Reliability-based Maintenance Research Articles

A sensor-driven operations and maintenance planning approach for large-scale leased manufacturing systems

The rise of mass customisation drives manufacturers to adopt leasing agreements for their machinery rather than outright their ownership. This industrial trend results in large-scale leased manufacturing systems, where the asset conditions and maintenance requirements for a fleet of machines and/or production lines from several manufacturers are continuously monitored and managed by a lessor. In this paper, we propose an operations and maintenance planning model that explicitly models (i) dynamic real-time failure rate predictions for machines based on sensor-driven degradation data, (ii) optimal routes for maintenance teams across a number of geographically-distributed manufacturing sites, and (iii) operational outcomes. The model can handle complex maintenance and operational outcome interdependencies between multiple machines, and incorporate demand for different products, heterogeneous machine capacities and capabilities, factory topology, and the resulting production flow capacities of products. To demonstrate the model’s practicality, it is applied to three experimental case studies with various numbers of machines, operating schedules, product types, and flow capacities. The results show that the proposed model significantly outperforms the traditional reliability-based maintenance model in key metrics such as the number of unexpected failures, the cost of preventive maintenance actions, machine downtime, the percentage of unsatisfied demand, and total cost.

Reliability-based maintenance optimization of long-distance oil and gas transmission pipeline networks

This paper presents a maintenance model for optimal planning of pipeline network inspections subjected to corrosion and residual stress. The proposed model includes predictive degradation modeling, spatio-temporal reliability, and expected cost minimization. The pipeline networks are formed by linear segments and irregular zones such as elbows, weld joints, and flanges for putting pipes together and changing the direction of gas or liquid flow. The mean of Karhunen-Loève expansion is used to model space-variant corrosion, residual stress, and stochastic dependency in different exposure zones. Monte Carlo simulations are employed to compute the failure probability of individual components. Subsequently, a Bayesian network evaluates the failure probability of the complex pipeline network and identifies the most critical components. Depending on the objective or market demand uncertainties, a cost model is also proposed to optimize the inspection policy. The methodology is applied to a case study, and the results show that it can provide valuable insights to decision-makers to enhance complex systems' safety and reliability.

Application of Overall Equipment Effectiveness for Optimizing Ventilator Reliability in Intensive Care Units and Emergency Departments

Healthcare organizations seek to offer a comprehensive medical services system framework with safety and high quality. This needs continuously developing integrated governance systems that provide safe, reliable, and maintaining high-quality care and value-based care for patients. In addition to improving access to health services in time, increasing efficiencies in providing medical services, reducing the risk-based medical services, and selecting a suitable maintenance policy for maintaining the medical devices. This paper adopted a quantitative analysis method based on Reliability-Based Maintenance (RCM) to comprehensively evaluate the overall medical equipment's effectiveness. RCM is an integrated approach to continuous improvement of the maintenance programs because it can predict early medical equipment failure and determine the mean time between failures. It also assesses the risk level to the patient's life in case of a medical equipment breakdown while provisioning medical services. This study targeted biomedical engineering, intensive care units, and emergency departments at 24 public hospitals in a top 20 OECD country. It selected these departments due to the rise in the rate of patients needing ventilator equipment availability during COVID-19, risk based on the sudden breakdown of the ventilator machines, and increasing the annual budget percentage required to provide medical services, where 239 of the ventilator equipment were investigated. Staff Experience-based Evidence was adopted to collect data by interviewing staff and distributing the survey. The study found that the average OEE for ventilator devices in Intensive Care Units and Emergency Departments was 63%. The device's performance was rated at 65%, while its availability and quality rate were both rated at 100%. These findings suggest that the use of the OEE metric has improved ventilator device reliability and performance in selected hospitals. The OEE metric may have potential benefits for improving the performance of other medical devices as well.

An adaptive reliability-based maintenance policy for mechanical systems under variable environments

The performance of mechanical systems is greatly influenced by varying operational environments such as load, temperature, and humidity, which can result in accelerated degradation and failure. To address this issue, this article considers an adaptive maintenance policy based on updated conditional reliability for mechanical systems under variable environments. A model of deterioration-integrated failure process is presented, which integrates degradation process, failure events, and environmental variables of the system. The degradation process is modeled by a random effect Wiener process, with its drift parameter following a general distribution to represent the environmental random effects. The failure events are modeled by a proportional hazards (PH) model with environment-varying covariate. The health of the system is evaluated using a matrix-based approximation method that provides estimates of the system's conditional reliability and mean residual life. Using the prognostic information, an adaptive maintenance model with a switchable control chart is then developed to monitor and maintain system based on the status of system reliability. The decision variables of the adaptive reliability-based maintenance are determined by a computational algorithm formulated in a semi-Markov decision process (SMDP) framework, with the objective of minimizing the long-run expected average cost per unit time. Vibrational data of bearings under varying operational environments are presented to illustrate the effectiveness of the proposed approach.

