Equipment health monitoring with non-parametric statistics for online early detection and scoring of degradation

This paper develops a health monitoring scheme to detect and trend degradation in dynamic systems that are characterised by multiple parameter time-series data. The presented scheme provides early detection of degradation and ability to score its significance in order to inform maintenance planning and consequently reduce disruption. Non-parametric statistics are proposed to provide this early detection and scoring. The non-parametric statistics approximate the data distribution for a sliding time window, with the change in distribution is indicated using the two-sample Kolmogorov-Smirnov test. Trending the changes to the signal distribution is shown to provide diagnostic capabilities, with deviations indicating the precursors to failure. The paper applies the equipment health monitoring scheme to address the growing concerns for future gas turbine fuel metering valve availability. The fuel metering unit within a gas turbine is a complex electro-mechanical system, failures of which can be a major source of airline disruption. The application is performed on data acquired from a series of industrial tests performed on large civil aero-engine fuel metering units subjected to varying levels of contaminant. The data exhibits characteristics of degradation, which are identified and trended by the equipment health monitoring scheme presented in this paper

Formal Verification of a Gain Scheduling Control Scheme

Gain scheduling is a commonly used closed-loop control approach for safety critical non-linear systems, such as commercial gas turbine engines. It is preferred over more advanced control strategies due to a known route to certification. Nonetheless, the stability of the system is hard to prove analytically, and consequently, safety and airworthiness is achieved by burdensome extensive testing. Model checking can aid in bringing down development costs of such a control system and simultaneously improve safety by providing guarantees on properties of embedded control systems. Due to model-checking exhaustive verification capabilities, it has long been recognised that coverage and error-detection rate can be increased compared to traditional testing methods. However, the statespace explosion is still a major computational limitation when applying model-checking to verify dynamic system behaviour. A practical methodology to incrementally design and formally verify control system requirements for a gain scheduling scheme is demonstrated in this paper, overcoming the computational constraints traditionally imposed by model checking. In this manner, the gain-scheduled controller can be efficiently and safely generated with the aid of the model checker

Detection and classification of turn fault and high resistance connection fault in permanent magnet machines based on zero sequence voltage

Health monitoring and fault detection are becoming more and more important in electrical machine systems due to the increasing demand for reliability. Winding turn fault is a common fault in permanent magnet machines which can cause severe damages and requires prompt detection and mitigation. High resistance connection (HRC) fault which result in phase asymmetry may also occur but does not require immediate shutdown. Thus, apart from the fault detection, the classification between the two faults is also required. In this paper, a new technique for detecting and classifying turn fault and HRC fault by utilizing both the high and low frequency components of the zero sequence voltage is proposed. The dependence on the operating conditions is minimized with the proposed fault indicators. The effectiveness of fault detection and classification has been verified by extensive experimental tests on a triple redundant fault tolerant permanent magnet assisted synchronous reluctance machine (PMA SynRM). The robustness of the turn fault detection in transient states and under no load conditions has also been demonstrated

PWM Ripple Currents Based Turn Fault Detection for Multiphase Permanent Magnet Machines

Most permanent magnet machines are driven by inverters with pulse width modulation (PWM) voltages. The currents contain high frequency (HF) components which are inversely proportional to machine inductance. The HF PWM ripple currents can be used to detect a turn fault that gives rise to changes in inductance. The features of these HF components in turn fault conditions are analyzed. A bandpass (BP) filter is designed to extract the selected sideband components, and their root-mean-square (RMS) values are measured. The RMS values in all phases are compared. It is shown that the RMS ripple current ratios between two adjacent phases provide a very good means of detecting turn fault with high signal-to-noise ratio. The detection method can identify the faulted phase, tolerate inherent imbalance of the machine, and is hardly affected by transient states. The method is assessed by simulations and experiments on a five-phase permanent magnet machine

Control of DC power distribution system of a hybrid electric aircraft with inherent overcurrent protection

In this paper, a novel nonlinear control scheme for the on-board DC micro-grid of a hybrid electric aircraft is proposed to achieve voltage regulation of the low voltage (LV) bus and power sharing among multiple sources. Considering the accurate nonlinear dynamic model of each DC/DC converter in the DC power distribution system, it is mathematically proven that accurate power sharing can be achieved with an inherent overcurrent limitation for each converter separately via the proposed control design using Lyapunov stability theory. The proposed framework is based on the idea of introducing a constant virtual resistance at the input of each converter and a virtual controllable voltage that can be either positive or negative, leading to a bidirectional power flow. Compared to existing control strategies for on-board DC micro-grid systems, the proposed controller guarantees accurate power sharing, tight voltage regulation and an upper limit of each source's current at all times, including during transient phenomena. Simulation results of the LV dynamics of an aircraft on-board DC micro-grid are presented to verify the proposed controller performance in terms of voltage regulation, power sharing and the overcurrent protection capability

Control of DC power distribution system of a hybrid electric aircraft with inherent overcurrent protection

