Importance Measure-Based Maintenance Strategy Considering Maintenance Costs

Maintenance is an important way to ensure the best performance of repairable systems. This paper considers how to reduce system maintenance cost while ensuring consistent system performance. Due to budget constraints, preventive maintenance (PM) can be done on only some of the system components. Also, different selections of components to be maintained can have markedly different effects on system performance. On the basis of the above issues, this paper proposes an importance-based maintenance priority (IBMP) model to guide the selection of PM components. Then the model is extended to find the degree of correlation between two components to be maintained and a joint importance-based maintenance priority (JIBMP) model to guide the selection of opportunistic maintenance (OM) components is proposed. Also, optimization strategies under various conditions are proposed. Finally, a case of 2H2E architecture is used to demonstrate the proposed method. The results show that generators in the 2E layout have the highest maintenance priority, which further explains the difference in the importance of each component in PM

Study on Preventive Maintenance Strategies of Filling Equipment Based on Reliability-Cantered Maintenance

In order to ensure normal operation of enterprise production activities and enhance the competitiveness of enterprise, equipment management and maintenance strategy formulation has always been one of important contents of daily management of enterprise. According to the actual requirement of a Chinese beer production enterprise, preventive maintenance strategy of filling equipment is put forward based on reliability-centred maintenance (RCM). Firstly, on the basis of analyzing RCM theory and equipment maintenance, the general process of failure analysis of beer production equipment is presented. Secondly, the general production process of bottled beer is analyzed, and the composition of major filling equipment is also introduced in the beer production line. With the help of key indicators of equipment reliability, such as mean time between failure (MTBF), mean time to repair restoration (MTTR) and availability Ai, the fault analysis of filling production line is carried out, and the relevant results are calculated. Then, process failure mode and effect analysis (PFMEA) of filling machine is implemented, and fault tree analysis (FTA) of potential failure modes with high risk priority numbers is also completed. Finally, preventive and maintenance strategies of filling equipment are established on the basis of RCM. Through the research in this paper, maintenance costs and unplanned breakdown hours can be significantly reduced

Key Performance Indicators for Wind Farm Operation and Maintenance

Key performance indicators (KPI) are tools for measuring the progress of a business towards its goals. Although wind energy is now a mature technology, there is a lack of well-defined best practices to asses the performance of a wind farm (WF) during the operation and maintenance (O&M) phase; processes and tools of asset management, such as KPIs, are not yet well-established. This paper presents a review of the major existing indicators used in the O&M of wind farms (WFs), as such information is not available in the literature so far. The different stakeholders involved in the O&M phase are identified and analysed together with their interests, grouped into five categories. A suggestion is made for the properties that KPIs should exhibit. For each category, major indicators that are currently in use are reviewed, discussed and verified against the properties defined. Finally, we propose a list of suitable KPIs that will allow stakeholders to have a better knowledge of an operating asset and make informed decisions. It is concluded that more detailed studies of specific KPIs and the issues of their implementation are probably needed

A framework for modelling mobile radio access networks for intelligent fault management

Postprin

Strategies to Improve Data Quality for Forecasting Repairable Spare Parts

Poor input data quality used in repairable spare parts forecasting by aerospace small and midsize enterprises (SME) suppliers results in poor inventory practices that manifest into higher costs and critical supply shortage risks. Guided by the data quality management (DQM) theory as the conceptual framework, the purpose of this exploratory multiple case study was to identify the key strategies that the aerospace SME repairable spares suppliers use to maximize their input data quality used in forecasting repairable spare parts. The multiple case study comprised of a census sample of 6 forecasting business leaders from aerospace SME repairable spares suppliers located in the states of Florida and Kansas. The sample was collected via semistructured interviews and supporting documentation from the consenting participants and organizational websites. Eight core themes emanated from the application of the content data analysis process coupled with methodological triangulation. These themes were labeled as establish data governance, identify quality forecast input data sources, develop a sustainable relationship and collaboration with customers and vendors, utilize a strategic data quality system, conduct continuous input data quality analysis, identify input data quality measures, incorporate continuous improvement initiatives, and engage in data quality training and education. Of the 8 core themes, 6 aligned to the DQM theory\u27s conceptual constructs while 2 surfaced as outliers. The key implication of the research toward positive social change may include the increased situational awareness for SME forecasting business leaders to focus on enhancing business practices for input data quality to forecast repairable spare parts to attain sustainable profits

