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The Environmental Control System (ECS) of an aircraft provides thermal and pressure control of the engine bleed air for comfort of the crew members and passengers onboard. For safe and reliable operation of the ECS under complex operating environments, it is critical to detect and diagnose performance degradations in the system during early phases of fault evolution. One of the critical components of the ECS is the heat exchanger, which ensures proper cooling of the engine bleed air. This paper presents a wavelet-based fouling diagnosis approach for the heat exchanger.

This paper addresses the issues of Fault Detection and Isolation (FDI) in complex networked systems such as the Environmental Control System (ECS) of an aircraft. The ECS controls and supplies pressurized air to the aircraft and consists of multiple subsystems that in turn consist of interconnected components, heterogeneous sensing devices, and feedback controllers. These complex interconnections and feedback control loops make fault detection and isolation a very challenging task in the ECS. For example, a faulty component yields off-nominal outputs which are inputs to the other coupled components. This coupling leads to off-nominal outputs from otherwise healthy components, thus causing unwanted false-alarms. Secondly, due to off-nominal inputs, the healthy components are driven beyond their normal operating conditions, leading to cascading failures.

This paper addresses the issue of fault diagnosis in the heat exchanger of an aircraft Air Conditioning System (ACS). The heat exchanger cools the air by transferring the heat to the ram-air. Due to a variety of biological, mechanical and chemical reasons, the heat exchanger may experience fouling conditions that reduces the efficiency and could considerably affect the functionality of the ACS. Since, the access to the heat exchanger is limited and time consuming, it is preferable to implement an early fault diagnosis technique that would facilitate Condition Based Maintenance (CBM). The main contribution of the paper is pre-flight fault assessment of the heat exchanger using a combined model-based and data-driven approach of fault diagnosis. A Simulink model of the ACS, that has been designed and validated by an industry partner, has been used for generation of sensor data for various fouling conditions.

The Aircraft System Health Management (ASHM) tool is a UTC developed web application that provides access to Aircraft Condition Monitoring Function (ACMF) reports and Flight Deck Effects (FDE) records for Boeing 787®, A320®, and A380® aircraft. The tool was built with a flexible architecture to field a range of off-board diagnostics and prognostics modules designed to transform an abundance of data into actionable and timely knowledge about fleet health. This paper describes the system architecture and implementation with a focus on “lessons learned” in applying diagnostic and prognostics algorithms to available fleet data. Key topics include ensuring analytic robustness, design for cross-enterprise collaboration and defining a workable approach to testing, validating and deploying prognostics and diagnostics models with various degrees of complexity. A case study is provided related to fluid leak detection within an environmental control subsystem.

Airport environments consist of several moving objects both in the air and on the ground. In air moving objects include aircraft, UAVs and birds etc. On ground moving objects include aircraft, ground vehicles and ground personnel etc. Detecting, classifying, identifying and tracking these objects are necessary for avoiding collisions in all environmental situations. Multiple sensors need to be employed for capturing the object shape and position from multiple directions. Data from these sensors are combined and processed for object identification. In current scenario, there is no comprehensive traffic monitoring system that uses multisensor data for monitoring in all the airport areas. In this paper, for explanation purposes, a hypothetical airport traffic monitoring system is presumed that uses multiple sensors for avoiding collisions.