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Mishra, Madhav
Publications (10 of 20) Show all publications
Mishra, M. & van Riet, M. (2018). A channel model for power line communication using 4PSK technology for diagnosis: Some lessons learned. International Journal of Electrical Power & Energy Systems, 95, 617-634
Open this publication in new window or tab >>A channel model for power line communication using 4PSK technology for diagnosis: Some lessons learned
2018 (English)In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 95, p. 617-634Article in journal (Refereed) Published
Abstract [en]

Modern smart grids and smart metering concepts are based on reliable digital communications. The absence of dedicated communications media, such as telephone lines or fibre optics within a power line network, can make transmission challenging. Electrical power companies are interested in implementing an overall communicating power line network. The power line communication (PLC) system uses the electric power distribution grid as a data transmission medium. The data transmission problem resulted due to poorly developed Medium Voltage Network of PLC Channel Model and challenges in data transmission technology, so this hampers better performance. This paper studies PLC over a medium voltage network with a goal of achieving greater bit rates and communication that is more reliable over power lines. It presents a complete channel model of a PLC system and evaluation of Bit Error Rate (BER) of Phase Shift Keying (PSK) when corrupted with noise. It calculates the number of sections between two substations to determine signal loss. The PSK modulation scheme in simulation is experimentally found to be more robust against such power line distortions as noise for point-to-point transmission. The model and calculations use Matlab and QUCS.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Engineering and Technology Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-65791 (URN)10.1016/j.ijepes.2017.09.020 (DOI)000416497900055 ()2-s2.0-85029721539 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-09-25 (andbra)

Available from: 2017-09-24 Created: 2017-09-24 Last updated: 2018-05-07Bibliographically approved
Mishra, M., Martinsson, J., Rantatalo, M. & Goebel, K. (2018). Bayesian hierarchical model-based prognostics for lithium-ion batteries. Reliability Engineering & System Safety, 172, 25-35
Open this publication in new window or tab >>Bayesian hierarchical model-based prognostics for lithium-ion batteries
2018 (English)In: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 172, p. 25-35Article in journal (Refereed) Published
Abstract [en]

To optimise operation and maintenance, knowledge of the ability to perform the required functions is vital. The ability is governed by the usage of the system (operational issues) and availability aspects like reliability of different components. This paper proposes a Bayesian hierarchical model (BHM)-based prognostics approach applied to Li-ion batteries, where the goal is to analyse and predict the discharge behaviour of such batteries with variable load profiles and variable amounts of available discharge data. The BHM approach enables inferences for both individual batteries and groups of batteries. Estimates of the hierarchical model parameters and the individual battery parameters are presented, and dependencies on load cycles are inferred. A BHM approach where the operational and reliability aspects end of life (EoD) and end of life (EoL) is studied where its shown that predictions of EoD can be made accurately with a variable amount of battery data. Without access to measurements, e.g. predicting a new battery, the predictions are based only on the prior distributions describing the similarity within the group of batteries and their dependency on the load cycle. A discharge cycle dependency can also be identified in the result giving the opportunity to predict the battery reliability.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Other Civil Engineering Probability Theory and Statistics
Research subject
Operation and Maintenance; Mathematical Statistics
Identifiers
urn:nbn:se:ltu:diva-66884 (URN)10.1016/j.ress.2017.11.020 (DOI)000424960000003 ()2-s2.0-85037540599 (Scopus ID)
Note

Validerad;2018;Nivå 2;2017-12-14 (andbra)

Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2018-06-13Bibliographically approved
Mishra, M. (2018). Prognostics and Health Management of Engineering Systems for Operation and Maintenance Optimisation. (Doctoral dissertation). Luleå: Luleå University of Technology
Open this publication in new window or tab >>Prognostics and Health Management of Engineering Systems for Operation and Maintenance Optimisation
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Prognostik ochtillståndskontroll av tekniska system för optimering av drift och underhåll
Abstract [en]

Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Prognostics is defined as the estimation of remaining useful life. It is the most critical part of this process and is a key feature of maintenance strategies since the estimation of the remaining useful life (RUL) is essential to avoiding unscheduled maintenance. Prognostics is relatively immature compared to diagnostics, and a challenging task facing the research community is to overcome some of the major barriers to the application of PHM technologies to real-world industrial systems. This thesis presents research into methods for addressing these challenges for industrial applications. The thesis work focuses on prognostic approaches for three different engineering systems with different characteristics in terms of the prognostics of operation and maintenance aspects. The aim of this thesis is to facilitate better operation and maintenance decision making. The main benefits of prognostics are in anticipating future failures to increase uptime, implementing dynamic maintenance planning toward decreasing total costs and decreasing energy consumption. Therefore, there is a need for methods that can be used in these cases to classify the health states and predict the remaining useful life of assets. The studied engineered systems in this thesis are railway tracks, batteries and rolling element bearings.

