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Schmidt, B., Gandhi, K., Wang, L. & Galar, D. (2017). Context preparation for predictive analytics – a case from manufacturing industry. Journal of Quality in Maintenance Engineering, 23(3), 341-354
Open this publication in new window or tab >>Context preparation for predictive analytics – a case from manufacturing industry
2017 (English)In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, no 3, p. 341-354Article in journal (Refereed) Published
Abstract [en]

Purpose

The purpose of this paper is to exemplify and discuss the context aspect for predictive analytics where in parallel condition monitoring (CM) measurements data and information related to the context are gathered and analysed.

Design/methodology/approach

This paper is based on an industrial case study, conducted in a manufacturing company. The linear axis of a machine tool has been selected as an object of interest. Available data from different sources have been gathered and a new CM function has been implemented. Details about performed steps of data acquisition and selection are provided. Among the obtained data, health indicators and context-related information have been identified.

Findings

Multiple sources of relevant contextual information have been identified. Performed analysis discovered the deviations in operational conditions when the same machining operation is repeatedly performed.

Originality/value

This paper shows the outcomes from a case study in real word industrial setup. A new visualisation method of gathered data is proposed to support decision-making process.

Place, publisher, year, edition, pages
Emerald Publishing Limited, 2017
Keywords
Predictive maintenance, Context awareness, Condition monitoring
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-14033 (URN)10.1108/JQME-10-2016-0050 (DOI)000412478700007 ()2-s2.0-85027991065 (Scopus ID)
Available from: 2017-08-24 Created: 2017-08-24 Last updated: 2019-11-21Bibliographically approved
Gerdes, M., Galar, D. & Scholz, D. (2017). Decision Trees and the Effects of Feature Extraction Parameters for Robust Sensor Network Design. Eksploatacja i Niezawodnosc - Maintenance and Reliability, 19(1), 31-42
Open this publication in new window or tab >>Decision Trees and the Effects of Feature Extraction Parameters for Robust Sensor Network Design
2017 (English)In: Eksploatacja i Niezawodnosc - Maintenance and Reliability, ISSN 1507-2711, Vol. 19, no 1, p. 31-42Article in journal (Refereed) Published
Abstract [en]

Reliable sensors and information are required for reliable condition monitoring. Complex systems are commonly monitored by many sensors for health assessment and operation purposes. When one of the sensors fails, the current state of the system cannot be calculated in same reliable way or the information about the current state will not be complete. Condition monitoring can still be used with an incomplete state, but the results may not represent the true condition of the system. This is especially true if the failed sensor monitors an important system parameter. There are two possibilities to handle sensor failure. One is to make the monitoring more complex by enabling it to work better with incomplete data; the other is to introduce hard or software redundancy. Sensor reliability is a critical part of a system. Not all sensors can be made redundant because of space, cost or environmental constraints. Sensors delivering significant information about the system state need to be redundant, but an error of less important sensors is acceptable. This paper shows how to calculate the significance of the information that a sensor gives about a system by using signal processing and decision trees. It also shows how signal processing parameters influence the classification rate of a decision tree and, thus, the information. Decision trees are used to calculate and order the features based on the information gain of each feature. During the method validation, they are used for failure classification to show the influence of different features on the classification performance. The paper concludes by analysing the results of experiments showing how the method can classy different errors with a 75% probability and how different feature extraction options influence the information gain.

Abstract [pl]

