A Model-based Avionic Prognostic Reasoner (MAPR)

Abstract

The Model-based Avionic Prognostic Reasoner (MAPR) presented in this paper is an innovative solution for non-intrusively monitoring the state of health (SoH) and predicting the remaining useful life (RUL) of electronic and electromechanical assets by accessing and processing data obtained from a standard avionics data bus. To support Integrated Vehicle Health Monitoring (IVHM) initiatives, the solution being described here has been designed to be as non-intrusive as possible. An innovative, model-driven anomaly diagnostic and fault characterization system for electromechanical actuator (EMA) systems was developed to mitigate potentially catastrophic faults. EMA systems are used in a wide variety of aircraft applications to control critical components such as control surfaces, landing gear and thrust vector control. Failure in any one of these systems can compromise passenger safety, as well as mission success. A MIL-STD-1553 bus interface and monitor were designed to extract environmental (e.g., altitude, air speed, air density) and operational (i.e., response of system to a commanded change) data of a representative EMA system and to determine whether an anomaly is detected, and the corresponding severity. The MIL-STD-1553 bus was chosen as the test bed to develop this approach, due to its large installed base and availability of compatible development tools. Advanced and unique reasoning methodologies are applied to the extracted data sets to provide anomaly detection and fault classification on various fault modes and eventually yield SoH and RUL. In this paper we describe a data monitoring unit that will, in real time, identify, isolate, and characterize faults and establish their severity so that major performance problems can be alleviated. When built, this system will consist of a laptop with a Peripheral Component Interconnect (PCI) card slot that can accept multiple interfaces to the MAPR software package. The MAPR package will be designed to be adaptable for a large number of different platforms, for portability and for maximum input data type flexibility. This paper describes a ground-based prototype of the technology to show the efficacy of the method.

Digital Simulation and Identification of Faults with Neural Network Reasoners in Brushed Actuators Employed in an E-Brake System

“…This ability comes from the incorporation of a memory cell in its architecture.. Each cell takes in an input, the previous cell state, the weight and biases parameters determine what values are passed on to the next cell and which data are retained or ultimately forgotten [25]. Formulas governing the LSTM model used can be found from Equations ( 5)- (10) [26]:…”

“…Cell candidate, 𝑔 𝑡 = 𝜎 𝑐 (𝑊 𝑜 𝑋 𝑡 + 𝑅 𝑜 ℎ 𝑡−1 + 𝑏 𝑜 ) (10) where W, X, R, h and b denote weight, input, recurrent weights, and biases. The gate activation function is represented by 𝜎 𝑔 .…”

“…By adequately training such reasoners, data obtained from the sensor systems can be used accordingly to provide insight into the overall system performance and modify the maintenance regime. A reasoner with good accuracy helps achieve the dual objective of saving time as well as other resources, such as manpower since it could potentially eliminate manual inspections of the target system [1,9,10].…”

A Generalised Methodology for the Diagnosis of Aircraft Systems

“…Currently, IVHM's principal focus is on components and LRUs, with subsystems and systems receiving less attention. For example, the Model-based Avionics Prognostic Reasoner provided a solution for non-intrusively monitoring the health and predicting remaining useful life for Electro Mechanical Actuators by using exclusive algorithms and reasoning techniques [4]. Similarly, a high frequency vibration monitoring system was developed to detect and isolate incipient faults in critical rotary components in engines, gear trains, and generators.…”