In a railway system, the track geometry has to be maintained to provide a safe and functional track. Therefore, track degradation of ballasted railway track systems has to be measured on a regular basis to determine when to maintain the track by tamping. Tamping aims to restore the geometry to its original state to ensure an efficient, comfortable and safe transportation system. To minimise the disruption introduced by tamping, this action has to be planned in advance. Track degradation forecasts derived from regression methods are used to predict when the standard deviation of a specific track section will exceed a predefined maintenance or safety limit. In this thesis, a particle-filter-based prognostic approach for railway track degradation for railway switches is proposed. The particle-filter-based prognostic will generate a probabilistic prediction result that can facilitate risk-based decision making.

Li-ion batteries are another important components in engineering system and battery life prediction matters. Li-ion batteries are commonly used in a wide range of consumer electronic devices, electric vehicles of all types, military electronics,  maritime applications, astronaut suits, and space systems. Many critical operations depend on such batteries as a reliable power source. It is therefore important for the user to get an accurate estimate of the battery end of discharge because an unforeseen discharge of a battery could have catastrophic consequences. To address this issue, a Bayesian hierarchical model (BHM)-based prognostics approach was applied to Li-ion batteries, where the goal was to analyse and predict the discharge behaviour of such batteries with variable load profiles and variable amounts of available discharge data. The BHM approach enables inferences for both individual batteries and groups of batteries. Estimates of the hierarchical model parameters and the individual battery parameters are presented, and dependencies on load cycles are inferred. The operational and reliability aspects, end of life (EoD) and end of life (EoL), are studied; it is shown that predictions of the EoD can be made accurately with a variable amount of battery data. Without access to measurements, e.g., predicting performance of a new battery, the predictions are based only on the prior distributions describing the similarity within a group of batteries and their dependency on the load cycle. A discharge cycle dependency is identified helping with estimation of battery reliability.

Batteries have become a very important engineering system, rotating machines have played an important role, possibly the most important role, in the field of engineering. They have been used to drive the industrialisation of the world.

For rotating machinery, rolling element bearings are a vital component and have several failure modes. Hence, there is  significant need to monitor the health of bearings and detect degraded  states and  upcoming  failures  as  early  as  possible  to avoid serious accidents and equipment failure. For  rolling element bearings, an investigation in using FEM models for estimating bearing forces from acceleration measurements was conducted. This study was performed at a paper mill where a bearing monitoring system was installed. The purpose of the study was to feed the bearing rating life L10 (a bearing life length calculation) with estimations of the dynamic bearing forces  to continuously update the L10 calculation by generating a dynamic L10. In a second study for bearing lifetime prediction, a Bayesian hierarchical modelling (BHM) approach , which includes different data sources, such as enveloped acceleration data, in combination with degradation models and prior distributions of other parameters, was developed, in which the bearing rating life calculation can be included. The proposed prognostics methodology can be used in cases where there is less  or noisy data. The above approach can even be used in cases whereby there is no prior knowledge of the system or little measurement data on the conditions. The presented BHM approach can also be used to predict the remaining useful life (RUL) of bearings both in situations in which the bearing is considered to be in a healthy state and in situations after a defect has been detected.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2018
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
Prognostics and Health Management (PHM), Diagnostics, Prognostics, Bayesian, Hierarchical, L10, Prediction, Bearing, Li-ion battery, RUL, Particle filter, Model-based, Data-driven, Algorithms, Railway track geometry
National Category
Engineering and Technology Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-68345 (URN)978-91-7790-106-8 (ISBN)978-91-7790-107-5 (ISBN)
Public defence
2018-05-31, C305, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2018-04-16 Created: 2018-04-13 Last updated: 2018-05-31Bibliographically approved
Mishra, M. (2017). Model-Based Prognostic Approach for Battery Variable Loading Conditions: Some Accuracy Improved. In: Proceedings of the Asia Pacific Conference of  the Prognostics and Health Management Society 2017: . Paper presented at Asia Pacific Conference of the Prognostics and Health Management Society 2017, Jeju, Korea, July 12-15 2017 (pp. 147-149).
Open this publication in new window or tab >>Model-Based Prognostic Approach for Battery Variable Loading Conditions: Some Accuracy Improved
2017 (English)In: Proceedings of the Asia Pacific Conference of  the Prognostics and Health Management Society 2017, 2017, p. 147-149Conference paper, Published paper (Refereed)
Abstract [en]

Prognostics and Health Management (PHM) using a proper condition-based maintenance (CBM) deployment is a worldwide-accepted strategy and has grown very popular in many industries and academia over the past decades. PHM can provide a state assessment of the future health of systems or components, e.g. when a degraded state has been found. Using this technology, one can estimate how long it will take before the equipment will reach a failure threshold, in future operating conditions and future environmental conditions.

