Intelligent prognostics tools and e-maintenance

Energies

Most manufacturing and process industries require compressed air to such an extent that in Europe, for instance, about 10% of the total electrical energy consumption of industries is due to compressed air systems (CAS). However, energy efficiency in compressed air production and handling is often ignored or underestimated, mainly because of the lack of awareness about its energy consumption, caused by the absence of proper measurements on CAS in most industrial plants. Therefore, any effective energy saving intervention on generation, distribution and transformation of compressed air requires proper energy information management. In this paper we demonstrate the importance of monitoring and controlling energy performance in compressed air generation and use, to enable energy saving practices, to enhance the outcomes of energy management projects, and to obtain additional benefits for non-energy-related activities, such as operations, maintenance management and energy accounting. In ...

Journal of Quality in Maintenance Engineering, 2014

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Journal of Modern Transportation, 2016

The volume of rail traffic was increased by 5 % from 2006 to 2010, in Sweden, due to increased goods and passenger traffic. This increased traffic, in turn, has led to a more rapid degradation of the railway track, which has resulted in higher maintenance costs. In general, degradation affects comfort, safety, and track quality, as well as, reliability, availability, speed, and overall railway performance. This case study investigated the needs of railway stakeholders responsible for analysing the track state and what information is necessary to make good maintenance decisions. The goal is to improve the railway track performance by ensuring increased availability, reliability, and safety, along with a decreased maintenance cost. Interviews of eight experts were undertaken to learn of general areas in need of improvement, and a quantitative analysis of condition monitoring data was conducted to find more specific information. The results show that by implementing a long-term maintenance strategy and by conducting preventive maintenance actions maintenance costs would be reduced. In addition to that, problems with measured data, missing data, and incorrect location data resulted in increased and unnecessary maintenance tasks. The conclusions show that proactive solutions are needed to reach the desired goals of improved safety, improved availability, and improved reliability. This also includes the development of a visualisation tool and a life cycle cost model for maintenance strategies.

IOP Conference Series: Materials Science and Engineering

From the sustainable production perspective, the concept of sustainable management of the maintenance system covers activities/processes implemented at all levels of management (strategic, tactical and operational), including all the factors (TBL) and functions enabling achievement of goals and providing ability to create value in the long-term horizon. Considering the fact that sustainable development is a concept that assumes long-term sustainable results, sustainable maintenance management should refer to all levels of management, including strategic, tactical and operational. Therefore, considering maintenance from the perspective of sustainable development requires knowledge, measurement and assessment of economic, ecological and social effects of maintenance processes realization. Hence, it is necessary to develop a new approach to maintenance assessment that complements the conventionaland so far in most cases economic only-aspects. Based on the analysis of the literature on sustainable production and maintenance, factors which can significantly contribute to effective dealing with challenges of sustainable production by implementation of maintenance activities have been identified.

MATEC Web of Conferences

The Bali Aga is an Indigenous people of Bali scattered in remotes areas such as mountains, hills, along the Batur Lake, and highlands. The Bali Aga people still practices the original tradition and differ from other Balinese people who mostly live in mainland area which differentiated by the reign of Majapahit Kingdom. Bali Aga refused Majapahit’s influences, in contrast other Balinese followed the Majapahit rules that known as Bali Apanaga. Bali Aga still conserves original tradition but cannot avoid the increasing of development in technology and communication. Sukawana Village is one of Bali Aga village still conserve the Indigenous culture and tradition that located in highland of Kintamani Regency. The research aims to seek how the people adhere the tradition, norm, and values; and to explore the housing typology in Sukawana Village that faces on influence of development and threaten by globalization. The research primarily involves a qualitative method with includes an extensi...

