Health management of a typical small aircraft fuel system using an adaptive technique

Faults in the aircraft fuel system will degrade its performance and may lead to the complete system failure. In commercial aircraft system, efficient diagnosis can optimize the time to return the aircraft to service, thus allowing less disruption to passenger travel. In this work, an adaptive fault diagnosis technique is developed for a typical small aircraft fuel system, which facilitates efficient learning procedure to forecast the system parameters for non-linear situations. This adaptive technique represents the integration of the Fuzzy Logic and Support Vector Machine (SVM) algorithms in the field of fault diagnosis. Using this adaptive technique health monitoring of aircraft fuel system is discussed. In an aircraft fuel tank, the fault is effectively located by assessing and contrasting the actual parameters and set point parameters related to the system for various time-frames. The fuzzy logic controller is configured with the logical rules as per the required target output. It relies on the aircraft fuel system parameters like the fuel flow rate, level of fuel in the tank, fuel temperature, and fuel pressure. From the logical rules, the control signals related to the aircraft fuel system are derived by the SVM technique. The efficiency in execution of this fault diagnosis tool-based aircraft fuel system gets authenticated in the MATLab/Simulink platform. The simulation is carried by assuming normal operating conditions of aircraft in the laboratory environment

ANFIS-based fault diagnosis tool for a typical small aircraft fuel system

In the present paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) based intelligent diagnosis tool for investigating the health of a typical small aircraft fuel system simulation was proposed. The system was designed for identifying the faults present in the aircraft fuel system and to diagnose those conditions with a proper fuel flow to the engine. The ANFIS intelligent tool works based on the logical rules of an expert system, which are developed as per the engine’s fuel consumption and the fuel flow from the tanks. The inputs to train the ANFIS are the fuel flow at the previous instant and the engine’s fuel consumption and the corresponding target is the fuel tank’s control signals. Training of ANFIS, generates the control signals as per the fuel requirement of the engine and the fuel flow to the tanks. The proposed intelligent controller model was implemented in the platform of MATLAB/Simulink and a comparison with the other techniques allowed the effectiveness of the proposed model

A Novel Data Fusion Method for Incipient Fault Detection in TRU of Aircraft Electrical System

Incipient fault detection in the Transformer Rectifier Unit (TRU) can prompt early preparedness for replacement of Line Replaceable Units (LRUs) thereby reducing the downtime of the aircraft. This paper describes a novel incipient fault detection and fault localization method of TRU in the context of an aircraft electrical system. Results from Failure Mode and Effects Analysis (FMEA) study identified diode failure as the primary reason for failure of TRU. The incipient fault detection of TRU is done by applying data fusion algorithm. The algorithm uses Hilbert transform as one of the computing tools to derive Health Index Parameter (HIP). Fast Fourier Transforms (FFT) and wavelet transforms were used for fault localization of the component within the TRU. The developed incipient fault detection method is validated on a TRU test rig. Though the test rig is configured on 50Hz supply the results can be read across on 400Hz supply. A scheme of communicating the fault status integrating through 1553B network is also addressed

Application of Kalman Filter to prognostic method for estimating the RUL of a Bridge Rectifier

Abstract—Prediction of the Remaining Useful Lifetime (RUL) of the system brings down the maintenance cost, downtime and also helps to take corrective measures. This results in avoiding catastrophic events. In this Paper, The RUL prediction is done based on KalmanFilter approach with dynamic curve fitting. The desired state (which is synthetic data) of the system is estimated based on a second order Kalman Filter, where Newtonian Kinematic model is used in tracking the significant feature state of the system. The dynamic curve fitting is done based on Least Square Error Sense method. The dynamically fitted curve is extrapolated until the failure threshold is reached and subsequently RUL is estimated. The algorithm thus developed is validated by a single phase full wave bridge rectifier analogous to aircraft Transformer Rectifier Unit (TRU), to obtain the real time significant feature data. The experimental results are compared with those of developed algorithms and results show Kalman filter based algorithm is ~95% accurate

