immediately. The period for corrective maintenance is also uncertain. As a result, shortages may occur. Here, a production system is adopted where a single machine is responsible for total production and all the produced items are good. The objective of this problem is to minimise the total expected cost per unit item because of how much amount is to be produced. An example is given to analyse the model numerically and sensitivity analysis also has been done for key parameters to test the feasibility of our model.

Optimal production lot sizing for an imperfect manufacturing system with machine breakdown and emergency maintenance policy

Purpose The purpose of this paper is to study a production lot sizing problem with consideration of imperfect manufacturing and emergency maintenance policy, providing managerial implication for practitioners. Design/methodology/approach In this study, the authors introduce two models, where in Model I, shortages are not allowed and repair times are negligible. In Model II, shortages are allowed and are partially backlogged, and repair times are assumed to be exponentially distributed, algorithm is developed to solve the models, numerical examples were demonstrated the applications. Findings Results show that in the Model I, demand rate is the most significant parameter affecting the average expected cost, whereas the time needed to breakdown after machine shift is the most significant factor affecting the production lot size. Therefore, reduction in the time needed to breakdown after machine shift would be helpful for determining an appropriate production lot size in Model I. In Model II, repair time parameter is the most significant factor affecting the average expected cost. Reducing the value of machine shift parameter would be helpful for determining an adequate production lot size and reducing decision risk. Practical implications This paper can provide important reference value for practitioners with managerial implication of how to effectively maintain equipment, i.e. how to make product lot size considering the influence of the maintenance policy. Originality/value From the aspect of academia, this paper provides a solution to the optimal production lot sizing decision for an imperfect manufacturing system with consideration of machine breakdown and emergency maintenance, which is a supplement to imperfect EMQ model.

Conceptual Analysis and Survey of Total Productive Maintenance (TPM) and Reliability Centered Maintenance (RCM) Relationship

Maintenance is an important issue that needs to be addressed during the design and manufacturing or building of a system. Both Total Productive Maintenance (TPM) and Reliability Centered Maintenance (RCM) have its own concepts and principles as a maintenance strategy to give impact to the performance and reliability of the system. Therefore, in this research, the TPM will be studied with RCM to show their relationship. Based on the conceptual analysis, the author will analyze publications from journals, books, and articles to find all related information, such as genealogy, definition, concepts, and principle, and implementation of both strategy. The conceptual method used is Näsi’s four elements of conceptual analysis and a questionnaire survey method which has been answered electronically to study the current maintenance strategies in the real industries and to relate it to TPM and RCM. The result of the study shows that TPM had started in 1970 while RCM started in the 1960s, but the foundation of TPM and RCM was started in 1950s with Breakdown Maintenance (BM). TPM is basically about elimination of faults through day-today activities involving the entire force work while the concept of RCM is to improve equipment reliability. The objective of TPM is to achieve zero breakdowns, zero defects, and zero accidents while TPM is to preserve the functions. Although both are using different tools but both are linked together through the Lean Tool and can increase product quality, equipment reliability, increase safety, and increase profit. From the survey result, most of the respondent comes from the companies that implementing traditional maintenance (PM and CM) rather than TPM and RCM. TPM mostly can be implemented in big plant industries while RCM can be applied in small or medium size plant.

Optimization of preventive maintenance for series manufacturing system by differential evolution algorithm