Journal of Intelligent Manufacturing, 2014

Reliability of prognostics and health management systems (PHM) relies upon accurate understanding of critical components' degradation process to predict the remaining useful life (RUL). Traditionally, degradation process is represented in the form of data or expert models. Such models require extensive experimentation and verification that are not always feasible. Another approach that builds up knowledge about the system degradation over the time from component sensor data is known as data driven. Data driven models, however, require that sufficient historical data have been collected. In this paper, a two phases data driven method for RUL prediction is presented. In the offline phase, the proposed method builds on finding variables that contain information about the degradation behavior using unsupervised variable selection method. Different health indicators (HI) are constructed from the selected variables, which represent the degradation as a function of time, and saved in the offline database as reference models. In the online phase, the method finds the most similar offline health indicator, to the online health indicator, using k-nearest neighbors (k-NN) classifier to use it as a RUL predictor. The method finally estimates the degradation state using discrete Bayesian

Energies

This paper presents an empirical study on the impact of maintenance function on more sustainable manufacturing processes. The work methodology comprises four stages. At first, ten factors of maintenance activities from a sustainable manufacturing point of view were identified. Then, in the second stage, the matrix of crossed impact multiplications applied to a classification (MICMAC) was carried out to categorize these factors based on their influence and dependence values. In the third stage, the criteria for evaluation of maintenance factors were defined, then the fuzzy analytic hierarchy process (F-AHP) was applied to determine their relative weights. In the last stage, the results of MICMAC and F-AHP analyses were used as inputs for the fuzzy technique for order preference by similarity to ideal solution (F-TOPIS) to generate aggregate scores and selection of the most important maintenance factors that have an impact on sustainable manufacturing processes. A numerical example is...

The International Journal of Advanced Manufacturing Technology, 2011

This paper describes the development and validation of two software tools to explore the relationship between prognostics and health management (PHM) technologies and servitization in the machine tool industry. The software tools are based on software tools that were originally developed to analyse the costs and benefits of PHM technology in the aerospace industry. The development was initiated following an extensive review of relevant literature on both servitization and PHM technology. This review revealed the fact that the servitization literature makes almost no mention of the use of PHM technology to support servitization and the PHM literature makes almost no mention of the servitization business model that might justify expenditure on this technology. The tools model the life cycles of two sets of machine tools, with and without PHM technology, before calculating the net present value of the costs and benefits of PHM in the context of a servitization business model. Although the data used in the tools are hypothetical, the approach taken in the models was validated through interviews with a major machine tool vendor. This research extends existing knowledge on servitization as published in the management literature by linking it to research into PHM as published in the systems engineering literature. The paper concludes by indicating the need for more empirical data on the reliability of machine tool subsystems in order to increase confidence in the value of PHM technology to support machine tool servitization.

Sensors, 2015

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it

Production Planning & Control, 2016

The advanced use of the Information and Communication Technologies is evolving the way that systems are managed and maintained. A great number of techniques and methods have emerged in the light of these advances allowing to have an accurate and knowledge about the systems' condition evolution and remaining useful life. The advances are recognized as outcomes of an innovative discipline, nowadays discussed under the term of Prognostics and Health Management (PHM). In order to analyze how maintenance will change by using PHM, a conceptual model is proposed built upon three views. The model highlights: (i) how PHM may impact the definition of maintenance policies; (ii) how PHM fits within the Condition Based Maintenance (CBM) and (iii) how PHM can be integrated into Reliability Centered Maintenance (RCM) programs. The conceptual model is the research finding of this review note and helps to discuss the role of PHM in advanced maintenance systems.

Mathematics, 2019

This study deals with the preventive maintenance optimization problem based on a reliability threshold. The conditional reliability threshold is used instead of the system reliability threshold. Then, the difference between the two thresholds is discussed. The hybrid failure rate model is employed to represent the effect of imperfect preventive maintenance activities. Two maintenance strategies are proposed under two types of reliability constraints. These constraints are set to consider the cost-effective maintenance strategy and to evaluate the balancing point between the expected total maintenance cost rate and the system reliability. The objective of the proposed maintenance strategies is to determine the optimal conditional reliability threshold together with the optimal number of preventive maintenance activities that minimize the expected total maintenance cost per unit time. The optimality conditions of the proposed maintenance strategies are also investigated and shown via ...

Journal of Manufacturing Science and Engineering, 2019

Recent efforts in smart manufacturing (SM) have proven quite effective at elucidating system behavior using sensing systems, communications, and computational platforms, along with statistical methods to collect and analyze the real-time performance data. However, how do you effectively select where and when to implement these technology solutions within manufacturing operations? Furthermore, how do you account for the human-driven activities in manufacturing when inserting new technologies? Due to a reliance on human problem-solving skills, today’s maintenance operations are largely manual processes without wide-spread automation. The current state-of-the-art maintenance management systems and out-of-the-box solutions do not directly provide necessary synergy between human and technology, and many paradigms ultimately keep the human and digital knowledge systems separate. Decision makers are using one or the other on a case-by-case basis, causing both human and machine to cannibali...