Decision-Making Based on Multi-Dimensional Quality Control for Bridges

Quality control (QC) may be applied as a framework for maintenance planning when assigning different intervention measures to single structural elements or systems. This work proposes a reliability-based maintenance decision-making process for planning visual inspections on bridges based on the value of information and prior inspection data, and also promotes updating and improvement cycles for subsequent planning. To that aim, an integration between SHM (Structural Health Monitoring) data with a multidisciplinary approach is proposed to obtain a reliability index attending to QC. The data analysis was mainly carried out with respect to an existing measurement database and structural assessments, which were combined to obtain weighted importance coefficients for each component according to their significance in the structure. The Iranian railway network has a built stock of nearly 28,200 bridges from which a database obtained from 104 bridges was studied in this work, considering the data obtained from technical identification checklists. The results were then calibrated and validated with a dataset of seven bridges, which were inspected onsite. The inspection comprised the identification and grading of damages and defects on each element. Observed defects were considered as input for the risk analysis of each component of the network by considering the probability of detection, occurrence and its likely consequences. Decision making with inspection and intervention costs optimization was then performed, for a specific case study, using Principal Component Analysis (PCA) together with the value of information (VOI) for data filtering. With this approach, several parameters with lower values reduced from inspection and other valuable data remain for bridge quality assessment with optimum maintenance cost.

A Simulation – Optimization Model of Network Failure Prone Manufacturing Systems with a Reliability-Based Maintenance and Revenue Sharing Approach

با توجه به اثر عوامل تصادفی مانند خرابی ماشین‌ها بر قدرت رقابتی سازمان‌های تولیدی، اهمیت برنامه‌ریزی تولید دوچندان شده است. لذا سیستم‌های تولیدی مستعد شکست برای مقابله با این عدم قطعیت، پدید آمده‌اند. جهت حفظ سهم در بازار رقابت و افزایش بهره‌وری، سیستم‌های صنعتی به اسـتراتژی نگهداری و تعمیرات به‌منظور کاهش نرخ خرابی و افزایش قابلیت اطمینان روی آورده‌اند. به‌منظور افزایش ظرفیت تولید، فراهم­آوردن انعطاف‌پذیری بیشتر و اطمینان از رضایت‌مندی مشتری از نظر کمیّت، کیفیت و زمان‌بندی، استفاده از پیمانکاری فرعی با رویکرد اشتراک درآمدها، گزینه‌ای مناسب در این پژوهش است. این پژوهش، سیستمی متشکل از شبکه‌ای از ماشین‌آلات با محدودیت رابطه و زمان خرابی و تعمیر تصادفی می باشد. به‌منظور جلوگیری از کمبود از بافرهای میانی و یک بافر نهایی استفاده می‌شود. پارامتر مؤثر دیگر تعیین دفعات بهینه‌ نگهداری پیشگیرانه است که منتج به حداقل رساندن هزینه نگهداری و تعمیرات پیشگیرانه و اصلاحی می‌شود. هدف تعیین نرخ بهینه تولید و متغیر‌های نگهداری پیشگیرانه و متغیر‌های مربوط به پیمانکاران فرعی است. برای این منظور از شبیه‌سازی گسسته ـ پیشامد استفاده می‌شود. بعد از مدل‌سازی در نرم‌افزار ارنا بهترین مقادیر متغیر‌های تصمیم در بستر Opt Quest به­دست می‌آید که به کاهش 5/22 درصدی هزینه‌های کل سیستم منجر می‌شود.

