Deep Learning for industrial Prognostics & Health Management (PHM)

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 W2 T Layer 1 Layer 2 RBM RBM RBM Recursive pre-training W1 T W3 T • Successful adoption of Deep Learning methodologies to UTC applications in aerospace and building systems as shown in the timeline. Offers customized support agreements to help operators achieve optimal aircraft utilization. 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 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.