Mathematics

Predictive maintenance can be efficiently improved by studying the sensitivity of the maintenance decisions with respect to changes in the proposed model parameters (costs, duration of reparation, etc.). To address this issue, we first propose an original approach that includes both maintenance costs and maintenance risks in the same objective function to minimize. This approach uses the RUL as an indicator of the health state of the system and supposes that the system is under regular inspections and can only be replaced by a new system in case of serious deterioration or failure. Then, we present a process of human decision making under uncertainty based on several criteria. Finally, we study and analyze the influence of the model parameters and their implications on the obtained maintenance policies. The study will lead to some recommendations that can improve the predictive maintenance decisions and help experts better handle maintenance costs.

Journal of Computing and Information Science in Engineering, 2020

Prognostics and health management (PHM) of bearings is crucial for reducing the risk of failure and the cost of maintenance for rotating machinery. Model-based prognostic methods develop closed-form mathematical models based on underlying physics. However, the physics of complex bearing failures under varying operating conditions is not well understood yet. To complement model-based prognostics, data-driven methods have been increasingly used to predict the remaining useful life (RUL) of bearings. As opposed to other machine learning methods, ensemble learning methods can achieve higher prediction accuracy by combining multiple learning algorithms of different types. The rationale behind ensemble learning is that higher performance can be achieved by combining base learners that overestimate and underestimate the RUL of bearings. However, building an effective ensemble remains a challenge. To address this issue, the impact of diversity in base learners and extracted features in diff...

Energies

Prognostics and Health Management (commonly called PHM) is a field that focuses on the degradation mechanisms of systems in order to estimate their health status, anticipate their failure and optimize their maintenance. PHM uses methods, tools and algorithms for monitoring, anomaly detection, cause diagnosis, prognosis of the remaining useful life (RUL) and maintenance optimization. It allows for permanently monitoring the health of the system and provides operators and managers with relevant information to decide on actions to be taken to maintain the system in optimal operational conditions. This paper aims to present the emergence of the PHM thematically to describe the subjacent processes, particularly prognosis, how it supplies the different maintenance strategies and to explain the benefits that can be anticipated. More specifically, this paper establishes a state of the art in prognostic methods used today in the PHM strategy. In addition, this paper shows the multitude of po...

Applied Sciences, 2021

The maintenance of oil pumps is a complex task for any operating organization, and for an industrial enterprise in the oil and gas sector of the economy, this issue has a high degree of urgency. One of the reasons for this is a wide spread of pumping equipment in all areas of oil and gas enterprises. At the same time, an aggressive environment, uneven load, remote facilities, and harsh climatic zones (especially in the areas of the Arctic region or production platforms) are factors that make it relevant to develop special systems that help or simplify the maintenance of pumping equipment. Dynamic modeling is one of the modern technologies which allows for solving the urgent issue of assessing the technical condition of equipment. It is the basis of systems that carry out diagnostics and prognostic calculations and allow for assessing the dynamic state of objects under various conditions of their operation, among other functions. Augmented reality technology is a technology that allo...

Proceedings of the International Conference on Intelligent Science and Technology, 2018

Impacts of oil and gas production operations are always very obvious when there is imbalanced operation, uncontrolled stoppage or catastrophic failure of the system during normal operations. These impacts may range from high flaring and venting of associated petroleum gas, oil release or spillage, equipment damage, fire outbreak to even fatality. Possible causes of imbalanced operations or system failure are categorised into process upset, system degradation, ineffective operation and maintenance procedures and human errors. Effective maintenance strategy integrates major components of the system; people (human factors), operation and maintenance procedures (process) and production plant (technology) to develop an intelligent maintenance solution that is capable of monitoring and detecting fault in the system at incipient stage before operational integrity is compromised. This paper deploys data-based analytics technique to develop condition-based predictive maintenance system to monitor, predict and classify performance of gas processing system. Exhaust gas temperature (EGT) of Gas Turbine Engine (GTE) is one of the operating and control parameters associated with efficiency of the GTE operation. The EGT is measured using several thermocouples, temperature sensors spaced equidistant around the circumference of the exhaust duct of the GTE. Neural network technique of multisensory data fusion is integrated with intelligent maintenance system to monitor performance of GTE, detect fault and classify performance of GTE to optimal, average and abnormal performance.

