Assesment on on-wing engine health based on non linear dynamic engine behaviour
Lead Research Organisation:
CRANFIELD UNIVERSITY
Department Name: Sch of Aerospace, Transport & Manufact
Abstract
This work will focus on the development and validation of the novel:
-nonlinear higher order spectra and novel nonlinear detection analytical methods to determine the relationship between the developed higher order spectra and structural damage/deterioration, including for blade crack detection
-vibration diagnosis/predictive technology allowed damage diagnosis, esttimation of the remaining useful life and damage severity quantification based on the determined relationship and trending of the higher order spectra.
-nonlinear higher order spectra and novel nonlinear detection analytical methods to determine the relationship between the developed higher order spectra and structural damage/deterioration, including for blade crack detection
-vibration diagnosis/predictive technology allowed damage diagnosis, esttimation of the remaining useful life and damage severity quantification based on the determined relationship and trending of the higher order spectra.
Planned Impact
The main beneficiary is Rolls-Royce; other beneficiaries are manufacturers, repairers, maintenance teams and end-users of rotating machinery in the UK and worldwide, including Cranfield's existing and potential industrial collaborators: Dresser Rand, EON (UK), Scottish Energy, SKF, London Underground, Siemens, Shell, Severn Trent Water, Vertical Wind (Sweden), Vestas (UK), Doosan Babcock, ERIKS (UK), Boeing (USA), BAE Systems, Meggitt and United Utilities. The general UK, EU and worldwide public will also benefit from the project due to environmental and social benefits and quality of life improvements. The proposed research will benefit academics via significant advances in understanding of predictive methods for machinery.
The project results will be incorporated into the Rolls-Royce (RR) Engine Monitoring units. The value will be realised through the maintenance service for engines.
The predictive ability will deliver efficiencies to engine overhauls. This would reduce the environmental impact of the overhaul facilities and the number of additional engines required to support the airlines. The major impact for RR and other turbine manufacturers is the new capability of early diagnosis/prediction. The economic
impacts include improvement in: (i) use of engine components/systems (ii) maintenance costs (iii) engine performance, durability, reliability and downtime prevention (iv) productivity (v) manufacturing and operating costs (vi) engine sales. The technology is almost universally applicable to most of rotating machines and could be applied for structural monitoring (e.g. buildings, bridges, etc.), process and manufacturing (food or materials, etc.), power generation, oil and gas and transportation industries.
Environmental and social benefits and quality of life improvements include (i) improving health/safety of machinery inspections (ii) increasing safety of ground/water transportation (iii) preventing hazards and the release of toxic residues at nuclear power stations (iv) ensuring continuous energy production from landfill gas and renewable sources of energy (v) reducing automotive vehicle emissions (CO2, etc.); (vi) preventing annually about 500 pollution incidents at wastewater plants (vii) reducing the scrap rate (viii) ensuring continuous waste reduction by production of water from sewage and energy from waste, providing also energy recovery (ix) improving sustainability of UK rivers and coastline and, therefore, impacting social inclusion (x) preventing hazardous oil content in water, ensuring the delivery of safe water to the UK (xi) preventing environmental problems in offshore pipelines and oil rigs (xii) providing reliable cogeneration with 25% reduction of primary energy use, CO2 and SOx emissions and carbon.
The medical industry will benefit in patient care as a result of improved monitoring and equipment availability. The exploitation of the sensing system will occur in several phases, covering initial exploitation, UK and European expansion and global expansion.
Informal discussions have already taken place with a number of airworthiness and regulatory authorities (EASA, FAA, etc.) to make them aware of this proposed project. Continuing these discussions is essential to enable them to evolve their understanding and processes in line with the technology being developed. Airworthiness and regulatory authorities will be kept updated at regular 6 month intervals of the status of the project by members of the consortium. The EHM and VHM community will be kept informed through the Knowledge Transfer Networks (KTNs) that members of the consortium are
members of and SIMONET network.