Research to Improve Preventive Maintenance of Technical Equipment

Abstract In order to achieve the strategic level of military equipment exploitation, according to NATO requirements, it is necessary to continuously adapt the specific maintenance activities. The main objectives pursued in the maintenance system modernization process are: to implement reliability-based maintenance, to use management methods, to present the functional scheme, and to identify the critical points in which to intervene with maintenance operations. The use of the Pareto method in the direction research studied in the paper estimates that the first two faults among those identified, namely the imbalance of the braking force on the axle and the defective service brake, represent a significant percentage of the total downtime of the Roman-DAC trucks used for the present study. According to the AMDEC data sheet, these two defects have a criticality index of 70 and 60, respectively, compared to the following identified defects, which present a criticality index of 18 and 20, respectively. In practice, the aim will be to eliminate, in particular, the identified causes that entail defects in the first and second categories. Using this research methodology, and by extrapolation to all technical equipment components, it is possible to identify solutions to improve preventive maintenance by ranking defects and performing maintenance operations on those components more often subject to damage, before they fail.

A Mixed Integer Model for Unrelated Parallel Machine Scheduling with Job Deteriorating Effect

Maintenance planning has been widely applied in manufacturing systems to improve production efficiency. In some real cases, job processing times may change over time, but they are mostly assumed to be constant in the scheduling literature. Hence, in this article, a mixed integer model is developed to optimize scheduling jobs on unrelated parallel machines with reliability-based maintenance and job deteriorating effects. The proposed model considers a reliability-based maintenance system and multi-stage quality cost and starts a time-dependent deteriorating effect. Based on the assumptions, if machines work in undesirable conditions, quality reduction and quality cost increment would occur. According to the start time-dependent deteriorating effect, the processing time of each job is a function of its start time. The problem is modeled by an integer linear programming method. Computational experiments are performed on various numerical instances to show the model’s effectiveness.

Reliability-based Maintenance Modelling of Multi-Component Systems using the Proportional Intensity Model

Reliability-based Maintenance Modelling of Multi-Component Systems using the Proportional Intensity Model

Using expert judgement techniques to assess reliability for long service-life components: An application to railway wheelsets

Due to uncertainties in the deterioration process of long service-life assets, assessing reliability and planning maintenance and inspection activities are often difficult tasks. Most of the methods developed use operational or historical data from the manufacturer to predict the deterioration of the asset and estimate its reliability. However, in practice, such failure data is often scarce (e.g. very rare events) and the reliability prediction models might not be adapted to the operation of the user. In addition, available data gathered from maintenance or inspection tasks might only inform about a short period of time, and it might be difficult to obtain the corresponding reliability predictions as the associated uncertainty is still significant. Hence, this paper introduces a reliability assessment method that quantifies uncertainties regarding the failure of long service-life components. It combines Cooke’s classical method (also known as Structured Expert Judgement) with a histogram technique, creating a performance-based method to assess uncertainty in components lifetime distributions. A case study on railway wheelsets is analysed to illustrate the effectiveness of such approach. The results show that the presented method is able to assess failure rates, which can support decisions in reliability-based maintenance of engineering systems.

Expected Proportional Hazard Model in Preventive Maintenance

Whether directly in the form of expenses or indirectly, the objective of maintenance in the mining industry is self-evident in time losses and loss of production. In this paper, the reliability-based maintenance is examined with a different insight than before. The system goes back to the Good As New (GAN) state or too Bad As Old (BAO) maintenance state; why so, the maintenance of the system shifts to the midrange state. On the other hand, the implementation of repairs is strongly influenced by the environmental factors that are known as the “risk factors”. Therefore, an analysis requires a model that integrates two basic elements: (1) incompleteness of the maintenance effect and (2) risk factors. Thus, an extensive proportional hazard ratio model (EPHM) is used as a combination of the Proportional Hazard Model (PHM) and the Hybrid Imperfect Preventive Maintenance model (HIPM) in order to analyze these elements. In this regards, four different preventive maintenance strategies are proposed. All four strategies are time-based including constant interval or periodic (the first and second strategies) and cyclic interval (the third and fourth strategies). The proposed method is applied for a Komatsu HD785-5 dump-truck in the Songun copper mine as a case study. The PM intervals with a mean value of risk factors for the four activities to reach the 80% reliability for the first and second strategies are about 5 and 48 hours. These intervals for the third strategy are calculated as 48.36, 11.58, 10.25, and 9.035, and for the fourth strategy are 5.06, 4.078, 3.459, and 1.92.

