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The purpose of this paper is to investigate performance times from a quality engineering perspective for response and repair of pipes at a public utility. The objective is to scientifically determine the pipe that offers the most desirable downtime (DT) and time to repair (TTR).

Four types of water supply pipes – concrete cylinder (CC), cast iron (CI), ductile iron (DI), and polyvinyl chloride (PVC) – in prevalent use at the City and County of Honolulu Board of Water Supply were analyzed to determine the pipe type that is most consistently repaired to desired performance specifications. Data for mean downtime (MDT) and mean time to repair (MTTR) were used to evaluate the stability and capability of the repair processes for each pipe type. The analysis was completed through the use of control charts, operating characteristic (OC) curves, and process capability indices.

The results of the analysis indicated that CI pipes were the worst material in terms of DT and TTR. The control charts for MDT for all pipe types, and the MTTR for CI and CC pipes, were found to be out of statistical control, but the control charts for the MTTR of DI and PVC pipes were discovered to be in control. According to the OC curves, in which the hypothesis stated that the average MDT or MTTR was between the specification limits, there was a high tendency in all pipe types to accept the hypothesis when it was true. However, the probability of type I errors was high from operational standards at the USL level. Process capability analyses found that only CC pipes were able to meet performance design specifications; however, repair times are extremely large for CC pipes. Overall, it is recommended that CI pipes be replaced when the opportunity arises.

This investigation serves to address a major query in asset management at the public utility, that of which pipes should be selected during design and procurement from a maintenance perspective. In addition, the study helps to understand the trend of DT and TTR for the various pipes.

Quality water supply is of paramount social importance in modern cities.

A quality engineering approach to asset management for pipe systems at public utilities that serves to add a new dimension to asset performance analysis is adopted.

The purpose of this paper is to further develop the Decision Making Grid (DMG) proposed by Ashraf Labib (e.g. Labib, 1998, 2004; Fernandez et al., 2003; Aslam-Zainudeen and Labib, 2011; Stephen and Labib, 2018; Seecharan et al., 2018) by proposing an innovative solution for determining proactive maintenance tactics based on mean time between failures (MTBF) and mean time to repair (MTTR) indicators.

First, the influence of MTTR and MTBF indicators on proactive maintenance tactics was computed. The tactics included risk-based maintenance (RBM), reliability-centered maintenance (RCM), total productive maintenance (TPM), design out maintenance (DOM), accessibility-centered maintenance (ACM) and business-centered maintenance (BCM). Then, the tactics were allocated to the cells of a DMG with MTTR and MTBF axes. The proposed approach was examined on 32 pieces of equipment of the Esfahan Steel Company and appropriate maintenance tactics were consequently determined.

The findings indicate that the DOM, BCM, RBM and ACM tactics with weights of 0.86, 0.94, 0.68 and 1.00 are located at the corners of the DMG, respectively. The two remaining tactics of TPM and RCM are located at the middle corners. Also, the results indicate that the share of tactics per spotted equipment in the grid as 62, 22 and 16 percent for RCM, DOM and BCM, respectively.

While reactive and preventive maintenance strategies include corrective, prospective, predetermined, proactive and predictive policies, the focus of this study was merely on the tactics of proactive maintenance policy. The advantage of the developed DMG over Labib’s DMG lies in its application for equipment with the unique condition of the bathtub curve.

While the basic DMG has been mostly used regardless of the type of maintenance policies, this study provides a DMG for a specific application regarding the proactive policy. In addition, the heuristic approach proposed for the development of DMG distinguishes this study from other studies.

Decision-Making Grid (DMG) is used for determining maintenance tactics and is associated with the reliability and risk management of assets. In this grid, decision making is performed based on two indicators of Mean Time to Repair (MTTR) and frequency of failures. The purpose of this paper is to improve DMG by recognizing interdependence among failures.

Fault Tree Analysis and Reliability Block Diagram have been applied for improving DMG. The proposed approach has been examined on eight equipment of the steel making and continuous casting plant of Mobarakeh Steel Company.