This paper deals with the improvement of prognostic accuracy for battery discharge prediction and compares with previous results done by the other researchers. In this paper, physical models and measurement data were used in the prognostic development in such a way that the degradation behaviour of the battery could be modelled and simulated in order to predict the end-of-discharge (EoD). A particle filter turned out to be the method of choice in performing the state assessment and predicting the future degradation. 

Keywords
Prognostics, Particle filter, Battery, EoD
National Category
Engineering and Technology
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-66596 (URN)978-1-936263-27-1 (ISBN)
Conference
Asia Pacific Conference of the Prognostics and Health Management Society 2017, Jeju, Korea, July 12-15 2017
Projects
SKF UTC
Available from: 2017-11-16 Created: 2017-11-16 Last updated: 2018-05-07Bibliographically approved
Mishra, M., Odelius, J., Thaduri, A., Nissen, A. & Rantatalo, M. (2017). Particle filter-based prognostic approach for railway track geometry. Mechanical systems and signal processing, 96, 226-238
Open this publication in new window or tab >>Particle filter-based prognostic approach for railway track geometry
Show others...
2017 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 96, p. 226-238Article in journal (Refereed) Published
Abstract [en]

Track degradation of ballasted railway track systems has to be measured on a regular basis, and these tracks must be maintained by tamping. Tamping aims to restore the geometry to its original shape to ensure an efficient, comfortable and safe transportation system. To minimize the disturbance introduced by tamping, this action has to be planned in advance. Track degradation forecasts derived from regression methods are used to predict when the standard deviation of a specific track section will exceed a predefined maintenance or safety limit. This paper proposes a particle filter-based prognostic approach for railway track degradation; this approach is demonstrated by examining different railway switches. The standard deviation of the longitudinal track degradation is studied, and forecasts of the maintenance limit intersection are derived. The particle filter-based prognostic results are compared with the standard regression method results for four railway switches, and the particle filter method shows similar or better result for the four cases. For longer prediction times, the error of the proposed method is equal to or smaller than that of the regression method. The main advantage of the particle filter-based prognostic approach is its ability to generate a probabilistic result based on input parameters with uncertainties. The distributions of the input parameters propagate through the filter, and the remaining useful life is presented using a particle distribution.

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-63143 (URN)10.1016/j.ymssp.2017.04.010 (DOI)000401886800015 ()2-s2.0-85019145932 (Scopus ID)
Note

Validerad; 2017; Nivå 2; 2017-04-25 (andbra)

Available from: 2017-04-25 Created: 2017-04-25 Last updated: 2018-07-10Bibliographically approved
Mishra, M., Rantatalo, M. & Odelius, J. (2016). A Model-based Prognostic Approach to Predict Remaining Useful Life of Components. In: Jyoti K. Sinha, Akilu Yunusa-Kaltungo, Wolfgang Hahn (Ed.), Proceedings of 1st International Conference on Maintenance Engineering, IncoME-I, 2016: . Paper presented at 1st International Conference on Maintenance Engineering, IncoME-I, The University of Manchester, UK, August 30-31, 2016. , Article ID ME2016_1147.
Open this publication in new window or tab >>A Model-based Prognostic Approach to Predict Remaining Useful Life of Components
2016 (English)In: Proceedings of 1st International Conference on Maintenance Engineering, IncoME-I, 2016 / [ed] Jyoti K. Sinha, Akilu Yunusa-Kaltungo, Wolfgang Hahn, 2016, article id ME2016_1147Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

One of the major problems in the industry is the extension of the useful life of high-performance systems. Proper maintenance plays an important role by extending the useful life, reducing the lifecycle costs and improving the reliability and availability. Health management using a proper condition-based maintenance (CBM) deployment is a worldwide accepted strategy and has grown very popular in many industries over the past decades. A case of CBM is when the maintenance decision is taken based on a forecast of the asset state. This strategy is called predictive maintenance or prognostic health management (PHM). PHM is an engineering discipline that aims to maintain the system behaviour and function, and assure the mission success, safety and effectiveness. This strategy is relevant in environments where the prediction of a failure and the prevention and mitigation of its consequences increase the profit and safety of the facilities concerned. Prognosis is the most critical part of this process and is nowadays recognized as a key feature in maintenance strategies since estimation of the remaining useful life (RUL) is essential.