Global Energy Interconnection, 2021

Using electric vehicles (EVs) for transportation is considered as a necessary component for managing sustainable development and environmental issues. The present concerns regarding the environment, such as rapid fossil fuel depletion, increases in air pollution, accelerating energy demands, global warming, and climate change, have paved the way for the electrification of the transport sector. EVs can address all of the aforementioned issues. Portable power supplies have become the lifeline of the EV world, especially lithium-ion (Li-ion) batteries. Li-ion batteries have attracted considerable attention in the EV industry, owing to their high energy density, power density, lifespan, nominal voltage, and cost. One major issue with such batteries concerns providing a quick and accurate estimation of a battery's state and health; therefore, accurate determinations of the battery's performance and health, as well as an accurate prediction of its life, are necessary to ensure reliability and efficiency. This study conducts a review of the technological briefs of EVs and their types, as well as the corresponding battery characteristics. Various aspects of recent research and developments in Liion battery prognostics and health monitoring are summarized, along with the techniques, algorithms, and models used for current/voltage estimations, state-of-charge (SoC) estimations, capacity estimations, and remaining-useful-life predictions.

Scientific Reports

Selecting the appropriate maintenance type is a challenging task that involves multiple criteria working together. This decision has a significant impact on the organization and its overall market sustainability. The primary categorization of maintenance consists of two main types: corrective maintenance and preventive maintenance. All other classifications are encompassed within these two categories. For instance, preventive maintenance can be further classified as either predictive maintenance or periodic maintenance. Given the importance of this decision, this paper discusses the optimal maintenance type under different conditions. The scale of the business, the cost of machine failure, the effect of machine failure on the production schedule, the effect of machine failure on worker safety and the workplace environment, the availability of spare parts, the lifespan of the machine, and the manufacturing process are some of the factors that are covered in this paper. This paper pri...

International Journal of Operations & Production Management, 2020

Purpose-Scholars and practitioners within industrial maintenance management are focused on understanding antecedents, correlates and consequences of the concept of "Smart Maintenance," which consists of the four dimensions, namely, data-driven decision-making, human capital resource, internal integration and external integration. In order to facilitate this understanding, valid and reliable empirical measures need to be developed. Therefore, this paper aims to develop a psychometric instrument that measures the four dimensions of Smart Maintenance. Design/methodology/approach-The results from two sequential empirical studies are presented, which include generating items to represent the constructs, assessment of content validity, as well as an empirical pilot test. With input from 50 industrial experts, a pool of 80 items that represent the constructs are generated. Thereafter, using data from 42 industrial and academic raters, the content validity of all items is assessed quantitatively. Finally, using data from 59 manufacturing plants, the dimensionality and factor structure of the instrument are tested. Findings-The authors demonstrate content validity and provide evidence of good model fit and psychometric properties for one-factor models with 8-11 items for each of the four constructs, as well as a combined 24-item four-factor model. Originality/value-The authors provide recommendations for scholarly use of the instrument in further theory-testing research, as well as its practical use to assess, benchmark and longitudinally evaluate Smart Maintenance within the manufacturing industry.

Journal of Quality in Maintenance Engineering, 2019

The purpose of this research paper is firstly to understand data management challenges in E-Maintenance systems from a holistically viewpoint through summarizing the earlier scattered research in the field, and secondly to present a conceptual approach for addressing these challenges in practice. Design/Methodology/Approach The study is realized as a combination of a literature review and by the means of analyzing the practices on an industry leader in manufacturing and maintenance services. Findings This Research provides a general understanding over data management challenges in E-Maintenance and summarizes their associated proposed solutions. In addition, this paper lists and exemplifies different types and sources of data which can be collected in E-Maintenance, across different organizational levels. Analyzing the data management practices of an E-Maintenance industry-leader provides a conceptual approach to address identified challenges in practice Research limitations/implications (if applicable) Since this paper is based on studying the practices of a single company, it might be limited to generalize the results. Future research topics can focus on each of mentioned data management challenges and also validate the applicability of presented model in other companies and industries. Practical Implications (if applicable) Understanding the E-Maintenance related challenges helps maintenance managers and other involved stakeholders in E-Maintenance systems to better solve the challenges.

