SIMULATION STUDY OF AN ANALYTICALLY-REDUNDANT FLIGHT CONTROL SYSTEM

Instrument Fault Detection and Identification based on Analytical Redundancy

This paper determines the theoretical limits of failure detectability in systems with modeling errors. Such errors are the main obstacle to accurate failure detection in aircraft, jet engines, power generation systems, and most other systems that include mechanical components and/or thermofluid processes. The paper then introduces the reachable measurement intervals (RMI) failure detection method that performs at the limit of detectability. RMI is capable of detecting the smallest theoretically detectable failures in systems with modeling errors, and is implementable in real time on microprocessors. The method is illustrated on a jet engine example. Nomenclature a — entry of the matrix A A = system matrix b = entry of the matrix B B = input matrix Bj = input matrix equal to the identity matrix C = output matrix C{ = output matrix for additional measurements D = direct transmission matrix H = Pontryagin's function K — matrix of gains for additional measurements L = vector of Lagrange multipliers m = entry of the matrix M M = matrix of Lagrange multipliers associated with uncertain entries of A n = entry of the matrix N N = matrix of Lagrange multipliers associated with uncertain entries of B p = discrete-time index corresponding to current time P = performance index for optimization q - vector of system parameters / = time t0 — current time ts = length of the processing window T - sampling time u = input vector x = state vector y = output vector z - vector of additional measurements Subscripts d = discrete e = uncertain and uncorrelated n = nominal v = uncertain

INSTRUMENT FAULT DETECTION AND IDENTIFICATION BASED ON ANALYTICAL REDUNDANCY

The number of operations incorporating E-VTOL aircrafts is increasing each year, and the optimization of the energy consumption of such vehicles is a major problem. In this paper, a small-scale ARCHER helicopter’s energy consumption is analyzed, wherein different flight conditions, main rotor revolutions, and flight control system settings are considered. The helicopter dynamic model was developed in the FLIGHTLAB environment and was then validated based on flight test data. The model used for the calculation of energy consumption was developed using the electric and dynamic characteristics of the main rotor, electric motor, and transmission system. The main part of this work concerns the analysis of electric energy consumption during the vehicle’s flight via the use of an automatic flight control system (AFCS) that ensures repeatable flight conditions. The AFCS was designed such that it includes both path and attitude control to provide hover and cruise control modes. The helicopter’s energy consumption was analyzed during different phases of flight, when executing maneuvers, and using different main rotor angular velocities to perform - a given task. The results show that the level of energy consumption significantly depends on the helicopter’s main rotor revolutions, flight speed, and the maneuvers performed. The proposed methodology can be used in prospective energy-efficient mission planning and UAV helicopter design.

In this paper, we propose a novel approach for Failure Detection and Identification (FDI) in nonlinear systems based on the Interacting Multiple Model (IMM) Extended Kalman Filter (EKF) approach. In the nonlinear system FDI application, the main idea consists of representing each failure mode by a model and combining the outputs of EKF's based on different models in a near-optimal way. This IMM-FDI filter provides not only failure detection and identification but also a near-optimal estimate of the system state (even during a failure). The approach has been applied successfully to a problem of spacecraft autonomy for the detection and identification of sensor (gyro, star tracker) and actuator failures. The results of this application show that IMM-EKF detects and identifies failures much more rapidly and reliably than the multi-hypothesis EKF. Furthermore, it handles satisfactorily both permanent and transient failures. Current efforts are underway to perform extensive validation testing on high-fidelity simulation models of representative spacecraft.