PHM can provide a state assessment of the future health of systems or components, e.g. when a degraded state has been found. The aim of using PHM is to estimate how long it will take before the equipment will reach a failure threshold, in future operating conditions and future environmental conditions.

The aim of the paper is to improve the estimation of bearing RUL by dynamically updating the SKF L10 bearing life length calculation. Using a physics-based prognostic approach, the behaviour of a roller in a paper machine was simulated using the finite element method (FEM). A transfer function representing the relation between bearing acceleration and bearing forces was generated and used to convert the acceleration signal into an estimation of the dynamically changing bearing force. The estimated force is then used as input to the bearing life length calculation generating an updated L10 calculation for each time step. 

Keywords
Prognostics, Degradation, FEM, Modelling, Particle Filter, CBM, RUL, SKF L10
National Category
Engineering and Technology Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-66625 (URN)
Conference
1st International Conference on Maintenance Engineering, IncoME-I, The University of Manchester, UK, August 30-31, 2016
Projects
SKF-UTC
Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2018-05-07Bibliographically approved
Mishra, M. & Rantatalo, M. (2016). Hybrid Models for Rotating Machinery Prognosis:Estimate Remaining Useful Life. In: : . Paper presented at 3rd European Conference of the Prognostics and Health Management Society, Bilbao, Spain, 5-8 July 2016.
Open this publication in new window or tab >>Hybrid Models for Rotating Machinery Prognosis:Estimate Remaining Useful Life
2016 (English)Conference paper, Poster (with or without abstract) (Refereed)
National Category
Engineering and Technology Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-64919 (URN)
Conference
3rd European Conference of the Prognostics and Health Management Society, Bilbao, Spain, 5-8 July 2016
Projects
SKF-LTU-UTC project
Available from: 2017-07-28 Created: 2017-07-28 Last updated: 2018-05-07Bibliographically approved
Leturiondo, U., Salgado, O., Galar, D. & Mishra, M. (2016). Methodology for the Estimation of the Fatigue Life of Rolling Element Bearings in Non-stationary Conditions (ed.). In: (Ed.), Fakher Chaari; Radozlaw Zimroz; Walter Bertelmus; Mahamed Haddar (Ed.), Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the Fourth International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO'2014, Lyon, France December 15-17. Paper presented at International Conference on Condition Monitoring of Machinery in Non-Stationary Operations : 15/12/2014 - 16/12/2014 (pp. 413-423). Cham: Encyclopedia of Global Archaeology/Springer Verlag, 4
Open this publication in new window or tab >>Methodology for the Estimation of the Fatigue Life of Rolling Element Bearings in Non-stationary Conditions
2016 (English)In: Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the Fourth International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO'2014, Lyon, France December 15-17 / [ed] Fakher Chaari; Radozlaw Zimroz; Walter Bertelmus; Mahamed Haddar, Cham: Encyclopedia of Global Archaeology/Springer Verlag, 2016, Vol. 4, p. 413-423Conference paper, Published paper (Refereed)
Abstract [en]

The estimation of the life of rolling element bearings (REBs) is crucial to determine when maintenance is required. This paper presents a methodology to calculate the fatigue life of REBs considering non-stationary conditions. Instead of taking a constant value, the paper considers cyclic loading and unloading processes, as well as increasing and decreasing values of the speed of rotation. It employs a model-based approach to calculate contact loads between the different elements of the bearing, with a finite element model (FEM) used to calculate the contact stresses. Using this information, it then performs a fatigue analysis to study overloading in faulty bearings.

Place, publisher, year, edition, pages
Cham: Encyclopedia of Global Archaeology/Springer Verlag, 2016
Series
Applied Condition Monitoring, ISSN 2363-698X ; 4
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-30737 (URN)10.1007/978-3-319-20463-5_31 (DOI)000375989600031 ()4a68e13b-a120-46b6-b0a5-6ff312c76c7d (Local ID)978-3-319-20462-8 (ISBN)978-3-319-20463-5 (ISBN)4a68e13b-a120-46b6-b0a5-6ff312c76c7d (Archive number)4a68e13b-a120-46b6-b0a5-6ff312c76c7d (OAI)
Conference
International Conference on Condition Monitoring of Machinery in Non-Stationary Operations : 15/12/2014 - 16/12/2014
Note
Validerad; 2016; Nivå 1; 20150722 (urklet)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-07-10Bibliographically approved
Mishra, M. & Thaduri, A. (2016). Ontology based diagnosis for maintenance decisions of Paper Mill roller using dynamic response (ed.). In: (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 International Conference ICRESH-ARMS 2015 : 01/06/2015 - 04/06/2015 (pp. 187-196). Encyclopedia of Global Archaeology/Springer Verlag
Open this publication in new window or tab >>Ontology based diagnosis for maintenance decisions of Paper Mill roller using dynamic response
2016 (English)In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar; Alireza Ahmadi; Ajit Kumar Verma; Prabhakar Varde, Encyclopedia of Global Archaeology/Springer Verlag, 2016, p. 187-196Conference paper, Published paper (Refereed)
Abstract [en]