Simulation Study of Aircraft AC Generator with Electrical Fault Insertion

The aim of this paper is to model a typical Aircraft Generator along with its control system and study the behaviour / performance during fault, particularly in the rectifier block which is used in the field circuit and fault in the winding. The study shows that there is no considerable change in the Generator supply characteristic up to a certain extent of fault, but the significant change in the field excitation was observed. The output of the developed model was successfully corroborated with the MIL-STD 704F power supply characteristics

Smart CMOS Sensor for 3D Measurement

3D measurement is concerned with extracting the three dimensional visual information of an object using a suitable image sensor, aptly called “the electronic eye” for subsequent processing. Advances in microelectronics technologies and intensive research in CMOS image sensors have made it possible to integrate processing circuits on the focal plane of the imager. In this context, a smart image sensor for 3D measurement is proposed for design and development using the industry standard 0.35micron CMOS technology. Preliminary simulation results for finding the centroid of a light spot used in 3D measurement show conformance to the design. The proposal highlights design and implementation of a simple architecture of pixel, a self timing asynchronous clocking mechanism in conjunction with a straightforward digital algorithm to achieve the objective. Early digital approach and ability to choose the number of threshold enable high flexibility of date processing of data processing with sub pixel resolution. The sensor will be the core of an advanced 3D measuring system based on active optical triangulation principl

Intelligent fault diagnosis of synchronous generators

Condition based maintenance (CBM) requires continuous monitoring of mechanical/electrical signals and various operating conditions of the machine to provide maintenance decisions. However, for expensive complex systems (e.g. aerospace), inducing faults and capturing the intelligence about the system is not possible. This necessitates to have a small working model (SWM) to learn about faults and capture the intelligence about the system, and then scale up the fault models to monitor the condition of the complex/prototype system, without ever injecting faults in the prototype system. We refer to this approach as scalable fault models. We check the effectiveness of the proposed approach using a 3 kVA synchronous generator as SWM and a 5 kVA synchronous generator as the prototype system. In this work, we identify and remove the system-dependent features using a nuisance attribute projection (NAP) algorithm to model a system-independent feature space to make the features robust across the two different capacity synchronous generators. The frequency domain statistical features are extracted from the current signals of the synchronous generators. Classification and regression tree (CART) is used as a back-end classifier. NAP improves the performance of the baseline system by 2.05%, 5.94%, and 9.55% for the R, Y, and B phase faults respectively

Review of Aircraft Engine Health Monitoring Systems

Aircraft engine is one of the most complex systems demanding efficient monitoring for safe operation and timely maintenance of the aircraft. A survey of the aircraft engine health monitoring / management (EHM) systems available in the literature is presented in this report. Attempt has been made to identify distinct systems and methodologies available at present for the aircraft engines. This will provide a good basis towards the engine health monitoring research initiated at NAL under a NPMASS sponsored project. Detailed review of gas turbine engine health management systems and related literatures has been presented. Attempt has been made to understand future requirements of the advance sensors for intelligent aero engine from EHM perspective

Design, Fabrication and Testing of Pressure Adaptors and Temperature Sensors for an Aero Engine Health Monitoring

Engine Health Monitoring (EHM) systems are becoming essential parts of modern aircraft engines to enhance safe and long operation and to reduce down time. Activities initiated to develop EHM for a typical aero engine under NPMASS sponsored project. It is planned to acquire detailed gas path data to evaluate engine performance parameters more accurately. This requires additional sensors to be incorporated in the engine gas path along with the available sensors already mounted by the manufacturer. Consequently, an assignment has been taken under EHM program to mount additional sensors in a typical aero engine with active participation from HAL / RCMA, Koraput. The design, fabrication, assembly and testing of several pressure sensing adaptors and temperature sensors have been discussed in detail in this report. The mechanical design of adaptors / sensors has been made to meet the requirements of high temperature and vibration environment during all the ratings of the engine test. The preliminary trial test for the fabricated sensor components has been carried out in the engine test bed. Satisfactory results of the components have been observed during trial tests