Assessing the Effect of Reliability-Based Maintenance Approach in Job Shop Scheduling with Setup Time and Energy Consideration Using Simulation; A Simulation Study

ABSTRACT Maintenance in a stochastic job shop scheduling environment significantly impacts real-time scheduling problems with the environmental aspect. This simulation research assesses the effect of a reliability-based preventive maintenance approach to system performance for considering job shop scheduling problems with sequence-dependent setup time (SDST). Two types of reliability-based maintenance approaches are considered, i.e., reliability-centered preventive maintenance (RCPM) and a reliability-centered periodic preventive maintenance approach (RCPPM). The shop comprises 10 different machines and six job types. Six scheduling and two energy-oriented performance measures (Pms) are considered for evaluating the system’s performance. Results reveal that lower levels of reliability, namely, 0.74, 0.78, and 0.82 recommended for mean flow time, makespan, average operation energy consumption, average idle energy consumption, and total setups Pms. A 0.74 level of reliability is recommended for mean tardiness and the number of tardy jobs Pms for the RCPM approach. RCPPM approach provides the best system Pms by mean flow time, makespan, mean tardiness, average operation energy consumption, average idle energy consumption, and the number of tardy jobs when maintenance time is 5% of operation time. In contrast, mean setup, and total setups Pms are independent of maintenance time. Both approaches get compared, and its statistical analysis shows that if maintenance time is 15% or more than 15% of the operation time, the RCPM method is recommended. If maintenance time is 10% or less than 10% of the operation time, maintenance planning is recommended using the RCPPM approach. Considering real-time scheduling work with the environmental aspect represents the novelty of the present study.

A two-stage fuzzy multi-criteria approach for proactive maintenance strategy selection for manufacturing systems

This paper studies the use of fuzzy logic theory embedded in fuzzy axiomatic design to develop an integrated model with the analytical hierarchy process, and a selection of maintenance strategies in manufacturing systems through weighted sum-product evaluation. In this paper, an analysis was conducted to understand the model characteristics of combined fuzzy axiomatic design, analytic hierarchy process and weighted aggregated sum product assessment. The results from the analysis served as information to establish the best maintenance strategy. Based on the data obtained from a factory, which was tested on the proposed framework, it is concluded that the most preferred proactive maintenance strategy for the case study was preventive maintenance, followed by reliability-based maintenance while predictive maintenance is the least preferred maintenance strategy in the rolling mill. Accordingly, the two-stage fuzzy multi-criteria maintenance strategy selection approach is appropriate to select the best maintenance strategy for the factory. This paper offers a new method to establish the best maintenance strategy for a rolling mill. As such, the manager could install a method for significant improvement in engineering practices with promising business excellence and competitiveness results.

Performance Evaluation of Underground Mining Machinery: A Case Study

Unexpected occurrence of uneven breakdowns and their consequences have a significant influence on the equipment life. Hence, there is a need to discover the motives for the happening of critical potential failures and required repair or replacement action to control. Reliability analysis is utilized to approximate the performance of the equipment. In this study, the performance of the underground mining machinery known as load haul dumper (LHD) has been estimated with reliability analysis. The best-fit distribution of the data sets was selected by testing the numerous statistical distributions using the Kolmogorov–Smirnov (K-S) test. The percentage of reliability of each subsystem of the LHD machine was computed based on the best-fit approximation. The overall system reliability of the equipment was estimated using a series configuration-based reliability block diagram (RBD) approach. The reliability-based preventive maintenance (PM) time intervals were also computed for estimated 90.00% reliability. To accomplish the desired level of reliability, a review on maintenance programs should be made. Possible recommendations were made to the maintenance department in the industry for improvement in equipment.

Reliability-Based Maintenance Strategy for Gusset Plate Connections in Steel Bridges Based on Life-Cost Optimization

AbstractThis paper focuses on the maintenance decision of vulnerable parts that need rehabilitating within the design service period under repetitive vehicular loads in steel bridges based on the t...

Information value-based optimization of structural and environmental monitoring for offshore wind turbines support structures

The use of load and structural performance measurement information is vital for efficient structural integrity management and for the cost of energy production with Offshore Wind Turbines (OWTs). OWTs are dynamically sensitive structures subject to an interaction with a control unit exposed to repeated cyclic wind and wave loads causing deterioration and fatigue. This study focuses on the quantification of the value of structural and environmental information on the integrity management of OWT structures, with the focus on fatigue of welded joints. By utilizing decision analysis, structural reliability methods, measurement data, as well as the cost-benefit models, a Value of Information (VoI) analysis can be performed to quantify the most beneficial measurement strategy. The VoI assessment is demonstrated for the integrity management of a butt welded joint of a monopile support structure for a 3 MW OWT with a hub height of approximately 71m. The conditional value of three-year measured oceanographic information and one-year strain monitoring information is quantified posteriori in conjunction with an inspection and repair planning. This paper provides insights on how much benefits can be achieved through structural and environmental information, with practical relevance on reliability-based maintenance of OWT structures.