In this paper, a novel nonlinear control scheme for the on-board DC micro-grid of a hybrid electric aircraft is proposed to achieve voltage regulation of the low voltage (LV) bus and power sharing among multiple sources. Considering the accurate nonlinear dynamic model of each DC/DC converter in the DC power distribution system, it is mathematically proven that accurate power sharing can be achieved with an inherent overcurrent limitation for each converter separately via the proposed control design using Lyapunov stability theory. The proposed framework is based on the idea of introducing a constant virtual resistance at the input of each converter and a virtual controllable voltage that can be either positive or negative, leading to a bidirectional power flow. Compared to existing control strategies for on-board DC micro-grid systems, the proposed controller guarantees accurate power sharing, tight voltage regulation and an upper limit of each source's current at all times, including during transient phenomena. Simulation results of the LV dynamics of an aircraft on-board DC micro-grid are presented to verify the proposed controller performance in terms of voltage regulation, power sharing and the overcurrent protection capability

Stable isotopes demonstrate intraspecific variation in habitat use and trophic level of non‐breeding albatrosses

The non‐breeding period is critical for restoration of body condition and self‐maintenance in albatrosses, yet detailed information on diet and distribution during this stage of the annual cycle is lacking for many species. Here, we use stable isotope values of body feathers (δ13C, δ15N) to infer habitat use and trophic level of non‐breeding adult Grey‐headed Albatrosses Thalassarche chrysostoma (n = 194) from South Georgia. Specifically, we: (1) investigate intrinsic drivers (sex, age, previous breeding outcome) of variation in habitat use and trophic level; (2) quantify variation among feathers of the same birds; and (3) examine potential carry‐over effects of habitat use and trophic level during the non‐breeding period on subsequent breeding outcome. In agreement with previous tracking studies, δ13C values of individual feathers indicate that non‐breeding Grey‐headed Albatrosses from South Georgia foraged across a range of oceanic habitats, but mostly in subantarctic waters, between the Antarctic Polar Front and Subtropical Front. Sex differences were subtle but statistically significant, and overlap in the core isotopic niche areas was high (62%); however, males exhibited slightly lower δ13C and higher δ15N values than females, indicating that males forage at higher latitudes and at a higher trophic level. Neither age nor previous breeding outcome influenced stable isotope values, and we found no evidence of carry‐over effects of non‐breeding habitat use or trophic level on subsequent breeding outcome. Repeatability among feathers of the same individual was moderate in δ13C and low in δ15N. This cross‐sectional study demonstrates high variability in the foraging and migration strategies of this albatross population

Oil system health management for aerospace gas turbine engines

The oil system of a gas turbine performs essential lubrication and thermal management functions, providing that the fluidic and tribological properties of the oil can meet functional requirements. New engine designs place increasing thermal and mechanical loads on the oil, and thus increase the risks of accelerated degradation potentially causing the oil properties to deviate from requirements. Presented with these risks, there is a potential business benefit for in-situ oil condition knowledge to support oil system health management. Starting with the business needs elicited from stakeholders, a Quality Functional Deployment process is performed to derive sensing system requirements. Sensing principles are reviewed for their capability to assess tribological failure mechanisms, and this is related back to stakeholder requirements. A set of sensors were procured and a testing programme performed that exercises the sensors against different degradations of oil and the noise factors representative of service. These sensors are evaluated for their ability to provide oil condition information. The framework presented in this paper uses system engineering principles to derive a health system design and verification process. The results from verification are reported to aid in providing overarching system availability management

Gas turbine engine condition monitoring using Gaussian mixture and hidden Markov models

This paper investigates the problem of condition monitoring of complex dynamic systems, specifically the detection, localisation and quantification of transient faults. A data driven approach is developed for fault detection where the multidimensional data sequence is viewed as a stochastic process whose behaviour can be described by a hidden Markov model with two hidden states --- i.e. `healthy / nominal' and `unhealthy / faulty'. The fault detection is performed by first clustering in a multidimensional data space to define normal operating behaviour using a Gaussian-Uniform mixture model. The health status of the system at each data point is then determined by evaluating the posterior probabilities of the hidden states of a hidden Markov model. This allows the temporal relationship between sequential data points to be incorporated into the fault detection scheme. The proposed scheme is robust to noise and requires minimal tuning. A real-world case study is performed based on the detection of transient faults in the variable stator vane actuator of a gas turbine engine to demonstrate the successful application of the scheme. The results are used to demonstrate the generation of simple and easily interpretable analytics that can be used to monitor the evolution of the fault across time

High frequency voltage injection based stator inter-turn fault detection in permanent magnet machines

An inter-turn short-circuit fault in the stator winding of an electric machine, denoted as turn fault, has been recognized as one of the most severe faults in permanent magnet machines which requires swift and reliable detection, in order to implement appropriate mitigations. The asymmetry brought by a turn fault is widely used for the fault detection. However, similar features also emerges in a less severe high resistance connection (HRC) fault, which may led to incorrect fault identification. In this paper, a more exclusive turn fault detection method with the ability to differentiate from the HRC fault is proposed. It injects high frequency square wave voltage signals and makes use of the difference in high frequency impedance under the two fault conditions. The sensitivity to HRC fault is largely reduced. The proposed turn fault indicator is independent of operating conditions and robust with respect to state transients. This method is validated in a fault tolerant permanent magnet assisted synchronous reluctance machine drive