Applying Deep Learning to Aerospace and Building System Applications at UTCVivek Venugopalan, Kishore Reddy and Michael Giering

• Deep Learning is an evolving area of research in neural networks and it has been adopted by UTC for tackling various problems in aerospace and building systems.• Three different use cases discussed here: (1) Aircraft sensor diagnostics for UTAS, Pratt & Whitney, (2) Prognostic Health Monitoring for Otis Elevators, (3) Chiller power estimation for Carrier Climate Control systems• Aircraft sensors provide huge amount of data that needs to be tracked such as air data systems, fuel measurement and management systems, health and usage systems and mission data recorders.

[1] Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, et al., “Greedy layer-wise training of deep networks,” Advances in neural information processing systems, vol. 19, p. 153, 2007. [2] P. Vincent, H. Larochelle, Y. Bengio, and P. A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in ICML, 2008[3] F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, N. Bouchard, D. Warde-Farley, and Y. Bengio, “Theano: new features and speed improvements,” arXiv preprint arXiv:1211.5590, 2012 [4] M. Giering, V. Venugopalan, and K. Reddy. “Multi-modal sensor registration for vehicle perception via deep neural networks”. In IEEE High Performance Extreme Computing Conference (HPEC), 2015.

Implementation and Results

Introduction

Conclusion

References

Deep Auto-Encoders

• 4xNvidia K40 GPUs with with 2880 cores and 12 GB device RAM each in Ubuntu OS workstation• Theano based toolchain for Deep Learning• Nvidia K40 with 12 GB device RAM - driving factor for large dataset inhalation, caching and computation - especially the pre-training stage for DBNs

Email:{venugov, gierinmj, reddykk}@utrc.utc.com

Deep Belief Nets

Layer 1

Layer 2

Bottleneck layer

Input layer

W2T

Layer 1

Layer 2

RBM

RBM

RBM

Recursive pre-training

W1T

W3T

• Successful adoption of Deep Learning methodologies to UTC applications in aerospace and building systems as shown in the timeline.

• Deep Belief Nets (DBN) consist of using a probabilistic Restricted Boltzmann Machine (RBM) approach, trying to reconstruct noisy inputs. • Training involves the reconstruction of a clean sensor input from a partially destroyed/missing sensor.•Depending on the application, a final layer can be added after the bottleneck layer.

• Deep Auto-Encoders (DAE) performs the fine-tuning by generating the layers mirroring the initial network upto the bottleneck layer after the pre-training using the DBNs.• The weights and the bias of the upper and lower hidden layers for the DAE are updated in the fine-tuning stage.• The main objective of the DAE is to minimize the reconstruction error.

utcaerospacesystems.com

MRO & Support Services

Features more than 6,000 customer service employees across 16 countries dedicated to the operation of nearly 60 MRO service and support facilities. Customer Response Center available for a range of needs – from AOG to spare parts and technical support. Offers customized support agreements to help operators achieve optimal aircraft utilization. +1 877 808 7575 crc@utas.utc.com utascrc.com

150004001.indd 05/27/2015

Actuation & Propeller Systems Designs and manufactures actuation and propeller systems for commercial and military aircraft. Products range from single actuators to complete flight control systems for the fixed wing, rotorcraft and missile segments as well as fly-by-wire cockpit controls, cabin equipment, trimmable horizontal stabilizer actuators and flight safety parts for helicopters.

Engine & Environmental Control Systems Provides engine controls, accessories and solutions for turbofan, turboprop and turboshaft engines and environmental control systems for aerospace and defense applications. Engine products include electronic engine controllers, fuel systems, engine actuation, thermal management systems, accessory drive gearboxes and transmissions, drive shafts and flexible couplings, engine start systems, turbine blades and vanes. Environmental control systems include air conditioning, liquid cooling, engine bleed air, pressurization control, ventilation control, humidification and fuel tank inerting.

Landing Systems Designs, manufactures and services fully integrated landing systems such as main and nose gear structures, electric and hydraulically actuated brakes with steel or carbon friction material, and brake control systems. Innovative solutions include more electric technologies, DURACARB® carbon friction material, EDL® extended life configurations, and lighter-weight, high-strength materials.