Niezawodne monitorowanie stanu wymaga niezawodności czujników i pochodzących z nich informacji. Systemy złożone są zazwyczaj monitorowane przez wiele czujników, co pozwala na  ocenę stanu technicznego oraz aspektów eksploatacyjnych. Gdy jeden z czujników ulega uszkodzeniu, uniemożliwia to obliczenie bieżącego stanu systemu z dotychczasową niezawodnością lub uzyskanie kompletnych informacji o bieżącym stanie. Stan można co prawda monitorować nawet przy niekompletnych danych, ale wyniki takiego monitorowania mogą nie odpowiadać rzeczywistemu stanowi systemu.  Sytuacja taka ma miejsce w szczególności, gdy uszkodzony czujnik jest odpowiedzialny za monitorowanie istotnego parametru systemu. Problem uszkodzenia czujnika można rozwiązywać na dwa sposoby. Pierwszy polega na zwiększeniu złożoności systemu, co umożliwia jego sprawniejsze działanie w sytuacji, gdy dane są niekompletne. Drugim sposobem jest wprowadzenie nadmiarowego sprzętu (hardware'u) lub oprogramowania. Niezawodność czujników stanowi krytyczny aspekt systemu. Oczywiście, ze względu na ograniczenia przestrzenne, ekonomiczne i środowiskowe nie wszystkie czujniki w systemie mogą być nadmiarowe. Redundancja powinna dotyczyć wszystkich czujników, które dostarczają istotnych informacji na temat stanu systemu, natomiast dopuszczalne są błędy mniej ważnych czujników. W niniejszej pracy pokazano jak obliczać istotność informacji o systemie dostarczanych przez poszczególne czujniki z wykorzystaniem metod przetwarzania sygnałów oraz drzew decyzyjnych. Zademonstrowano również w jaki sposób parametry przetwarzania sygnałów wpływają na poprawność klasyfikacji metodą drzewa decyzyjnego, a tym samym na poprawność dostarczanych informacji. Drzew decyzyjnych używa się do obliczania i porządkowania cech w oparciu o przyrost informacji charakteryzujący poszczególne cechy. Podczas weryfikacji zastosowanej metody, drzewa decyzyjne wykorzystano do klasyfikacji uszkodzeń celem przedstawienia wpływu różnych cech na dokładność klasyfikacji. Pracę kończy analiza wyników eksperymentów pokazujących w jaki sposób zastosowana metoda pozwala na klasyfikację różnych błędów z 75-procentowym prawdopodobieństwem oraz jak różne opcje ekstrakcji cech wpływają na przyrost informacji.

Place, publisher, year, edition, pages
Polskie Naukowo - Techniczne Towarzystwo Eksploatacyjne, 2017
Keywords
decision trees, feature extraction, sensor optimization, sensor fusion, sensor selection, drzewa decyzyjne, ekstrakcja cech, optymalizacja czujników, fuzja czujników, dobór czujników
National Category
Computer and Information Sciences Electrical Engineering, Electronic Engineering, Information Engineering Reliability and Maintenance
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-13394 (URN)10.17531/ein.2017.1.5 (DOI)000392367100005 ()2-s2.0-85006786154 (Scopus ID)
Available from: 2017-02-16 Created: 2017-02-16 Last updated: 2019-01-24Bibliographically approved
Gerdes, M., Galar, D. & Scholz, D. (2017). Genetic algorithms and decision trees for condition monitoring and prognosis of A320 aircraft air conditioning. International Journal of Condition Monitoring, 59(8), 424-433
Open this publication in new window or tab >>Genetic algorithms and decision trees for condition monitoring and prognosis of A320 aircraft air conditioning
2017 (English)In: International Journal of Condition Monitoring, ISSN 1354-2575, E-ISSN 1754-4904, Vol. 59, no 8, p. 424-433Article in journal (Refereed) Published
Abstract [en]

Unscheduled maintenance is a large cost driver for airlines, but condition monitoring and prognosis can reduce the number of unscheduled maintenance actions. This paper discusses how condition monitoring can be introduced into most systems by adopting a data-driven approach and using existing data sources. The goal is to forecast the remaining useful life (RUL) of a system based on various sensor inputs. Decision trees are used to learn the characteristics of a system. The data for the decision tree training and classification are processed by a generic parametric signal analysis. To obtain the best classification results for the decision tree, the parameters are optimised by a genetic algorithm. A forest of three different decision trees with different signal analysis parameters is used as a classifier. The proposed method is validated with data from an A320 aircraft from Etihad Airways. Validation shows that condition monitoring can classify the sample data into ten predetermined categories, representing the total useful life (TUL) in 10% steps. This is used to predict the RUL. There are 350 false classifications out of 850 samples. Noise reduction reduces the outliers to nearly zero, making it possible to correctly predict condition. It is also possible to use the classification output to detect a maintenance action in the validation data.