Findings indicate different positions of equipment in the cells of the new grid compared to the basic grid.

DMG is limited to two criteria of frequency of failures and MTTR values. In both basic and new DMGs, cost analysis has not been performed. The application of the proposed approach will help the reliability/maintenance engineers/analysts/managers to allocate more suitable maintenance tactics to equipment. This, in turn, will enhance the equipment life cycle and availability as the main objectives of physical asset management.

A major limitation of basic DMG is that the determined tactic based on these two indicators might not be an appropriate solution in all conditions, particularly when failures are interdependent. This has been resolved in this paper.

Key performance indicators (KPIs) of maintenance systems serve as benchmarks to workers and organizations to compare their goals for decision-making purposes. Unfortunately, the effects of one KPI on the other are least known, restraining decisions on prioritization of KPIs. This article examines and prioritizes the KPIs of the maintenance system in a food processing industry using the novel Taguchi (T) scheme-decision-making trial and evaluation laboratory (DEMATEL) method, Taguchi–Pareto (TP) scheme–DEMATEL method and the DEMATEL method.

The causal association of maintenance process parameters (frequency of failure, downtime, MTTR, MTBF, availability and MTTF) was studied. Besides, the optimized maintenance parameters were infused into the DEMATEL method that translates the optimized values into cause and effect responses and keeping in view the result of analysis. Data collection was done from a food processing plant in Nigeria.

The results indicated that downtime and availability have the most causal effects on other criteria when DEMATEL and T-DEMATEL methods were respectively applied to the problem. Furthermore, the frequency of failure is mostly affected by other criteria in the key performance indication selection using the two methods. The combined Taguchi scheme and DEMATEL method is appropriate to optimize and establish the causal relationships of factors.

Hardly any studies have reported the joint optimization and causal relationship of maintenance system parameters. However, the current study achieves this goal using the T-DEMATEL, TP-DEMATEL and DEMATEL methods for the first time. The applied methods effectively ease decisions on prioritization of KPIs for enhancement.

The operating costs and production losses due to equipment failure of offshore gas and oil production systems can exceed initial investment cost. The article explores the probability distribution of downtime associated with random equipment failure and applies availability assessment to design optimisation.

The aim of this study is to develop a 3D model of decision- making grid (DMG) considering failure detection rate.

In a comparison between DMG and failure modes and effects analysis (FMEA), severity has been assumed as time to repair and occurrence as the frequency of failure. Detection rate has been added as the third dimension of DMG. Nine months data of 21 equipment of casting unit of Mobarakeh Steel Company (MSC) has been analyzed. Then, appropriate condition monitoring (CM) techniques and maintenance tactics have been suggested. While in 2D DMG, CM is used when downtime is high and frequency is low; its application has been developed for other maintenance tactics in a 3D DMG.

Findings indicate that the results obtained from the developed DMG are different from conventional grid results, and it is more capable in suggesting maintenance tactics according to the operating conditions of equipment.

In failure detection, the influence of CM techniques is different. In this paper, CM techniques have been suggested based on their maximum influence on failure detection.

In conventional DMG, failure detection rate is not included. The developed 3D DMG provides this advantage by considering a new axis of detection rate in addition to mean time to repair (MTTR) and failure frequency, and it enhances maintenance decision-making by simultaneous selection of suitable maintenance tactics and condition-monitoring techniques.

This paper aims to simulate vital corrective actions (CAs) affecting system availability through a system dynamics approach based on the results obtained by analyzing the causal relationships among failure modes and effects analysis elements.

A stock and flow diagram has been developed to simulate system behaviors during a timeframe. Some improvement scenarios regarding the most necessary CAs according to their strategic priority and the possibility of eliminating root causes of critical failure modes in a roller-transmission system have been simulated and analyzed to choose the most effective one(s) for the system availability. The proposed approach has been examined in a steel-manufacturing company.

Results indicated the most effective CAs to remove or diminish critical failure causes that led to the less reliability of the system. It illustrated the impacts of the selected CAs on eliminating or decreasing root causes of the critical failure modes, lessening the system’s failure rate and increasing the system availability more effectively.