This article contains highlights of paper SPE 189799, “Self-Learning Probabilistic Detection and Alerting of Drillstring Washout and Pump Failure Incidents During Drilling Operations,” by A. Ambrus, formerly with Intellicess; P. Ashok, D. Ramos, A. Chintapalli, Intellicess; A. Susich, T. Thetford, B. Nelson, M. Shahri, J. McNab, and M. Behounek, Apache Corp., presented at the 2018 IADC/SPE Drilling Conference and Exhibition, Fort Worth, Texas, 6–8 March. https:/doi.org/10.2118/189700-MS Introduction In recent years, detection and alerting systems have been applied to numerous drilling failures, including stuck pipe, fluid influx/loss, and drilling dysfunctions. But the detection of drillstring washout and mud pump failure has been left primarily to traditional methods that rely solely on standpipe pressure and pump rates or on measurement-while-drilling (MWD) sensor data. Drillers commonly use a simple hydraulic coefficient relating standpipe pressure to pump rate to detect drillstring washouts. MWD pressure and mud motor data may improve detection but this approach remains problematic owing to poor-quality sensor data and various factors that affect flow and pressure data. As a result, drillers often do not recognize a crack in the drillstring before it grows into a washout that may cost operators hundreds of thousands of dollars in lost time, equipment replacement, and fishing operations. Drillers also rely on standpipe pressure changes to detect mud pump failures and degradation from damaged pump parts. High-frequency pump pressure data may enhance pump wear detection and accelerometer data may be used to infer valve leaks, but these solutions are impractical in most field applications. To address these shortcomings, a methodology has been introduced using basic rig sensors and contextual information to detect various drilling failure modes. It has applied that same approach to early wash-out and pump failures detection and alerting. Methodology The detection and alerting system uses a Bayesian network, which aggregates key sensor inputs and contextual data and predictions from hydraulic modeling. Cumulatively they are the inputs to a probabilistic belief system. The probabilistic model outputs belief values of between 0 and 1 that are indicative of specific drilling, equipment, and sensor failures. Just as the human brain uses experience to generate beliefs upon which to act and react to the world around it intelligently, the probabilistic model uses past and present trends and artificial intelligence (AI) to generate beliefs about drilling dysfunctions and equipment failures. The model also assesses these trends to increase its accuracy through self-learning and self-calibration that enables it to adjust for poor sensor data, drilling conditions, and model uncertainties. As a consequence, when the value of a belief rises to a specified level and the system triggers an alert, the driller can act upon it with increased certainty that it is not a false alarm. Bayesian networks consist of discrete, or continuous-valued, nodes. Nodes are connected via conditional probability tables (CPTs) and linked by arrows generally representing the direction of causation. Each CPT is assigned a specific weight based on its relative impact on specific outcomes. A Bayesian Network in which the key node is “Unplanned Event” includes several event types and their associated probabilities, or beliefs. This network arrangement can be used to detect a broad variety of incidents related to well control and hydraulics, such as washouts, pump failures, kicks, lost circulation, wellbore breathing, packoff and others to which a probability value is assigned.

A new Sensor Validity Monitoring, Verification, and Accommodation (SVMVA) technique based on an artificial neural network is developed for a self-repairing Flight Control System (FCS). For the proposed system, the Learning Vector Quantization (LVQ) method is employed as the on-line, real time learning, monitoring, and estimation tool. In order to conduct a feasibility study, we applied the developed algorithm to a flight vehicle simulator. The simulation results show that the proposed SVMVA with LVQ can instantly detect the failure of physical sensors and accommodate them for more than 30 minutes. By employing this type of analytical sensor redundancy, a flight vehicle can save power, weight, and space, which are required for installing redundant physical sensors.

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The ability to conduct an automatic landing maneuver is a key requirement for many unmanned aerial vehicles. By the example of the novel SAGITTA Demonstrator UAV, an autoland algorithm is presented as a modular extension to an automatic flight guidance and control system. At the Institute of Flight System Dynamics of the Technical University of Munich, a procedural definition of the automatic landing has been elaborated and implemented as a finite state machine. Based on this, existing guidance and control modules of the auto flight system are utilized to perform the landing maneuver. Particular attention has been paid to the design of transition conditions of the state machine, as these guide the temporal sequence of the maneuver and must reflect the high uncertainties that prevail concerning the performance of a novel aircraft configuration and its systems. Simulation testing and verification results in combination with ground testing of SAGITTA present a proof of concept of the design and give an outlook to its application during SAGITTA's maiden flight.