Place, publisher, year, edition, pages
British Institute of Non-Destructive Testing, 2017
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-14118 (URN)10.1784/insi.2017.59.8.424 (DOI)000408276100008 ()2-s2.0-85026321620 (Scopus ID)
Available from: 2017-09-14 Created: 2017-09-14 Last updated: 2019-11-25Bibliographically approved
Schmidt, B., Wang, L. & Galar, D. (2017). Semantic Framework for Predictive Maintenance in a Cloud Environment. In: Roberto Teti, Doriana M D'Addona (Ed.), 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '16: . Paper presented at 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME '16, Ischia, Italy, 20-22 July 2016 (pp. 583-588). Elsevier, 62
Open this publication in new window or tab >>Semantic Framework for Predictive Maintenance in a Cloud Environment
2017 (English)In: 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME '16 / [ed] Roberto Teti, Doriana M D'Addona, Elsevier, 2017, Vol. 62, p. 583-588Conference paper, Published paper (Refereed)
Abstract [en]

Proper maintenance of manufacturing equipment is crucial to ensure productivity and product quality. To improve maintenance decision support, and enable prediction-as-a-service there is a need to provide the context required to differentiate between process and machine degradation. Correlating machine conditions with process and inspection data involves data integration of different types such as condition monitoring, inspection and process data. Moreover, data from a variety of sources can appear in different formats and with different sampling rates. This paper highlights those challenges and presents a semantic framework for data collection, synthesis and knowledge sharing in a Cloud environment for predictive maintenance.

Place, publisher, year, edition, pages
Elsevier, 2017
Series
Procedia CIRP, ISSN 2212-8271 ; 62
Keywords
Predictive maintenance, Knowledge management, Cloud manufacturing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-13568 (URN)10.1016/j.procir.2016.06.047 (DOI)000414525400101 ()2-s2.0-85020714569 (Scopus ID)
Conference
10th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME '16, Ischia, Italy, 20-22 July 2016
Available from: 2017-05-19 Created: 2017-05-19 Last updated: 2019-01-24Bibliographically approved
Galar, D., Kans, M. & Schmidt, B. (2016). Big Data in Asset Management: Knowledge Discovery in Asset Data by the Means of Data Mining. In: Kari T. Koskinen, Helena Kortelainen, Jussi Aaltonen,Teuvo Uusitalo, Kari Komonen, Joseph Mathew & Jouko Laitinen (Ed.), Kari T. Koskinen, Helena Kortelainen, Jussi Aaltonen,Teuvo Uusitalo, Kari Komonen, Joseph Mathew, Jouko Laitinen (Ed.), Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015): . Paper presented at 10th World Congress on Engineering Asset Management (WCEAM 2015), Tampere, Finland, September 2015 (pp. 161-171). Springer
Open this publication in new window or tab >>Big Data in Asset Management: Knowledge Discovery in Asset Data by the Means of Data Mining
2016 (English)In: Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015) / [ed] Kari T. Koskinen, Helena Kortelainen, Jussi Aaltonen,Teuvo Uusitalo, Kari Komonen, Joseph Mathew, Jouko Laitinen, Springer, 2016, p. 161-171Conference paper, Published paper (Refereed)
Abstract [en]

