Automated Performance Monitoring for Rotorcraft Turboshaft Engines Using a Multimodel Approach

Navy STTR 24.A - Topic N24A-T004
NAVAIR - Naval Air Systems Command
Pre-release 11/29/23   Opens to accept proposals 1/03/24   Now Closes 2/21/24 12:00pm ET    [ View Q&A ]

N24A-T004 TITLE: Automated Performance Monitoring for Rotorcraft Turboshaft Engines Using a Multimodel Approach

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy

OBJECTIVE: Develop and integrate multiple models using machine learning and artificial intelligence to continuously and accurately estimate and predict power available for rotorcraft turboshaft engines across all aircraft operating conditions.

DESCRIPTION: Accurate estimates of engine health are critical for ensuring safe operation of helicopters supporting heavy-lift operations. Various approaches exist to assess engine health and available power in a rotorcraft context, and the rapid evolution of machine learning and artificial intelligence is further expanding the realm of possible solutions. The development and maturation of algorithms that utilize existing aircraft data parameters and that have the potential for real-time, or near real-time performance, are of considerable interest. In particular, significant operational efficiencies can be obtained if engine performance deterioration can be accurately determined and predicted over a wide range of operating conditions. Maintenance can be planned in advance, with necessary personnel and resources pre-positioned to minimize mission and readiness impacts. Specific aircraft operating conditions that lend themselves to accurate estimation of power available may not occur with regularity, thereby limiting the potential effectiveness of any individual approach. Optimal predictive performance can be achieved by combining multiple models and algorithms via decision-fusion, ensemble learning, and so forth. An ideal solution would also provide the means to monitor and evolve the models over time, support the incorporation of new models, provide interpretability and explainability, and be broadly applicable to different engines.

PHASE I: Design and demonstrate multiple approaches for engine health and/or power available estimation using Navy datasets and commercially available, open-source computing languages and packages (Python, etc.). Design and demonstrate technical feasibility for combining the models using machine learning and artificial intelligence approaches to improve model performance. The raw data may need to be filtered, manipulated, or normalized to enable implementation of the models. The Phase I effort will include prototype plans to be developed under Phase II.

PHASE II: Develop and demonstrate a multimodel approach for accurately estimating and predicting engine health and/or power available over a wide range of operating conditions. Demonstrate and validate the approach within a Navy data environment in an automated context.

PHASE III DUAL USE APPLICATIONS: Demonstrate scenarios involving model re-training, updating, and incorporation of new models, within the Navy data environment. Develop tools and processes to monitor model performance and assist with long-term management.

This software capability would be broadly applicable to aerospace, turboshaft engines, and could be commercialized as an engine management tool for commercial operators.

REFERENCES:

  1. Peddareddygari, L. M. (2020, August). Time to failure prognosis of a gas turbine engine using predictive analytics [Masterís thesis, Texas A&M University]. https://oaktrust.library.tamu.edu/bitstream/handle/1969.1/192563/PEDDAREDDYGARI-THESIS-2020.pdf?sequence=1&isAllowed=y
  2. Simon, D.L., & Litt, J.S. (2008). Automated power assessment for helicopter turboshaft engines. NASA/TM-2008-215270. https://ntrs.nasa.gov/citations/20080032562
  3. Li, Z., Goebel, K., & Wu, D. (2019). Degradation modeling and remaining useful life prediction of aircraft engines using ensemble learning. Journal of Engineering for Gas Turbines and Power, 141(4). https://c3.ndc.nasa.gov/dashlink/static/media/publication/2018_DegradationModelingRULEnsemble_Wu.pdf
  4. Li, Z, Wu, D., Hu, C., & Terpenny, J. (2019). An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction. Reliability Engineering & System Safety 184, 110-122. https://www.sciencedirect.com/science/article/pii/S0951832017308104
  5. Rigamonti, M., Baraldi, P., Zio, E., Roychoudhury, I., Goebel, K., & Poll, S. (2018). Ensemble of optimized echo state networks for remaining useful life prediction. Neurocomputing, 281, 121-138. https://c3.ndc.nasa.gov/dashlink/static/media/publication/2017_12_ESN_Ensemble_NEUCOM.pdf

KEYWORDS: Ensemble Learning; Artificial Intelligence; Machine Learning; Prognostic Health Management; Engine Health Monitoring; Turboshaft Engine


** TOPIC NOTICE **

The Navy Topic above is an "unofficial" copy from the Navy Topics in the DoD 24.A STTR BAA. Please see the official DoD Topic website at www.defensesbirsttr.mil/SBIR-STTR/Opportunities/#announcements for any updates.

The DoD issued its Navy 24.A STTR Topics pre-release on November 28, 2023 which opens to receive proposals on January 3, 2024, and now closes February 21, (12:00pm ET).

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Topic Q & A

1/23/24  Q. The data available will determine the types of models that can be trained. As such, a better understanding of the data available is desirable. Please describe the dataset in greater detail. Specifically, approximately how many variables are recorded in the dataset? Approximately how many datapoints are available? Approximately how many data points correspond to something similar to a maximum power check condition (i.e., at or near maximum engine power output)?
   A. The data set will contain the following parameters recorded continuously at a 1Hz data rate (or higher), from power-up to power-down:
Aircraft state parameters: airspeed (indicated and computed), pressure altitude, attitude (roll/pitch/yaw), rotor speed, EAPPS position
Engine parameters: gas generator speed, power turbine speed, torque, T2, T5, Ps3, fuel flow rate, bleed status
1/9/24  Q. Will the Power Available determination need to be up to a set altitude across all airframes or up to each airframe type's service ceiling?
   A. H-53 heavy lift helicopters are the primary application for this effort. It is desirable for any proposed approaches to also be generally applicable to other rotorcraft platforms.
1/9/24  Q. Will the aircraft performance data for each airframe type be made available in Phase I?
   A. The proposed approaches should not rely on the provision of engine performance charts, or related models, as those are not expected to be made available under this effort. A data set containing aircraft parameter recordings will be made available through a NAVAIR data environment to support demonstration.
1/9/24  Q. Will the same data need to be determined for the CV-22?
   A. The V-22 is not the intended application for this effort
1/9/24  Q. Is this SBIR strictly for the US Navy or will it also apply to the US Marine aircraft?
   A. H-53 heavy lift helicopters are the primary application for this effort.
1/9/24  Q. What is the current standard method to determine Power Available/ TOLD?
   A. Any relevant metrics that provide an ability to observe changes in engine performance could be used in this effort. The goal is not to dictate the estimation or prediction of a certain parameter or quantity, but to provide enhanced awareness as to the performance capability of the engine.
1/9/24  Q. Is Power Available the only item that needs determined or do all Take Off and Landing Data (TOLD) items, Power Required to Hover (IGE/OGE), Single Engine Hover, Etc need determined with this system?
   A. Any relevant metrics that provide an ability to observe changes in engine performance could be used in this effort. The goal is not to dictate the estimation or prediction of a certain parameter or quantity, but to provide enhanced awareness as to the performance capability of the engine.
12/29/23  Q. Will all turboshaft engine raw data get provided by the Navy?
   A. Awardees will be given access to our standard data repository (SDR) where they will be able to access the data and analyze it through Cloudera Data Science Workbench (CDSW). This will be view only access, and awardees will not be permitted to remove any data from the system.

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