An integrated model for maintenance policies and production scheduling based on immune–culture algorithm

The development of integrated modelling for maintenance policies of multi-component repairable system and production scheduling is challenging for two reasons. First, capturing dependency of this multi-component repairable system is difficult because different failure types associated with different components are under competing risks and their complicated relationships may lead to overall system dependency. Second, the integrated model is difficult to optimize because it is an NP-hard problem that exact optimization methods are intractable. For coping with these two difficulties, we propose a parametric statistical model using copula function to capture the overall system dependency. Under partially perfect maintenance policy at component-level, the likelihood functions for observed failures are derived and maximum likelihood method is used to estimate unknown parameters. Then relying on this parametric statistical model, the system hazard function is derived to depict the reliability-based imperfect preventive maintenance policy at system-level. Finally, to obtain the optimal solution(s) of the integrated model, we design an adaptive immune clone selection–culture algorithm, which is inspired from immune clone selection algorithm and culture algorithm. Results of the case study validate that our proposed maintenance policies and methodology have great advantages over the component-level or system-level maintenance policy and immune clone selection algorithm.

Availability-based reliability-centered maintenance planning for gas transmission pipelines

A large portion of natural gas is transferred through transmission pipelines. For these facilities, in the United States, external corrosion has led to more than 1700 failures in the past decades, causing a property damage of approximately $189 M. Such numbers highlight the importance of maintaining such facilities in safe conditions to postpone corrosion failure. Given such criticality, addressing pipeline availability is of high importance from economic consequences perspective for a national economy. However, most of the developed methods merely rely on considering costs, reliability or condition levels as maintenance decision criteria and ignore the importance of continuity of operation and pipeline availability. In this research, a maintenance planning framework is proposed for external corrosion of gas transmission pipelines through an availability-centered reliability-based maintenance planning procedure. This framework is based on the pipeline reliability profile obtained from a Monte Carlo simulation. This simulation is carried on a previously developed failure prediction model for gas transmission pipelines buried in the Great Plains region of the U.S. and considers failure uncertainties. In addition, a discrete event simulation (DES) approach is proposed to assess an availability-cost indicator of different maintenance scheduling alternatives for the case study of a 24-inch gas pipeline. This research reveals that a combination of wrap and replacement maintenance actions at the service life of 30.1 and 40.5 years respectively, is the most effective maintenance alternative in terms of improvement of availability per unit cost for the presented case study. This framework can help pipeline professionals in maintaining such facilities by considering their criticality using an availability-based maintenance scheduling approach. Such method can complement the conventional cost-based industry practices.

Optimum Consecutive Preventive Maintenance Scheduling Model Considering Reliability

As an effective means for improving system condition and reducing failures, a reasonable preventive maintenance scheduling guarantees the stability and safety of the system. This paper studies a consecutive maintenance scheduling problem for single-component systems with imperfect maintenance, and an optimum model with minimum reliability constraint for minimal cost rate is developed based on the renewal theory. The age-based and reliability-based maintenance strategies are modeled and compared according to a numerical example based on the degradation data of the actual system, which also verifies the optimality of the reliability-based strategy. At last, the influence of some key parameters is discussed by sensitive analysis.

Operational reliability evaluation-based maintenance planning for automotive production line

ABSTRACTReliability evaluation plays a critical role in upgrading the availability and productivity of automotive manufacturing industries by adopting the well-planned maintenance. Due to the lack of operation management studies in automotive industry, this paper addresses an operational reliability evaluation through failure behavior trend in an automotive production line. The main approaches for reliability analysis in this study include statistical structure and Monte Carlo simulation model. The statistical structure consists of three steps: data acquisition and homogenization process, validity of the trend hypothesis and parameters estimation. The reliability evaluation under statistical approach identified the main bottlenecks through the recognized behavior trend of system so that needs to be considered as a priority. Besides, K–R algorithm as Monte Carlo simulation was carried out to simulate reliability regarding failure distribution function. The result of Monte Carlo simulation with different iterations provides a high prediction accuracy of reliability with the lowest error. In addition, regarding the computed reliability through the proposed approaches and total expected cost, a reliability-based maintenance optimization model was conducted. The proposed maintenance intervals could be useful for improving the operational performance of critical components in automotive system.