Assets are complex mixes of complex systems, built from components which, over time, may fail. The ability to quickly and efficiently determine the cause of failures and propose optimum maintenance decisions, while minimizing the need for human intervention is necessary. Thus, for complex assets, much information needs to be captured and mined to assess the overall condition of the whole system. Therefore the integration of asset information is required to get an accurate health assessment of the whole system, and determine the probability of a shutdown or slowdown. Moreover, the data collected are not only huge but often dispersed across independent systems that are difficult to access, fuse and mine due to disparate nature and granularity. If the data from these independent systems are combined into a common correlated data source, this new set of information could add value to the individual data sources by the means of data mining. This paper proposes a knowledge discovery process based on CRISP-DM for failure diagnosis using big data sets. The process is exemplified by applying it on railway infrastructure assets. The proposed framework implies a progress beyond the state of the art in the development of Big Data technologies in the fields of Knowledge Discovery algorithms from heterogeneous data sources, scalable data structures, real-time communications and visualizations techniques.

Place, publisher, year, edition, pages
Springer, 2016
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356, E-ISSN 2195-4364
National Category
Reliability and Maintenance
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-12083 (URN)10.1007/978-3-319-27064-7_16 (DOI)000375993100016 ()2-s2.0-85028017463 (Scopus ID)978-3-319-27062-3 (ISBN)978-3-319-27064-7 (ISBN)3-319-27064-8 (ISBN)
Conference
10th World Congress on Engineering Asset Management (WCEAM 2015), Tampere, Finland, September 2015
Available from: 2016-04-01 Created: 2016-04-01 Last updated: 2018-03-28Bibliographically approved
Schmidt, B., Galar, D. & Wang, L. (2016). Big data in maintenance decision support systems: aggregation of disparate data types. In: Euromaintenance 2016 ConferenceProceedings: . Paper presented at Euromaintenance 2016, Athens, 30 May-1 June, 2016 (pp. 503-512).
Open this publication in new window or tab >>Big data in maintenance decision support systems: aggregation of disparate data types
2016 (English)In: Euromaintenance 2016 ConferenceProceedings, 2016, p. 503-512Conference paper, Published paper (Other academic)
Abstract [en]

There is need to obtain reliable information on current and future asset health status to support maintenance decision making process. Within maintenance two main sources of data can be distinguished: Computerized Maintenance Management System (CMMS) for asset registry and maintenance work records; and Condition Monitoring Systems (CM) for direct asset components health state monitoring. There are also other sources of information like SCADA (Supervisory Control and Data Acquisition) for process and control monitoring that can provide additional contextual information leading to better decision making. However data produced acquired and processed and in those system are of disparate types, nature and granularity. This variety includes: event data about failures or performed maintenance work mostly descriptions in unstructured natural language; process variables obtained from different types of sensors and different physical variables from transducers, acquired with different sampling frequencies. Indeed, condition monitoring data are so disparate in nature that maintainers deal with scalars (temperature) through waveforms (vibration) to 2D thermography images and 3D data from machine geometry measuring. Integration and aggregation of those data is not a trivial task and requires modelling of knowledge about those data types, their mutual dependencies and dependencies with monitored processes. There are some attempts of standardisation that try to enable integration of CBM data from different sources. The conversion of those amount of data in meaningful data sets is required for better machine health assessment and tracking within the specific operational context for the asset. This will also enhance the maintenance decision support system with information on how different operational condition can affect the reliability of the asset for concrete contextual circumstances.

Keywords
Big Data, Context-awareness, Maintenance Decision Support System
National Category
Reliability and Maintenance
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-12332 (URN)978-618-82601-0-8 (ISBN)
Conference
Euromaintenance 2016, Athens, 30 May-1 June, 2016
Available from: 2016-06-07 Created: 2016-06-07 Last updated: 2018-03-28Bibliographically approved
Schmidt, B., Galar, D. & Wang, L. (2016). Context Awareness in Predictive Maintenance. In: Uday Kumar, Alireza Ahmadi, Ajit Kumar Verma & Prabhakar Varde (Ed.), Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective (pp. 197-211). Springer
Open this publication in new window or tab >>Context Awareness in Predictive Maintenance
2016 (English)In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar, Alireza Ahmadi, Ajit Kumar Verma & Prabhakar Varde, Springer, 2016, p. 197-211Chapter in book (Refereed)
Abstract [en]

Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance approach utilizes the condition monitoring (CM) data to predict the future machine conditions and makes decisions upon this prediction. Recent development in CM leads to context aware approach where in parallel with CM measurements also data and information related to the context are gathered. Context could be operational condition, history of machine usage and performed maintenance actions. In general more obtained information gives better accuracy of prediction. It is important to track operational context in dynamically changing environment. Today in manufacturing we can observe shift from mass production to mass customisation. This leads to changes from long series of identical products to short series of different variants. Therefore implies changing operational conditions for manufacturing equipment. Moreover, where asset consist of multiple identical or similar equipment the context aware method can be used to combine in reliable way information. This should allow to increase accuracy of prediction for population as a whole as well as for each equipment instances. Same of those data have been already recorded and stored in industrial IT systems. However, it is distributed over different IT systems that are used by different functional units (e.g. maintenance department, production department, quality department, tooling department etc.). This paper is a conceptual paper based on initial research work and investigation in two manufacturing companies from automotive industry.

Place, publisher, year, edition, pages
Springer, 2016
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356
Keywords
Context modeling, Context awareness, Condition monitoring, Condition based maintenance, Predictive maintenance
National Category
Reliability and Maintenance
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-11826 (URN)10.1007/978-3-319-23597-4_15 (DOI)2-s2.0-85013159805 (Scopus ID)978-3-319-23597-4 (ISBN)978-3-319-23596-7 (ISBN)
Available from: 2016-01-13 Created: 2016-01-13 Last updated: 2018-05-07Bibliographically approved
Gerdes, M. & Galar, D. (2016). Fuzzy condition monitoring of recirculation fans and filters. International Journal of Systems Assurance Engineering and Management, 7(4), 469-479
Open this publication in new window or tab >>Fuzzy condition monitoring of recirculation fans and filters
2016 (English)In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 7, no 4, p. 469-479Article in journal (Refereed) Published
Abstract [en]

A reliable condition monitoring is needed to be able to predict faults. Pattern recognition technologies are often used for finding patterns in complex systems. Condition monitoring can also benefit from pattern recognition. Many pattern recognition technologies however only output the classification of the data sample but do not output any information about classes that are also very similar to the input vector. This paper presents a concept for pattern recognition that outputs similarity values for decision trees. Experiments confirmed that the method works and showed good classification results. Different fuzzy functions were evaluated to show how the method can be adapted to different problems. The concept can be used on top of any normal decision tree algorithms and is independent of the learning algorithm. The goal is to have the probabilities of a sample belonging to each class. Performed experiments showed that the concept is reliable and it also works with decision tree forests (which is shown during this paper) to increase the classification accuracy. Overall the presented concept has the same classification accuracy than a normal decision tree but it offers the user more information about how certain the classification is.

Place, publisher, year, edition, pages
Springer, 2016
Keywords
Fuzzy decision trees, Post-fuzzyfication, Condition monitoring, Aircraft
National Category
Computer and Information Sciences Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-13176 (URN)10.1007/s13198-016-0535-y (DOI)000387346100010 ()2-s2.0-85014045708 (Scopus ID)
Available from: 2016-12-01 Created: 2016-12-01 Last updated: 2019-11-26Bibliographically approved
Linnéusson, G., Galar, D. & Wickelgren, M. (2016). In Need for Better Maintenance Cost Modelling to Support the Partnership with Manufacturing (1ed.). In: Uday Kumar, Alireza Ahmadi, Ajit Kumar Verma & Prabhakar Varde (Ed.), Uday Kumar, Alireza Ahmadi, Ajit Kumar Verma & Prabhakar Varde (Ed.), Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective. Paper presented at 3rd International Conference on Reliability Safety and Hazard Conference (ICREsh-ARMS), Luleå University of Technology, 1 June - 4 June, 2015 (pp. 263-282). Springer
Open this publication in new window or tab >>In Need for Better Maintenance Cost Modelling to Support the Partnership with Manufacturing
2016 (English)In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar, Alireza Ahmadi, Ajit Kumar Verma & Prabhakar Varde, Springer, 2016, 1, p. 263-282Conference paper, Published paper (Other academic)
Abstract [en]