Context-aware systems have been applied in several fields like Information Technology, mobile, web services, travel guidance etc. These systems deliver decisions based on a ‘context’ by using contextual models. In paper industries, the failures of rollers were prominent and rolling element bearing is one of the critical components. The failure occurs due to the varying levels of the loads and external parameters that defines context. This paper demonstrates the ontology contextual modeling for the diagnosis of rollers as a context by using dynamic response. The roller is modeled using physical models and applying runs of different parameters and its levels. Then contextual models are generated for rollers to show relation among input contextual parameters with different features. This paper shows that this conceptual idea of decision based on different contexts using ontology models is for effective diagnosis facilitates maintenance strategies and further prospects in prognosis.

Place, publisher, year, edition, pages
Encyclopedia of Global Archaeology/Springer Verlag, 2016
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-30599 (URN)10.1007/978-3-319-23597-4_14 (DOI)2-s2.0-85043759032 (Scopus ID)474a7cb7-6701-4f1f-a0cc-5c5a7e6c40d0 (Local ID)978-3-319-23596-7 (ISBN)978-3-319-23597-4 (ISBN)474a7cb7-6701-4f1f-a0cc-5c5a7e6c40d0 (Archive number)474a7cb7-6701-4f1f-a0cc-5c5a7e6c40d0 (OAI)
Conference
International Conference ICRESH-ARMS 2015 : 01/06/2015 - 04/06/2015
Note

Godkänd; 2016; Bibliografisk uppgift: Containing selected papers from the ICRESH-ARMS 2015 conference in Lulea, Sweden, collected by editors with years of experiences in Reliability and maintenance modeling, risk assessment, and asset management, this work maximizes reader insights into the current trends in Reliability, Availability, Maintainability and Safety (RAMS) and Risk Management. Featuring a comprehensive analysis of the significance of the role of RAMS and Risk Management in the decision making process during the various phases of design, operation, maintenance, asset management and productivity in Industrial domains, these proceedings discuss key issues and challenges in the operation, maintenance and risk management of complex engineering systems and will serve as a valuable resource for those in the field. ; 20151222 (andbra)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-05-07Bibliographically approved
Mishra, M., Odelius, J., Rantatalo, M., Johnsson, R., Larsson, J.-O., Bellander, M. & Niemi, I. (2016). Simulations and measurements of the dynamic response of a paper machine roller (ed.). Insight (Northampton), 58(4), 210-212
Open this publication in new window or tab >>Simulations and measurements of the dynamic response of a paper machine roller
Show others...
2016 (English)In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 58, no 4, p. 210-212Article in journal (Refereed) Published
Abstract [en]

The paper industry is a highly automated industry that includes many different production steps, in which a variety of machine components are used. In a paper machine, where the pulp is being transformed into paper, rotating components such as bearing-mounted rollers play an important part in driving the wire with the pulp through the process. In this type of industry with a serial layout, the failure of a single roller or bearing could lead to the stoppage of several production steps, with costly consequences as a result. To ensure and optimise asset availability, a condition-based maintenance (CBM) strategy could be implemented. However, CBM is dependent on an appropriate condition monitoring (CM) technique to detect a physical phenomenon that defines the state of critical components or systems. For the development of CM techniques, it is therefore important to understand and model the physical behaviour of the system in question. In this paper, the behaviour of a roller in a paper machine is analysed using the finite element method (FEM). The physical model was compared with vibration measurements collected from an online monitoring system and an experimental modal analysis.

National Category
Other Civil Engineering Fluid Mechanics and Acoustics
Research subject
Operation and Maintenance; Engineering Acoustics
Identifiers
urn:nbn:se:ltu:diva-12822 (URN)10.1784/insi.2016.58.4.210 (DOI)000373565000008 ()2-s2.0-84963831882 (Scopus ID)bf9500b5-5abe-4388-b1d1-9ec8202757ed (Local ID)bf9500b5-5abe-4388-b1d1-9ec8202757ed (Archive number)bf9500b5-5abe-4388-b1d1-9ec8202757ed (OAI)
Note

Validerad; 2016; Nivå 2; 20160414 (madmis)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
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