Performance Degradation Based on Importance Change and Application in Dissimilar Redundancy Actuation System

The importance measure is a crucial method to identify and evaluate the system weak link. It is widely used in the optimization design and maintenance decision of aviation, aerospace, nuclear energy and other systems. The dissimilar redundancy actuation system (DRAS) is a key aircraft control subsystem which performs aircraft attitude and flight trajectory control. Its performance and reliability directly affect the aircraft flight quality and flight safety. This paper considers the influence of the Birnbaum importance measure (BIM) and integrated importance measure (IIM) on the reliability changes of key components in DRAS. The differences of physical fault characteristics of different components due to performance degradation and power mismatch, are first considered. The reliability of each component in the system is then estimated by assuming that the stochastic degradation process of the DRAS components follows an inverse Gaussian (IG) process. Finally, the weak links of the system are identified using BIM and IIM, so that the resources can be reasonably allocated to the weak links during the maintenance period. The proposed method can provide a technical support for personnel maintenance, in order to improve the system reliability with a minimal lifecycle cost

Reliability Evaluation and Prediction Method with Small Samples

How to accurately evaluate and predict the degradation state of the components with small samples is a critical and practical problem. To address the problems of unknown degradation state of components, difficulty in obtaining relevant environmental data and small sample size in the field of reliability prediction, a reliability evaluation and prediction method based on Cox model and 1D CNN-BiLSTM model is proposed in this paper. Taking the historical fault data of six components of a typical load-haul-dump (LHD) machine as an example, a reliability evaluation method based on Cox model with small sample size is applied by comparing the reliability evaluation models such as logistic regression (LR) model, support vector machine (SVM) model and back propagation neural network (BPNN) model in a comprehensive manner. On this basis, a reliability prediction method based on one-dimensional convolutional neural network-bi-directional long and short-term memory network (1D CNN-BiLSTM) is applied with the objective of minimizing the prediction error. The applicability as well as the effectiveness of the proposed model is verified by comparing typical time series prediction models such as the autoregressive integrated moving average (ARIMA) model and multiple linear regression (MLR). The experimental results show that the proposed model is valuable for the development of reliability plans and for the implementation of reliability maintenance activities

Maintenance policy selection for ships : finding the most important criteria and considerations

Maintenance of technical capital assets is gaining increasing attention, as maintenance is an important contributor to reach the intended life-time of these expensive assets. This paper focusses on maintenance policy selection (MPS) for ships using the Analytic Hierarchy Process. It builds on earlier research where we have investigated MPS specifically for naval ships. Here, we aim to generalize our findings on naval ships towards ships in general, and to elicit the most important criteria for ship MPS. We propose an improved hierarchy of criteria that we use during six workshops at six different companies to investigate MPS. We conclude that it is possible to obtain meaningful outcomes using a single hierarchy of criteria at multiple companies considering various ship types. The workshops reveal that crew safety is the most important criterion when selecting a aintenance policy, followed by reliability and availability—surprisingly, costs minimization is only moderately important. Furthermore, the workshops reveal that softer criteria, such as experience with maintenance and planability, must be included in the MPS process. Finally, we see that, for ship MPS, failure-based maintenance is never preferred, and that there is no clear preference for either time/use-based maintenance or condition-based maintenance

Data-driven maintenance priority and resilience evaluation of performance loss in a main coolant system

The main coolant system (MCS) plays a vital role in the stability and reliability of a nuclear power plant. However, human errors and natural disasters may cause some reactor coolant system components to fail, resulting in severe consequences such as nuclear leakage. Therefore, it is crucial to perform a resilience analysis of the MCS, to effectively reduce and prevent losses. In this paper, a resilience importance measure (RIM) for performance loss is proposed to evaluate the performance of the MCS. Specifically, a loss importance measure (LIM) is first proposed to indicate the component maintenance priority of the MCS under different failure conditions. Based on the LIM, RIMs for single component failure and multiple component failures were developed to measure the recovery efficiency of the system performance. Finally, a case study was conducted to demonstrate the proposed resilience measure for system reliability. Results provide a valuable reference for increasing the system security of the MCS and choosing the appropriate total maintenance cost