The costs of preventive maintenance have been extensively studied by scholars across all preventive optimization model disciplines. However, one phenomenon fails to be fully studied: breakdown and breakdown maintenance costs. We set out to fill this gap in this study. This study considered the cost of equipment preventive maintenance, and the breakdown maintenance cost caused by an accidental breakdown. In order to more accurately establish the reliability model of equipment breakdown, the three-parameter Weibull distribution was applied to set up the reliability model of equipment and the differential evolution algorithm was adopted to optimize the parameters. On this basis, preventive maintenance was regarded as imperfect maintenance in the study of preventive maintenance strategies for single equipment. In consideration of the combined influence of preventive maintenance and breakdown maintenance, a maintenance strategy in which preventive maintenance times N served as the decision variable was obtained to build a mathematical model on benefit expectation of single equipment in unit time. Based on the research of single equipment, a further study was performed on the multi-equipment series system. Moreover, two strategies were given, both of which use preventive maintenance times N as the decision variable. The first strategy is to take the average cost rate of the system under long-term operation as the optimization objective, while the second strategy is to apply single component maintenance strategy in series system. In the numerical example study, a series manufacturing system composed of two devices was chosen as the research object. Interestingly, we discussed the effect of initial conditions of two-parameter DE on output results. After acquiring the optimal initialization parameters, the failure rate function was achieved by the DE to estimate the parameters of three-parameter Weibull distribution. Meanwhile, the maintenance times N was optimized according to the two strategies respectively. The best result were selected from the results of both strategies based on availability. Thus, the validity and practicability of the proposed research methods are verified.

Nuclear Power Plant Maintenance Optimisation: Models, Methods & Strategies

Maintenance activities are very important for the complex and high-risk installations, especially for Nuclear Power Plant. Good maintenance will be able to minimise loss of production, reducing the cost of the electricity generation, and/or reducing the risk. However, there are many issues to make a good maintenance program, such as regarding limitation of reliability data, the complexity of the system, (in)dependency failure mechanism, unpredictable spare parts supply chain, accessibility for maintenance and repair, environmental limitation, human resources, management, regulation, etc. Many studies have been done by researchers and engineers from various sectors to optimising maintenance activities. We have studied several kinds of available literatures, especially that can be used for the nuclear industry. By using proposed maintenance framework, we would like to describe Nuclear Power Plant maintenance optimisation studies that have been done systematically. We also did a critical review and gap identification between scholar studies and real applications and then classified them to provide more easy understanding information that can be used as an information for the future development. Based on our study, there is a wide gap between academic study and implementation. Although many models, methods, and strategies have been proposed, most of the NPPs in the world are using traditional approach by following time-based and breakdown maintenance strategies. There are such kinds of factors, such as because of the regulatory framework barrier, weak relationships between stakeholders and regarding data availability. Future studies to explore those problems are needed.

Development of a Preventive Maintenance Strategy for an Automatic Production Line Based on Group Maintenance Method

The high maintenance costs and low reliability of automatic production line are attributed to the complexity of maintenance management. In the present study, a preventive maintenance strategy for the automatic production line was developed based on the group maintenance method. The criticality of machines in the production line was evaluated, and then the machines were classified into three groups: the most critical machines, the secondary critical machines and the general machines. The general machines were performed on the breakdown maintenance. The preventive maintenance model of the most critical machines was established with the shortest shutdown time as decision objective on basis of the Delay-time theory. The maintenance model of the secondary critical machine was established based on the considering of reliability-maintenance cost. A case study on an automotive part automatic production line was carried out to verify the proposed preventive maintenance strategy based on the production line data, and the maintenance periods of the most and secondary critical machines were gained; meanwhile, the machines all satisfied the reliability requirements during the maintenance periods.

Usulan Penjadwalan Penggantian dan Pemeriksaan Komponen Kritis Mesin Feeder dan Fanblower (Studi Kasus di PT. Petnesia Resindo)

Many companies do not pay attention to machine maintenance. One of them is PT Petnesia Resindo. The company that have been engaged in platic is still implementing a breakdown maintenance. This proved to disturb the production process. Sothis research is interested in trying to implement preventive maintenance against the proposed machine company, Research begins with the determination of critical machines based on the OEE level. Both engines are feeder and fanblower engine is including a critical engine. The critical components based on the rules " 80-20 "pareto diagram are tapper roll bearing, thrust ball bearing,timing belt,and disc pack coupling . After the analysis of distribution test, it will get the replacement interval and checking interval replacement tapper roll bearings is 1300 hours, for thrust ball bearing component is 1900 hours, for timing belt component is 3220 hours, and for disc pack components is 2080 hours As for the inspection interval of tapper roll bearing component is 1034 hours, for thrust ball bearing component is 1179 hours, for timing belt component is 2545 hours, and disc pack component is 1280 hours. After implementing preventive maintenance it is expected cost savings and slowing down the level of reliability. The cost savings of tapper roll bearing component is around 71%, for thrust ball bearing component is 76%, for timing belt component is 61%, and disc pack component is 59%.

