Competitive evaluation of failure detection algorithms for strapdownredundant inertial instruments

Guaranteed Active Failure Detection and Isolation for Linear Dynamical Systems

EDUNDANCY management for the purpose of enhaning reliability is performed in three stages: failure detection in which the presence of a failure is discovered, failure isolation in which the failed instrument is identified, and failure correction in which a suitable reorganization of the system is accomplished. Many different failure detection, isolation, and correction (FDIC) algorithms for inertial navigators have appeared in the literature. Eight FDIC algorithms for the dodecahedron configuration *'2 of six strapdown gyros and 6 strapdown accelerometers have been compared in a previous study.3 We shall limit our discussion to the strapdown dodecahedron inertial navigation case although the methods to be developed are applicable to other cases in which the outputs of redundant devices are combined by least squares. All 8 of the FDIC algorithms previously mentioned require a finite period of time to isolate a failure. If the failure causes a large enough error in the instrument output, it can be isolated in less than a minor cycle time so that no erroneous output is used and will be referred to as a hard failure. A failure that causes a smaller error in the instrument output cannot be isolated in less than a minor cycle time so that erroneous output is used. It will be referred to as a soft failure. Thus soft failures degrade system performance while hard failures do not. An algorithm is desired that retroactively removes the effects of soft failures from the navigational solution at the time that the failure is finally isolated. Ideally, only the errors contributed by the instrument since the time of failure would be removed. However the time of failure is unknown, so all of the errors contributed by the failed instrument since initialization of the navigate mode will be removed. Since the FDIC cannot distinguish hard from soft failures, all of the error of the failed instrument is removed even if, in the case of hard failures, no error was introduced by the failure itself. Through the action of the least squares estimator, elimination of the error of one instrument magnifies the effects of the errors of the remaining unfailed instruments to the level they would have reached if the failed instrument had never been present. However this increase in error will usually be less

Software-Defined Networking (SDN) is a networking approach that separates the network's control plane and data plane to provide better network control. The nature of SDN requires an essential focus on its reliability and availability, which includes failure detection aspects. Several techniques have been proposed to enhance SDN failure detection, including passive and active approaches. However, the Route Inspection (RI) algorithm, an example of the active probing method in active detection, has scalability issues when deployed in large-scale networks as it generates inefficient and lengthy probe paths. To address this, we propose using community detection (CD) algorithms to improve the probe path generation of the RI algorithm, resulting in the 'Failure Detection using Community Detection and probing (FDCD)' framework. With the aim to reduce travel paths, the proposed framework minimizes the network topology using CD algorithms to reduce the topology size into smaller communities. Then, the RI algorithm calculates the probe path in the communities instead of the single large topology, thus improving the efficiency of the failure detection mechanism. This paper adopts three community detection algorithms: Louvain, Label Propagation, and Girvan-Newman. We evaluate the framework using three well-known network topologies, COST266, PIORO40, and GERMANY50, in Mininet to observe the framework's performance. The results indicate that the proposed approach improves the algorithm's scalability against its baseline approach, improving the average probe round trip time by 66.87% and the average path installation time by 2.67%.

The use of temperature rate represents a unique approach to the design of control and guidance system for re-entry vehicles. The main design objective achieved in this system is the separation of vehicle safety from the task of accurate navigation. The safety portion of the Temperature Rate Flight Control System (TRFCS), utilizing thermocouples as primary sensors rather than the normally used inertial instruments, is very simple and therefore reliable. TRFCS provides short-period stability for the vehicle as well as trajectory control, which results in minimizing temperature peaks and limiting of maximum dynamic pressure and g loads. Around this basic temperature rate autopilot are closed navigation and guidance loops. A failure in the navigation system, which of necessity is a piece of complex equipment, will not affect at all the safety features mentioned. In emergency, in case of automatic system failure, TRFCS can be flown manually. This consists essentially of flying constant temperature rates. The stability and performance of the over-all system was demonstrated by extensive analog and digital simulations, some of which included the pilot in the loop. Following analytical phase, the sensor and electronic hardware were developed. TRFCS recently has been flight tested successfully on the Aerothermodynamic Elastic Structural Systems Environment Test (ASSET) vehicle.

