Fault diagnosis in aircraft fuel system components with machine learning algorithms

There is a high demand and interest in considering the social and environmental effects of the component’s lifespan. Aircraft are one of the most high-priced businesses that require the highest reliability and safety constraints. The complexity of aircraft systems designs also has advanced rapidly in the last decade. Consequently, fault detection, diagnosis and modification/ repair procedures are becoming more challenging. The presence of a fault within an aircraft system can result in changes to system performances and cause operational downtime or accidents in a worst-case scenario. The CBM method that predicts the state of the equipment based on data collected is widely used in aircraft MROs. CBM uses diagnostics and prognostics models to make decisions on appropriate maintenance actions based on the Remaining Useful Life (RUL) of the components. The aircraft fuel system is a crucial system of aircraft, even a minor failure in the fuel system can affect the aircraft's safety greatly. A failure in the fuel system that impacts the ability to deliver fuel to the engine will have an immediate effect on system performance and safety. There are very few diagnostic systems that monitor the health of the fuel system and even fewer that can contain detected faults. The fuel system is crucial for the operation of the aircraft, in case of failure, the fuel in the aircraft will become unusable/unavailable to reach the destination. It is necessary to develop fault detection of the aircraft fuel system. The future aircraft fuel system must have the function of fault detection. Through the information of sensors and Machine Learning Techniques, the aircraft fuel system’s fault type can be detected in a timely manner. This thesis discusses the application of a Data-driven technique to analyse the healthy and faulty data collected using the aircraft fuel system model, which is similar to Boeing-777. The data is collected is processed through Machine learning Techniques and the results are comparedPhD in Manufacturin

Comprehensive Joint Time-Frequency Analysis Toward Condition Based Maintenance Regimes for Electrical and Mechanical Components

Based upon a framework of time-frequency analysis, we outline condition based maintenance (CBM), or maintenance only upon evidence of need, for both electrical and mechanical systems. We apply novel time-frequency cross-correlation metrics to helicopter drivetrain systems and electrical cables as remaining useful life assessments by non-destructive and non-invasive tests. In both cases, these metrics for health assessment provide a basis for diagnostic and prognostic analysis of underlying systems and components by performing accelerated condition tests on actual mechanical and electrical systems. We present novel time-frequency domain vibration analysis of a gearbox failure in an AH-64 Apache drivetrain testbed and quantify transient precursors of failure where previous diagnostic methods rely on stationary power spectrum analysis to analyze nonstationary signals. Using time-frequency representations of the vibration signals, a shift in energy is seen from the first harmonic of the gearmesh frequency to the third and fourth harmonics in intermittent patterns indiscernible by the standard power spectrum over the course of 4 days leading to gearbox failure due to grease lubrication drought. We demonstrate a new form of Rényi entropy-based mutual information measure based upon Shannon and Hartley entropy and derived from a cross-time-frequency distribution of separate accelerometer vibration signals for comparing rotational harmonics from multiple bearings to create new condition indicators of damage in rotorcraft drivetrains. Baseline, unbalanced, and misaligned experimental settings of helicopter drivetrain bearings and shafts are quantitatively distinguished by the proposed techniques. With unbalance quantifiable by variance in the in-phase mutual information and misalignment quantifiable by variance in the quadrature mutual information, machine health classification is accomplished by use of statistical bounding regions. Utilizing similar methods to form a time-frequency cross-correlation derived metric, a process for non-invasively assessing the health of low voltage instrumentation cables and medium to high voltage feeder and underground transmission cables is proposed by way of Joint Time-frequency Domain Reflectometry (JTFDR). We introduce a new standardized method for determination of the optimal reference signal for reflectometry to allow implementation of a stand-alone reflectometer device for cable testing and provide theoretical background for a more generalized time-frequency enveloping function of this reference. Fault location and life estimation methods are verified in networks of instrumentation cable, cable tray and conduit systems, multiple localized fault scenarios, simulations of faults on endless lines, and three separate thermal accelerated aging tests of low to high voltage cables. A 24 hour thermal aging test of underground 15kV, tree-resistant cross-linked polyethylene (TR-XLPE) cable simulates 90 years of service life and shows a monotonic increase in the measured JTFDR metric. This is compared to aging of similar duration for other cables utilizing silicon rubber (SIR), cross-linked polyethylene (XLPE), and ethylene propylene rubber (EPR) insulation types. Expanding on this preliminary aging, we present a 916 hour extended accelerated aging test of XLPE insulated RG-58 instrumentation cable at reduced and more realistic temperatures to simulate 30 years of service. The time-frequency optimal reference signal is updated with a separated spectrum as enveloped by a Gaussian derivative function. Lastly, we utilize a single broadband monopole surface wave launcher and receiver in combination with the JTFDR algorithm to obtain fault location and health assessment metrics non-invasively and provide fault assessment in unshielded concentric neutral cables