The problem of maintenance consequential costs has to be dealt with in manufacturing and is core of this paper. The need of sustainable partnership between manufacturing and maintenance is addressed. Stuck in a best practice thinking, applying negotiation as a method based on power statements in the service level agreement, the common best possible achievable goal is put on risk. Instead, it may enforce narrow minded sub optimized thinking even though not intended so. Unfortunately, the state of origin is not straightforward business. Present maintenance cost modelling is approached, however limits to its ability to address the dynamic complexity of production flows are acknowledged. The practical problem to deal with is units put together in production flows; in which downtime in any unit may or may not result in decreased throughput depending on its set up. In this environment accounting consequential costs is a conundrum and a way forward is suggested. One major aspect in the matter is the inevitable need of shift in mind, from perspective thinking in maintenance and manufacturing respectively towards shared perspectives, nourishing an advantageous sustainable partnership.

Place, publisher, year, edition, pages
Springer, 2016 Edition: 1
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356
Keywords
Maintenance, Cost modelling, Consequential costs, Manufacturing, Production flows, Dynamic complexity, Sustainable partnership, Shift in mind
National Category
Other Engineering and Technologies not elsewhere specified Social Sciences Interdisciplinary
Research subject
Technology; Humanities and Social sciences; Production and Automation Engineering; Followership and Organizational Resilience
Identifiers
urn:nbn:se:his:diva-11619 (URN)10.1007/978-3-319-23597-4_20 (DOI)2-s2.0-85043763107 (Scopus ID)978-3-319-23596-7 (ISBN)978-3-319-23597-4 (ISBN)
Conference
3rd International Conference on Reliability Safety and Hazard Conference (ICREsh-ARMS), Luleå University of Technology, 1 June - 4 June, 2015
Available from: 2015-10-20 Created: 2015-10-20 Last updated: 2018-05-07Bibliographically approved
Fornlöf, V., Galar, D., Syberfeldt, A., Almgren, T., Catelani, M. & Ciani, L. (2016). Maintenance, prognostics and diagnostics approaches for aircraft engines. In: 3rd IEEE International Workshop on Metrology for Aerospace, MetroAeroSpace 2016: Proceedings. Paper presented at 3rd IEEE International Workshop on Metrology for Aerospace (MetroAeroSpace), Florence, Italy, June 21-23, 2016 (pp. 403-407). IEEE
Open this publication in new window or tab >>Maintenance, prognostics and diagnostics approaches for aircraft engines
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2016 (English)In: 3rd IEEE International Workshop on Metrology for Aerospace, MetroAeroSpace 2016: Proceedings, IEEE, 2016, p. 403-407Conference paper, Published paper (Refereed)
Abstract [en]

In avionics application one of the most important competition factors is the reliability, given that the failure occurrence may leads to a critical state for the functioning of the aircraft. Different maintenance, prognostics and diagnostics approaches are possible with the final aim to optimize both system's availability and safety. Aircraft engines represent a safety critical part of the airplane. For this reason it is a key issue to allocate the proper amount of maintenance at each individual maintenance event. In this paper a mathematical replacement model is proposed to guarantee that the correct amount of maintenance is performed.

Place, publisher, year, edition, pages
IEEE, 2016
Keywords
maintenance, prognostics, diagnostics, aircraft engines, remaining useful life
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-13303 (URN)000389769800074 ()2-s2.0-84991757125 (Scopus ID)978-1-4673-8292-2 (ISBN)978-1-4673-8293-9 (ISBN)
Conference
3rd IEEE International Workshop on Metrology for Aerospace (MetroAeroSpace), Florence, Italy, June 21-23, 2016
Available from: 2017-01-11 Created: 2017-01-11 Last updated: 2018-03-28Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4107-0991

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