Predictive maintenance strategy of running fault based on ELM algorithm for power transformer

Transformer is the most important core equipment of power system, and once the fault happened, the economic losses and adverse social impacts resulted in faults by the twinkling of eye are difficult to estimate. In order to prevent transformer fault happened, the paper explored a sort of predictive maintenance strategy of transformer fault power transformer supported by the extreme learning machine (ELM) algorithm. In this paper, it made the anatomy of the drawbacks in breakdown maintenance (BM) and preventive maintenance (PM) maintenance system, pointed out the advantage of predictive reliability maintenance (PRM) maintenance system, studied on prediction of the fault diagnosis and predictive algorithm based on ELM, discussed the dynamic fault diagnosis model of power transformer in smart grid environment. A comparative study of different fault pattern prediction algorithms confirmed the rationality and feasibility of the fault prediction algorithm based on ELM. Transformer operation experience shows that the ELM algorithm can provide powerful technical support for the maintenance strategy of transformer fault prediction.

An Integrated Framework to Optimize the Safety Management for Process Industry: The Gas Station as a Case Study

The safety management is considered to be an effective way to eliminate out accident potentials and help managers adopt proper measures to improve the whole system safety level. In this regard, an integrated framework centered maintenance (SRCM) method has been proposed as a crossing strategy to improve the gas station safety performance with resort to Risk Based Inspection (RBI), Reliability Centered Maintenance (RCM) and Safety Instrument Level (SIL). It benefits from the fusion analysis of equipment risk and instrument grade. That is, we address global-scale concerns that may cause combination effect to the system performance and establish a fusion matrix to estimate the optimal maintenance strategy. An innovative logical judgment has been introduced to model the process of selecting proper maintenance measures. Meanwhile, determine the obvious failure impacts or high-risk factors. Also, Hazard and operability analysis (HAZOP) and layer of protection analysis (LOPA) have been developed to validate the SIL. The integrated framework can be also used to explain the issue of unknowns and uncertainties in reliability analysis and how these issues can be addressed in system components by optimizing component breakdown maintenance.

English

Maintenance is important to the success and continuity of firms, particularly in the changing society as ours. The effectiveness and the survival of organizations are largely enhanced by the ability of management to ensure that there is functional equipment, lands and building, infrastructure and fixtures. In the manufacturing setup, there is wear and tear of machines and equipment in periods of usage that require sufficient maintenance to enhance their useful life. This ensures reliability of the machines and equipments in the production plants as to uninterrupted production runs. Most manufacturing firms have lost their effectiveness and productivity, because of poor equipment maintenance. Poor attitude towards equipment maintenance hamper firm’s operation. This study examined the principles of preventive and breakdown maintenance aimed at addressing the issues of negligence and lack of equipment maintenance. Also, the study focused more on maintenance and replacement problem solving, and the main difficulties are reported along with probable solutions. Key words: Maintenance, preventive maintenance, breakdown maintenance, reliability and replacement.

A Wireless Sensor Network for Prognostics and Health Management

Equipment maintenance mode is changing from Breakdown Maintenance and Periodic Maintenance to Condition-based Maintenance. Fault Prognostic (Remaining Useful Life, RUL) is the key to Condition-Based Maintenance. This article first introduces the basic principles of Dynamic Bayesian Network and the Fault Tree, and then uses the Fault Tree and Dynamic Bayesian Network for Wireless Sensor Network for fault prediction methods and procedures. Dynamic Bayesian networks can integrate information from various sources and give a probabilistic representation of the system. DBNs provide a platform suited for seamless integration of diagnosis and prediction. ly used in order to anticipate the failure At last, with the methods above, we predict the life cycle and the health status of the Wireless Sensor Networks. We use MATLAB to simulate and the result showed that the methodology proposed could have been successful of the wireless sensor network.