Design defects come from poor design choices and have the effect of degrading the quality of object-oriented designs.Therefore, they present opportunities for improvements.However, design defects have not been precisely specified and there are few appropriate tools that allow their detection as well as their correction. Our goal is to provide a systematic method to specify systematically design defects precisely and to generate automatically detection and correction algorithms from their specifications. The detection algorithms are based not only on metrics but also on semantical and structural properties whereas the correction algorithms are based on refactorings. We apply and validate these algorithms on open-source object-oriented programs to show that our method allows a systematic specification, a precise detection, and a suitable correction of design defects.

Wireless Sensor Networks (WSN), consists tiny but powerful devices to monitor or sense specific phenomena, e.g. temperature, visual, humidity etc. In WSNs nodes are deployed in the harsh and unattended environment. In such scenario, nodes may fail due to various reasons, e.g. link failure, battery depletion, hardware/software failure etc. In order to provide a reliable network, fault-tolerant network is required. Furthermore in WSN, energy is one of the major constraints, due to irreplaceable batteries. There have been various techniques proposed for fault tolerance and energy saving by detecting and correcting node failure. In this paper, the proposed algorithm called Cluster Head Fault Detection and Correction algorithm (FDFC), detects Cluster Head (CH) failure in a distributed manner and replace a faulty node. In this work, all the CHs present in network share status data with all the other CHs. Besides fault detection, this algorithm also suggests faulty node replacement technique. The effectiveness of the proposed algorithms is validated through simulation experiments.

This paper describes a power amplifier system for testing of distance relay operating characteristic. This power amplifier is based on two three phase inverters with a neutral wire. The output power currents and voltages are controlled by a sliding mode controller. A developed matlab/simulink digital simulator generates the test signals. This test signals are converted in current and voltage references using a microcontroller to connect the digital simulator to the power converter amplifier. Experimental results from a laboratory prototype are presented and discussed

We propose an adaptive run-time failure recovery control system for quadcopter drones, based on remote real-time processing of measurement data streams. Particularly, the measured RPM values of the quadcopter motors are transmitted to a remote machine which hosts failure detection algorithms and performs recovery procedure. The proposed control system consists of three distinct parts: (1) A set of computationally simple PID controllers locally onboard the drone, (2) a set of computationally more demanding remotely hosted algorithms for real-time drone state detection, and (3) a digital twin co-execution software platform — the ModelConductor-eXtended — for two-way signal data exchange between the former two. The local on-board control system is responsible for maneuvering the drone in all conditions: path tracking under normal operation and safe landing in a failure state. The remote control system, on the other hand, is responsible for detecting the state of the drone and communicating the corresponding control commands and controller parameters to the drone in real time. The proposed control system concept is demonstrated via simulations in which a drone is represented by the widely studied Quad-Sim six degrees-of-freedom Simulink model. Results show that the trained failure detection binary classifier achieves a high level of performance with F1-score of 96.03%. Additionally, time analysis shows that the proposed remote control system, with average execution time of 0.49 milliseconds and total latency of 6.92 milliseconds in two-way data communication link, meets the real-time constraints of the problem. The potential practical applications for the presented approach are in drone operation in complex environments such as factories (indoor) or forests (outdoor).

Electric field probe performance is measured inside a calibration waveguide. The electric field probes based on diode detection are designed and calibrated for continuous signals. When measuring electric field strength of a pulsed waveform, performance degradation is expected. The degradation is measured in terms of measurement error. It is defined as electric field strength reading on the probe vs. actual electric field strength in the test volume of a calibration waveguide. Two such probes were measured. Test signals were pulsed (ASK) signals with fixed duty cycle of 1/10 and varying repetition frequency. The results show great differences among the two probes measured. One probe shows only slight degradation in terms of increased reading (positive measurement error) for high repetition frequencies. The second probe shows the same feature but of even greater extent, together with significant degradation for low repetition frequencies in terms of decreased reading (negative measurement error). The existence of such errors demands a great amount of caution during EMC measurements using diode-based electric field probes.