Results allow managers and decision-makers to consider different maintenance scenarios without wasting time and more cost, choosing the most appropriate option according to system conditions.

This study innovation would be the dynamic analysis of interactions among failure modes, effects and causes over time to predict the system behavior and improve availability by choosing the most effective CAs through improvement scenario simulation via VENSIM software.

The purpose of this paper is to propose a method to compute RAMD indices to measure and improve the performance of skim milk powder production system of a dairy plant under real working conditions.

The present work is carried out by developing performance model based on Markov birth-death process. The skim milk powder production system consists of six units. The first order governing differential equations are derived using the mnemonic rule and further solved to calculate RAMD indices i.e. reliability, availability, maintainability, dependability, MTBF, MTTR and dependability ratio for each subsystem of the system.

The subsystem SS1 comprising of chiller and cream separator is the most critical from maintenance point of view, as the reliability, availability, maintainability, dependability, MTBF and dependability ratio indices are low as compared to those of other subsystems of skim milk powder production system of the dairy plant.

The RAMD indices of the present work is very useful for finding the critical subsystem and its effect on the performance of the system working under real working conditions. Further, based on findings the maintenance priorities for various subsystems can be decided.

Maintenance management is a vital strategic task given the increasing demand on sustained availability of machines. Machine performance depends primarily on frequency and downtime; therefore, ranking critical machines based on these two criteria is important to determine the appropriate maintenance strategy. The purpose of this paper is to compare two methods, using case studies, to allocate maintenance strategies while prioritising performance based on frequency and downtime or Mean Time to Repair: the Decision Making Grid (DMG) and Jack-Knife Diagram (JKD).

The literature indicates the need for an approach able to integrate maintenance performance and strategy in order to adapt existing data on equipment failures and to routinely adjust preventive measures. Maintenance strategies are incomparable; one strategy should not be applied to all machines, nor all strategies to the same machine.

Compared to the Pareto histogram, the DMG and JKD provide visual representations of the performance of the worst machines with respect to frequency and downtime, thus allowing maintenance technicians to apply the appropriate maintenance strategy. Each method has its own merits.

This work compares only two methods based on their original conceptualisation. This is due to their similarities in using same input data and their main features. However, there is a scope to compare to other methods or variations of these methods.

This paper highlights how the DMG and JKD can be incorporated in industrial applications to allocate appropriate maintenance strategy and track machine performance over time.

Neither DMG nor JKD have been compared in the literature. Currently, the JKD has been used to rank machines, and the DMG has been used to determine maintenance strategies.

The purpose of this paper is to investigate the performance of a turbine structure of the oil and gas Egyptian company in terms of reliability, mean time to failure (MTTF), mean time to repair (MTTR) and mean time between failures (MTBF) under fuzzy environment and working criteria. This paper examines the impact of the failure of various components on the complete turbine structure of the oil and gas system.

To overcome the problem of uncertain behavior of available data for various components, the right triangular generalized fuzzy number (RTrGFN) is proposed to be taken into the account to express the uncertainty which attains some tolerance in data. Furthermore, reliability indices are calculated with the help of the Lambda Tau method and the arithmetic operations on right generalized triangular fuzzy numbers (RTrGFN).

This paper explores the reliability of a repairable 3 out of 4 structure of turbines and along with the other parameters namely MTTF, MTTR and MTBF; under a fuzzy environment. Failure rates and repair times are expected to be exponential. The ranking of components of the structure is being found to decide the priority for maintenance.

This paper investigates the performance of the system with different spread/tolerance like 15%, 25% and 50% of crisp data. It helps to predict realistic results in the range value. To enhance the system's performance, the most important item of the system requires greater attention. For this, the authors find the sensitive part by ranking. For ranking, an extended approach has been developed to find the sensitive unit of the system by using the right triangular generalized fuzzy number. This paper explores the most and least sensitive component of the system, which helps the maintenance department to plan the maintenance action.