Sensors & Integrated Systems Provides cutting-edge sensors and sensor-based systems for the commercial aerospace, ground vehicle and defense industries including electronic flight bags, air data systems, ice detection and protection systems, fire protection systems, fuel measurement and management systems, guidance navigation and control systems, health and usage management systems, rescue hoists, mission data recorders, and sensing suites for aircraft engines.

Interiors Designs, manufactures and supports advanced systems that enhance safety, performance and aesthetics across a wide range of commercial, business jet and military aircraft. Provides WINSLOW life rafts, interior and exterior lighting systems, aircraft evacuation systems, cargo systems, pyrotechnic egress systems, VIP and specialty seating systems including Advanced Concept Ejection Seats (ACES II and ACES 5), and cabin systems featuring custom-crafted artisan Booth Veneers, cabin management systems and in-flight entertainment products.

ISR & Space Systems Provides products and services to global government and commercial markets that enable mission success in space, in the air, at sea and on the ground. Manufactures products providing actionable intelligence through surveillance and reconnaissance solutions; products for small unmanned airborne systems; state-of-the-art Shortwave Infrared (SWIR) products to support warfighters; and environmental control and life support systems that enable humans to safely operate in space and under the sea.

Electric Systems

Provides electric power systems for commercial, regional, business, and military aircraft. Products include main and emergency power generation, power conversion and motor control, power distribution, and aircraft utilities management. A complete range of electric power generation options is provided, including constant and variable frequency AC and high-voltage DC.

Aerostructures Designs, manufactures, and integrates nacelles, thrust reversers, pylons and flight control surfaces for commercial and military aircraft. Aerostructures includes the Engineered Polymer Products business, which designs, tests and manufactures composite components for ships, submarines and commercial airplanes.

UTC Aerospace Systems A range of capabilities

On-board sensor diagnostics and data collection (FAST box)

e.g. fuel measurement and management systems, mission data

recorders, etc.

Integrated sensor management and real-time analysis for variety of sensing

suites for aircraft engines

Aircraft sensors

Layer 1

Layer N

Bottleneck layer

Input layer

Layer 1

Layer N

Output layer

DBN pre-training

Bank of elevators

Sensors embedded for prognostic

health monitoring

Diagnostic and decision

• Sensors embedded in Otis Elevator systems mainly used for collecting data about the health of the system • Chillers used with Carrier HVAC units - understanding energy requirements

Carrier Chillers in HVAC units- understanding more about optimizing the energy utilization

Chiller power output prediction based on the inputs to DBN

GIVEN --->

PREDICT --->

Wat

ts

Chiller power output reconstruction

Blue – Original Red – Predicted

Wat

ts

Sensor estimation from the FAST box

Algorithm Reconstruction error

Discrete Bayesian Network 17192.63

Continuous Bayesian Network 17966.18

Structured Learning 14921.63

Koopman 16823

Deep Learning 10819.55

• Benchmarked Carrier Chiller energy utilization using variety of Machine Learning algorithms.• Deep Learning approach provided the lowest reconstruction error enhancing the energy prediction capability.

Deep Auto-Encoders

Elevator data streamed using smartphone app Damage information:

Good cab doorModerate or severe damage to cab door

• Sensors embedded in the elevators streamed using smartphone app and then fed to the Deep Auto-Encoder. • Performance metric measured in terms of the health of the elevator cab door

Timeline of Deep Learning adoption and application to UTC

• Variety of use cases - sensor estimation from onboard sensing suites on aircraft engines using DBN, chiller power prediction for building systems using DBN, PHM in elevator systems using DAE.• Huge amount of data generated - offline training using Nvidia GPUs.• Online diagnostics and decision using Nvidia’s Jetson GPUs - future.

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File: PWC_400057_0787331054_1PSNR: 27.69 dBNRMS: 4.46 %

File: PWC_400057_0788706605_1PSNR: 34.22 dBNRMS: 2.11 %

File: PWC_400057_0839520402_1PSNR: 25.47 dBNRMS: 5.72 %

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Problem formulation and capability development

Algorithm fine-tuning and technology demonstration

Data collection on-field, experimental setup

Infrastructure development, toolchain selection