This paper describes Team Tidyboy's approach to build a functional, robust, yet highly modular software framework for home service robots. Home service tasks require a number of software modules to work together, which includes verbal and nonverbal human robot interactions, 3D perception of known and unknown objects and manipulation of various objects using movement of both the manipulator and the base of the robot. World Robot Summit (WRS) 2020 Partner Robot Challenge (PRC) rule has raised the bar higher by asking for a reliable, long-term operation of such a system without human intervention. We present how we have overcome such challenges by robust software modules, repeated experiments in randomly initialized simulated environments, and finally introduction of various failure detection and recovery algorithms. Our software is successfully validated in RoboCup@Home 2021 and WRS 2020 PRC remote participation league, which led us to obtaining the first places in both competitions.

The performance of all failure detection, isolation, and accommodation (DIA) algorithms is influenced by the presence of model uncertainty. The authors present a unique framework to incorporate a knowledge of modeling error in the analysis and design of failure detection systems. A concept is introduced called the threshold selector, which is a nonlinear inequality whose solution defines the set of detectable sensor failure signals. It identifies the optimal threshold to be used in innovations-based DIA algorithms. The optimal threshold is shown to be a function of the bound on modeling errors, the noise properties, the speed of DIA filters and the classes of reference and failure signals. The size of the smallest detectable failure is also determined. The results are applied to a multivariable turbofan jet engine example, which demonstrates improvements compared to previous studies.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A fully automatic redeye detection and correction algorithm is presented to address the redeye artifacts in digital photos. The algorithm contains a redeye detection part and a correction part. The detection part is modeled as a feature based object detection problem. Adaboost is used to simultaneously select features and train the classifier. A new feature set is designed to address the orientation-dependency problem associated with the Haar-like features commonly used for object detection design. For each detected redeye, a correction algorithm is applied to do adaptive desaturation and darkening over the redeye region.

Roller bearings are critical components in various mechanical systems, and the timely detection of potential failures is essential for preventing costly downtimes and avoiding substantial machinery breakdown. This research focuses on finding and verifying a robust method that can detect failures early, without creating false positive failure states. Therefore, this paper introduces a novel algorithm for the early detection of roller bearing failures, particularly tailored to high-precision bearings and automotive test bed systems. The featured method (AFI-Advanced Failure Indicator) utilizes the Fast Fourier Transform (FFT) of wideband accelerometers to calculate the spectral content of vibration signals emitted by roller bearings. By calculating the frequency bands and tracking the movement of these bands within the spectra, the method provides an indicator of the machinery's health, mainly focusing on the early stages of bearing failure. The calculated channel can be used as a trend indicator, enabling the method to identify subtle deviations associated with impending failures. The AFI algorithm incorporates a non-static limit through moving average calculations and volatility analysis methods to determine critical changes in the signal. This thresholding mechanism ensures the algorithm's responsiveness to variations in operating conditions and environmental factors, contributing to its robustness in diverse industrial settings. Further refinement was achieved through an outlier detection filter, which reduces false positives and enhances the algorithm's accuracy in identifying genuine deviations from the normal operational state. To benchmark the developed algorithm, it was compared with three industry-standard algorithms: VRMS calculations per ISO 10813-3, Mean Absolute Value of Extremums (MAVE), and Envelope Frequency Band (EFB). This comparative analysis aimed to evaluate the efficacy of the novel algorithm against the established methods in the field, providing valuable insights into its potential advantages and limitations. In summary, this paper presents an innovative algorithm for the early detection of roller bearing failures, leveraging FFT-based spectral analysis, trend monitoring, adaptive thresholding, and outlier detection. Its ability to confirm the first failure state underscores the algorithm's effectiveness.

Skew Correction of Textural Documents

A fully automatic redeye detection and correction algorithm was developed at Eastman Kodak Company Research Laboratories. The algorithm is highly sophisticated so that it is able to distinguish most redeye pairs from scene content. It is also highly optimized for execution speed and memory usage enabling it to be included in a variety of products. Detected redeyes are corrected so that the red color is removed, but the eye maintains a natural look.