Dissemination to the public and other stakeholders will be maximised by developing
-articles in academic journals and relevant commercial publications
-presentations at EC and worldwide conferences, exhibitions and trade shows
-press releases
-case studies
-workshops and seminars
The project results will be incorporated into the Rolls-Royce (RR) Engine Monitoring units. The value will be realised through the maintenance service for engines.
The predictive ability will deliver efficiencies to engine overhauls. This would reduce the environmental impact of the overhaul facilities and the number of additional engines required to support the airlines. The major impact for RR and other turbine manufacturers is the new capability of early diagnosis/prediction. The economic
impacts include improvement in: (i) use of engine components/systems (ii) maintenance costs (iii) engine performance, durability, reliability and downtime prevention (iv) productivity (v) manufacturing and operating costs (vi) engine sales. The technology is almost universally applicable to most of rotating machines and could be applied for structural monitoring (e.g. buildings, bridges, etc.), process and manufacturing (food or materials, etc.), power generation, oil and gas and transportation industries.
Environmental and social benefits and quality of life improvements include (i) improving health/safety of machinery inspections (ii) increasing safety of ground/water transportation (iii) preventing hazards and the release of toxic residues at nuclear power stations (iv) ensuring continuous energy production from landfill gas and renewable sources of energy (v) reducing automotive vehicle emissions (CO2, etc.); (vi) preventing annually about 500 pollution incidents at wastewater plants (vii) reducing the scrap rate (viii) ensuring continuous waste reduction by production of water from sewage and energy from waste, providing also energy recovery (ix) improving sustainability of UK rivers and coastline and, therefore, impacting social inclusion (x) preventing hazardous oil content in water, ensuring the delivery of safe water to the UK (xi) preventing environmental problems in offshore pipelines and oil rigs (xii) providing reliable cogeneration with 25% reduction of primary energy use, CO2 and SOx emissions and carbon.
The medical industry will benefit in patient care as a result of improved monitoring and equipment availability. The exploitation of the sensing system will occur in several phases, covering initial exploitation, UK and European expansion and global expansion.
Informal discussions have already taken place with a number of airworthiness and regulatory authorities (EASA, FAA, etc.) to make them aware of this proposed project. Continuing these discussions is essential to enable them to evolve their understanding and processes in line with the technology being developed. Airworthiness and regulatory authorities will be kept updated at regular 6 month intervals of the status of the project by members of the consortium. The EHM and VHM community will be kept informed through the Knowledge Transfer Networks (KTNs) that members of the consortium are
members of and SIMONET network.
Dissemination to the public and other stakeholders will be maximised by developing
-articles in academic journals and relevant commercial publications
-presentations at EC and worldwide conferences, exhibitions and trade shows
-press releases
-case studies
-workshops and seminars
People |
ORCID iD |
Len Gelman (Principal Investigator) |
Publications
Klepka A
(2015)
Triple correlation for detection of damage-related nonlinearities in composite structures
in Nonlinear Dynamics
Gelman L
(2016)
Novel spectral kurtosis technology for adaptive vibration condition monitoring of multi-stage gearboxes
in Insight - Non-Destructive Testing and Condition Monitoring
Gelman L
(2017)
Novel In-Service Combustion Instability Detection Using the Chirp Fourier Higher Order Spectra
in International Journal of Prognostics and Health Management
Ciszewski T
(2016)
Current-based higher-order spectral covariance as a bearing diagnostic feature for induction motors
in Insight - Non-Destructive Testing and Condition Monitoring
Description | We developed novel vibration technologies for turbo machinery |
Exploitation Route | The users can use these technologies for turbo machinery and for bone diagnosis in healthcare |
Sectors | Aerospace Defence and Marine Energy Healthcare Transport |
Description | The findings contributed to healthcare for radiation free bone diagnosis |
