Accommodating repair actions into gas turbine prognostics

Elements of gas turbine degradation, such as compressor fouling, are recoverable through maintenance actions like compressor washing. These actions increase the usable engine life and optimise the performance of the gas turbine. However, these maintenance actions are performed by a separate organization to those undertaking fleet management operations, leading to significant uncertainty in the maintenance state of the asset. The uncertainty surrounding maintenance actions impacts prognostic efficacy. In this paper, we adopt Bayesian on-line change point detection to detect the compressor washing events. Then, the event detection information is used as an input to a prognostic algorithm, advising an update to the estimation of remaining useful life. To illustrate the capability of the approach, we demonstrated our on-line Bayesian change detection algorithms on synthetic and real aircraft engine service data, in order to identify the compressor washing events for a gas turbine and thus provide demonstrably improved prognosis

Allometry of Kinematics and Energetics in Carpenter Bees (Xylocopa Varipuncta) Hovering in Variable-Density Gases

We Assessed the Energetic and Aerodynamic Limits of Hovering Flight in the Carpenter Bee Xylocopa Varipuncta. using Normoxic, Variable-Density Mixtures of O2, N2 and He, We Were Able to Elicit Maximal Hovering Performance and Aerodynamic Failure in the Majority of Bees Sampled. Bees Were Not Isometric Regarding Thorax Mass and Wing Area, Both of Which Were Disproportionately Lower in Heavier Individuals. the Minimal Gas Density Necessary for Hovering (MGD) Increased with Body Mass and Decreased with Relative Thoracic Muscle Mass. Only the Four Bees in Our Sample with the Highest Body Mass-Specific Thorax Masses Were Able to Hover in Pure Heliox. Wingbeat Frequency and Stroke Amplitude during Maximal Hovering Were Significantly Greater Than in Normodense Hovering, Increased Significantly with Body Mass during Normodense Hovering But Were Mass Independent during Maximal Hovering. Reserve Capacity for Wingbeat Frequency and Stroke Amplitude Decreased Significantly with Increasing Body Mass, Although Reserve Capacity in Stroke Amplitude (10-30%) Exceeded that of Wingbeat Frequency (0-8%). Stroke Plane Angle during Normodense Hovering Was Significantly Greater Than during Maximal Hovering, Whereas Body Angle Was Significantly Greater during Maximal Hovering Than during Normodense Hovering. Power Production during Normodense Hovering Was Significantly Less Than during Maximal Hovering. Metabolic Rates Were Significantly Greater during Maximal Hovering Than during Normodense Hovering and Were Inversely Related to Body Mass during Maximal and Normodense Hovering. Metabolic Reserve Capacity Averaged 34% and Was Independent of Body Mass. Muscle Efficiencies Were Slightly Higher during Normodense Hovering. the Allometry of Power Production, Power Reserve Capacity and Muscle Efficiency Were Dependent on the Assumed Coefficient of Drag (CD), with Significant Allometries Most Often at Lower Values of CD. Larger Bees Operate Near the Envelope of Maximal Performance Even in Normodense Hovering Due to Smaller Body Mass-Specific Flight Muscles and Limited Reserve Capacities for Kinematics and Power Production

An information theoretic vulnerability metric for data integrity attacks on smart grids

A novel metric that describes the vulnerability of the measurements in power systems to data integrity attacks is proposed. The new metric, coined vulnerability index (VuIx), leverages information theoretic measures to assess the attack effect on the fundamental limits of the disruption and detection tradeoff. The result of computing the VuIx of the measurements in the system yields an ordering of their vulnerability based on the level of exposure to data integrity attacks. This new framework is used to assess the measurement vulnerability of IEEE 9-bus and 30-bus test systems and it is observed that power injection measurements are overwhelmingly more vulnerable to data integrity attacks than power flow measurements. A detailed numerical evaluation of the VuIx values for IEEE test systems is provided.Comment: 7 pages, 10 figures, submitted to IET Smart Grid. arXiv admin note: substantial text overlap with arXiv:2207.0697

Information Theoretic Data Injection Attacks with Sparsity Constraints

Information theoretic sparse attacks that minimize simultaneously the information obtained by the operator and the probability of detection are studied in a Bayesian state estimation setting. The attack construction is formulated as an optimization problem that aims to minimize the mutual information between the state variables and the observations while guaranteeing the stealth of the attack. Stealth is described in terms of the Kullback-Leibler (KL) divergence between the distributions of the observations under attack and without attack. To overcome the difficulty posed by the combinatorial nature of a sparse attack construction, the attack case in which only one sensor is compromised is analytically solved first. The insight generated in this case is then used to propose a greedy algorithm that constructs random sparse attacks. The performance of the proposed attack is evaluated in the IEEE 30 Bus Test Case.Comment: Submitted to SGC 202

Power Injection Measurements are more Vulnerable to Data Integrity Attacks than Power Flow Measurements

A novel metric that describes the vulnerability of the measurements in power system to data integrity attacks is proposed. The new metric, coined vulnerability index (VuIx), leverages information theoretic measures to assess the attack effect on the fundamental limits of the disruption and detection tradeoff. The result of computing the VuIx of the measurements in the system yields an ordering of the measurements vulnerability based on the level of exposure to data integrity attacks. This new framework is used to assess the measurements vulnerability of IEEE test systems and it is observed that power injection measurements are overwhelmingly more vulnerable to data integrity attacks than power flow measurements. A detailed numerical evaluation of the VuIx values for IEEE test systems is provided.Comment: 6 pages, 9 figures, Submitted to IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grid

Multi-train trajectory optimisation to maximise rail network energy efficiency under travel-time constraints

Optimising the trajectories of multiple interacting trains to maximise energy efficiency is a difficult, but highly desirable, problem to solve. A bespoke genetic algorithm has been developed for the multi-train trajectory optimisation problem and used to seek a near-optimal set of control point distances for multiple trains, such that a weighted sum of the time and energy objectives is minimised. Genetic operators tailored to the problem are developed including a new mutation operation and the insertion and deletion pairs of control points during the reproduction process. Compared with published results, the new GA was shown to increase the quality of solutions found by an average of 27.6% and increase consistency by a factor of 28. This allows more precise control over the relative priority given to achieving time targets or increasing energy efficiency