Predictive Fault Diagnosis System for Intelligent and Robust Health Monitoring

Abstract

This paper describes work related to the Predictive Fault Diagnosis System for Intelligent and Robust Health Monitoring, which exists as a solution to complete failure detection, identification, and prognostics (FDI&P) in health monitoring applications. Several advanced FDI analytical redundancy techniques have been applied for such a purpose, with the most notable being a compound method comprised of optimal filtering, statistical analysis, and neuro-fuzzy algorithms that is able to detect and diagnose both known and unknown failures. Although this scheme has been proven to be quite successful for systems that can be well described in a state space representation, current research has shown the viability in extending the process to highly complex systems with considerable nonlinearities while still maintaining the FDI capabilities. This paper highlights the utility of these algorithms for determining failures in (1) a known reusable liquid rocket engine model and (2) an unknown input-output relation in a fluid flow testbed. Other research has focused on prognostic capabilities provided by a neural architecture enhanced with fuzzy logic using rule-based knowledge. An example of using data to construct fuzzy rules for determining the remaining useful life of components is provided to give insight into the process.

Comparison of immunity-based schemes for aircraft failure detection and identification

“…The existence of a Failure Detection and Identification (FDI) scheme can support automatic accommodation as part of a fault-tolerant control system and it can also improve human accommodation through increased pilot situational awareness. Most of the research efforts in this area have considered only individual failures within limited regions of the flight envelope [4][5][6]. State estimation or observer-based schemes have been widely proposed [7][8][9][10] for actuator FDI relying on Kalman or other classes of filters.…”

Integrated Immunity-Based Framework for Aircraft Abnormal Conditions Management

“…Most of the research efforts in the area of abnormal condition detection and identification have focused on individual/isolated classes of failures occurring within limited regions of the flight envelope [5][6][7][8]. Several research efforts have been conducted in the past few years on the design of fault-tolerant adaptive control laws [5,9,10] and the development of envelope estimation and protection methodologies for aircraft under damage/failure conditions [11][12][13][14][15][16][17].…”

Aircraft Abnormal Conditions Detection, Identification, and Evaluation Using Innate and Adaptive Immune Systems Interaction

“…Any DC which migrates to the lymph node (i.e., the adaptive immune system) with IL12 IL10 is called stimulatory DC since it activates the production of cytotoxic T-cells in the adaptive immune system. The set of activated cytotoxic T-cells can be expressed as 11) where N SDC is the number of stimulatory DCs and K i is the number of cytotoxic T-cells corresponding to feature ' i . On the other hand, any migrated DC with IL12 < IL10 is called regulatory DC since it activates the production of suppressor (regulatory) T-cells in the adaptive immune system.…”

“…Most of the research efforts in the area of aircraft AC detection and identification (ACDI) have considered only individual failures within limited regions of the flight envelope [10][11][12]. Evaluation of aircraft failures (in particular, flight envelope estimation and protection) have also been conducted in the past several years [13][14][15][16][17][18][19].…”