Buildings

Today, building maintenance and repair (M&R) is a neglected aspect of the construction business throughout a building’s entire life cycle. Selecting appropriate M&R strategies is crucial, particularly for emerging economies like Iraq with severely constrained resources. This study seeks to identify the primary selection criteria for M&R methods of healthcare building facilities (HBFs) in Iraq. A comprehensive desktop literature analysis was undertaken to extract and determine the essential selection criteria for the most suited M&R approaches to buildings in general. Then, two rounds of the Delphi survey were conducted to consolidate the specific selection criteria to suit the circumstances of Iraq and HBFs. A total of 21 sub-criteria were identified and divided into six main groups. The main criteria and the associated sub-criteria were then analyzed and ranked using the fuzzy analytic hierarchy process (FAHP) technique. The ranking of the various main criteria revealed that the “c...

Applied sciences, 2018

The increasing interconnection of machines in industrial production on one hand, and the improved capabilities to store, retrieve, and analyze large amounts of data on the other, offer promising perspectives for maintaining production machines. Recently, predictive maintenance has gained increasing attention in the context of equipment maintenance systems. As opposed to other approaches, predictive maintenance relies on machine behavior models, which offer several advantages. In this highly interdisciplinary field, there is a lack of a literature review of relevant research fields and realization techniques. To obtain a comprehensive overview on the state of the art, large data sets of relevant literature need to be considered and, best case, be automatically partitioned into relevant research fields. A proper methodology to obtain such an overview is the bibliometric analysis method. In the presented work, we apply a bibliometric analysis to the field of equipment maintenance systems. To be more precise, we analyzed clusters of identified literature with the goal to obtain deeper insight into the related research fields. Moreover, cluster metrics reveal the importance of a single paper and an investigation of the temporal cluster development indicates the evolution of research topics. In this context, we introduce a new measure to compare results from different time periods in an appropriate way. In turn, among others, this simplifies the analysis of topics, with a vast amount of subtopics. Altogether, the obtained results particularly provide a comprehensive overview of established techniques and emerging trends for equipment maintenance systems.

Vestnik of Don State Technical University, 2020

Introduction . Digital systems that control the maintenance of separate mechatronic process facilities (MPF) and sets of production machines are mainly considered. Numerous issues on maintaining the reliability of the condition and emerging malfunctions, as well as the multifactorial nature of the using the existing monitoring and diagnostic systems, are noted. In this regard, the relevance of the tasks of developing methods of processing equipment maintenance to make decisions under the data veracity and limitation is specified. Materials and Methods . To analyze the criticality of the technical condition, an assessment of the efficiency of the autonomous control of the device state is formed. The method of the neuro-fuzzy system is used to determine the aggregate criterion of criticality. It is proposed to apply this approach to develop recommendations on equipping a production facility with the necessary means of maintaining overall performance and reliability. Results . The solu...

International Journal of Production Research, 2020

An integrated optimal design of attributes sampling and proactive maintenance for a discrete manufacturing system is studied in this paper. In the system, the failure of a critical component causes the process to shift. The new mathematical model for online sampling of the discrete manufacturing system is based on the binomial and truncated negative binomial distributions. In addition to performing scheduled maintenance and unscheduled corrective maintenance at the time of a true alarm, an additional maintenance opportunity when a false alarm occurs is also considered. The optimal scheduled maintenance time and sampling parameters are determined by solving a mixed integer nonlinear programming problem to minimise the long-run cost rate. A numerical example is provided to illustrate the proposed integrated attributes sampling and maintenance plan. The results show that the integrated approach outperforms the alternatives that consider different models separately. More importantly, showing the benefit of doing maintenance upon a false alarm provides a stakeholder with a new idea in managing a deteriorating manufacturing system.