First Year Of Impact | 2016 |
Sector | Healthcare |
Impact Types | Societal |
Description | Influence on on practice of diagnosis of osteoporosis anbd bone fracture in healthcare |
Geographic Reach | Local/Municipal/Regional |
Policy Influence Type | Influenced training of practitioners or researchers |
Impact | It was proposed new low cost radiation free technology for vibration diagnosis of bone fractures and osteoporosis; it is a significant step change in clinical service |
Description | Horizon 2020 |
Amount | £3,000,000 (GBP) |
Funding ID | 664892 |
Organisation | European Commission |
Department | Horizon 2020 |
Sector | Public |
Country | European Union (EU) |
Start | 06/2015 |
End | 06/2018 |
Title | New vibration diagnosis technologies |
Description | New vibration diagnosis technologies for fatigue damage detection in turbo machinery and human bones |
Type Of Material | Model of mechanisms or symptoms - human |
Year Produced | 2016 |
Provided To Others? | Yes |
Impact | The new tool provide low cost radiation free effective diagnosis of human bones and in-service effective diagnosis of turbomachinery |
Title | Data base of processed engine data |
Description | Aircraft engine vibration data were processed by the novel technologies |
Type Of Material | Database/Collection of data |
Provided To Others? | No |
Impact | The significant impact is that new database shows a possibility to make an integrated diagnosis of the whole engine |
Description | Development of diagnostic tool for turbo machinery |
Organisation | Ukrainian National Academy of Sciences |
Country | Ukraine |
Sector | Public |
PI Contribution | Extension of the validated technologies for general turbo machinery |
Collaborator Contribution | Comprehensive analytical modelling of fatigue damage in turbo machinery blades and structures |
Impact | The collaboration is multi-disciplinary, we are preparing application for Royal Society bilateral grant |
Start Year | 2015 |
Description | Research and development of a clinical tool for vibration diagnosis of bone fractures and osteoporosis |
Organisation | Bedford Hospital NHS Trust |
Country | United Kingdom |
Sector | Public |
PI Contribution | Cranfield extended the technology that we have validated in frames of award for diagnosis of bone fractures and osteoporosis |
Collaborator Contribution | Bedford hospital provided an important medical input/advices related to creation of vibration clinical tool |
Impact | This collaboration is multi-disciplinary; radically novel research and development of radiation free vibration bone diagnosis technologies that I performed for UK NHS and healthcare worldwide, was awarded National Facility Award: Oxford Academic Health Science Network Award, 2014 Award event described in: · newspaper "Bedfordshire On Sunday" (2014), · Bedford hospital WEB, https://www.bedfordhospital.nhs.uk/2014/10/09/bedford-hospital-and-cranfield-university-win-a-prestigious-collaboration-award/(external) · Oxford Academic Health Science WEB, http://www.oxfordahsn.org/news-and-events/news/ diagnostic-tool-developed-by-bedford-hospital-and-cranfield-university-wins-our-first-collaboration-prize/ |
Start Year | 2014 |
Title | New vibration technology for low cost radiation free diagnosis of bone fractures and osteoporosis |
Description | New vibration technology for low cost radiation free diagnosis of bone fractures and osteoporosis can dramatically improved effectiveness of bone diagnosis |
Type | Diagnostic Tool - Non-Imaging |
Current Stage Of Development | Initial development |
Year Development Stage Completed | 2016 |
Development Status | Actively seeking support |
Impact | The technology can be effectively used at home, in field at accidents, during war, battles |
Title | Novel vibration technologies for turbomachinery and health care |
Description | Novel vibration technologies for turbomachinery and health care for fatigue damage detection in turbo machinery and for bone diagnosis |
Type Of Technology | New/Improved Technique/Technology |
Year Produced | 2016 |
Impact | Improved effectiveness of early damage diagnosis, it is radiation free and low cost for health care |
Description | International Conference on Condition Monitoring |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | More than 120 attendees attended these two International Conferences, the presentations sparked questions and discussion, the audience was highly interested in the outcomes of the research |
Year(s) Of Engagement Activity | 2015,2016 |