Automatic Flight Control System (AFCS) Terrain Following (TF) mode allows military aircraft to fly at a certain altitude above ground level at a low altitude. TF mode reduces the probability of aircraft detection by enemy airborne radars. TF mode minimizes the effort the pilot spends to control the aircraft and allows the pilot to focus on other tasks or missions. In this study, the F-16 nonlinear model is linearized around a selected equilibrium point. The state variables of the linear model are decomposed into state space matrices on the lateral and longitudinal axes. Three different control methods, namely PID (Proportional-Integral-Derivative), LQR (Linear Quadratic Regulator), and MRAC (Model Reference Adaptive Control), are used. The results show that the designed algorithms can effectively control the aircraft's altitude, speed, pitch angle, angle of attack, and pitch rate on the longitudinal axis and the aircraft flies in accordance with the terrain profile. Finally, it is observed that MRAC outperforms PID and LQR methods due to its adaptive capability.

This chapter introduces a number of the issues which must be addressed during the design and development of an automatic flight control system (AFCS). It does not make a comparison of potential system architectures nor does it suggest an optimum approach because each AFCS is different, depending upon the airframe and application. However, there is a common theme associated with an AFCS development programme that can be introduced and used to familiarise the reader with some of the concepts which must be addressed during the design evolution.

A failure detection and isolation (FDI) method for validation of ultrasonic ranging sensor (URS) signals in robot position control systems is presented. The technique builds on the concepts of parity space and analytic redundancy, where integration of analytic and sensor redundancy provides a direct, reliable method for measuring the end-effector position of a robot relative to the world coordinates. These measurements are not influenced by deflections caused by the payload, accumulated joint measurement errors in a serial mechanism, or computational errors in executing kinematic relationships. The position control system's insensitivity to structural deflections allows the robot to handle larger payloads. Simulation results are presented to demonstrate how the FDI technique can be applied. >

In the paper, the results of a full-scale engine demonstration of a sensor failure detection algorithm are presented. The algorithm detects, isolates, and accommodates sensor failures using analytical redundancy. The experimental hardware, including the F100 engine, is described. Demonstration results were obtained over a large portion of a typical flight envelope for the F100 engine. They include both subsonic and supersonic conditions at both medium and full, nonafter burning, power. Estimated accuracy, minimum detectable levels of sensor failures, and failure accommodation performance for an F100 turbofan engine control system are discussed.

In a robot control system there are dynamics and sensor faults, modeling errors, and system and measurement noises. This paper addresses several techniques to control the robot manipulator under the uncertainty model and sensor failure. First, a neural network is proposed to control the robot to follow the generated path. This network model is able to compensate the structured and unstructured dynamic uncertainties of the robot by using both online and off-line training. The PD controller, computed torque controller and adaptive controller with and without exact model are used to control the robot and compared with the proposed method. Next, a residual generator for detection and isolation of the sensor failure is designed. Assuming one fault at a time, the proposed method can detect and isolate the fault sensor. The simulation study based on a 2-DOF planar robot is included.

The contemporary industrial systems are growing to be dependent on the high-dimensional sensor networks, thus exposing themselves to unknown faults that threaten the reliability and safety of operations. Conventional threshold based and supervised learning models fail in these settings particularly when labeled fault information is scarce or non-existent. As a remedy to these shortcomings, this paper introduces an unsupervised LSTM-Autoencoder model that can be used to detect abnormalities and learn to identify sensor failures at an early stage without using annotated data. The model is fit on regular-operation sequences only sampled off of the Kaggle Sensor Fault Detection Dataset, where reconstruction error is used as the anomaly score. The results of the experiment prove the constant decrease of reconstruction loss, high detection rate of 95.3%, and significant reduction of false-alarm rates, as well as good early fault detection. The model is useful in identifying slight pattern deviations on a temporal pattern way before major sensor failures take place. Altogether, the suggested LSTM-Autoencoder is a scalable, robust, and label-free tool to facilitate sensor health in a complex and high-dimensional industrial setting.