Knowledge-based fault detection using time-frequency analysis

This work studies a fault detection method which analyzes sensor data for changes in their characteristics to detect the occurrence of faults in a dynamic system. The test system considered in this research is a Boeing-747 aircraft system and the faults considered are the actuator faults in the aircraft. The method is an alternative to conventional fault detection method and does not rely on analytical mathematical models but acquires knowledge about the system through experiments. In this work, we test the concept that the energy distribution of resolution than the windowed Fourier transform. Verification of the proposed methodology is carried in two parts. The first set of experiments considers entire data as a single window. Results show that the method effectively classifies the indicators by more that 85% as correct detections. The second set of experiments verifies the method for online fault detection. It is observed that the mean detection delay was less than 8 seconds. We also developed a simple graphical user interface to run the online fault detection

Honeywell Enhancing Airplane State Awareness (EASA) Project: Final Report on Refinement and Evaluation of Candidate Solutions for Airplane System State Awareness

The loss of pilot airplane state awareness (ASA) has been implicated as a factor in several aviation accidents identified by the Commercial Aviation Safety Team (CAST). These accidents were investigated to identify precursors to the loss of ASA and develop technologies to address the loss of ASA. Based on a gap analysis, two technologies were prototyped and assessed with a formative pilot-in-the-loop evaluation in NASA Langleys full-motion Research Flight Deck. The technologies address: 1) data source anomaly detection in real-time, and 2) intelligent monitoring aids to provide nominal and predictive awareness of situations to be monitored and a mission timeline to visualize events of interest. The evaluation results indicated favorable impressions of both technologies for mitigating the loss of ASA in terms of operational utility, workload, acceptability, complexity, and usability. The team concludes that there is a feasible retrofit solution for improving ASA that would minimize certification risk, integration costs, and training impact

Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach

The rapidly growing world energy use already has concerns over the exhaustion of energy resources andheavy environmental impacts. As a result of these concerns, a trend of green and smart cities has beenincreasing. To respond to this increasing trend of smart cities with buildings every time more complex,in this paper we have proposed a new method to solve energy inefficiencies detection problem in smartbuildings. This solution is based on a rule-based system developed through data mining techniques andapplying the knowledge of energy efficiency experts. A set of useful energy efficiency indicators is alsoproposed to detect anomalies. The data mining system is developed through the knowledge extracted bya full set of building sensors. So, the results of this process provide a set of rules that are used as a partof a decision support system for the optimisation of energy consumption and the detection of anomaliesin smart buildings.Comisión Europea FP7-28522

Integrated Immunity-based Methodology for UAV Monitoring and Control

A general integrated and comprehensive health management framework based on the artificial immune system (AIS) paradigm is formulated and an automated system is developed and tested through simulation for the detection, identification, evaluation, and accommodation (DIEA) of abnormal conditions (ACs) on an unmanned aerial vehicle (UAV). The proposed methodology involves the establishment of a body of data to represent the function of the vehicle under nominal conditions, called the self, and differentiating this operation from that of the vehicle under an abnormal condition, referred to as the non-self. Data collected from simulations of the selected UAV autonomously flying a set of prescribed trajectories were used to develop and test novel schemes that are capable of addressing the AC-DIEA of sensor and actuator faults on a UAV. While the specific dynamic system used here is a UAV, the proposed framework and methodology is general enough to be adapted and applied to any complex dynamic system. The ACs considered within this effort included aerodynamic control surface locks and damage and angular rate sensor biases. The general framework for the comprehensive health management system comprises a novel complete integration of the AC-DIEA process with focus on the transition between the four different phases. The hierarchical multiself (HMS) strategy is used in conjunction with several biomimetic mechanisms to address the various steps in each phase. The partition of the universe approach is used as the basis of the AIS generation and the binary detection phase. The HMS approach is augmented by a mechanism inspired by the antigen presenting cells of the adaptive immune system for performing AC identification. The evaluation and accommodation phases are the most challenging phases of the AC-DIEA process due to the complexity and diversity of the ACs and the multidimensionality of the AIS. Therefore, the evaluation phase is divided into three separate steps: the qualitative evaluation, direct quantitative evaluation, and the indirect quantitative evaluation, where the type, severity, and effects of the AC are determined, respectively. The integration of the accommodation phase is based on a modular process, namely the strategic decision making, tactical decision marking, and execution modules. These modules are designed by the testing of several approaches for integrating the accommodation phase, which are specialized based on the type of AC being addressed. These approaches include redefining of the mission, adjustment or shifting of the control laws, or adjusting the sensor outputs. Adjustments of the mission include redefining of the trajectory to remove maneuvers which are no longer possible, while adjusting of the control laws includes modifying gains involved in determination of commanded control surface deflections. Analysis of the transition between phases includes a discussion of results for integrated example cases where the proposed AC-DIEA process is applied. The cases considered show the validity of the integrated AC-DIEA system and specific accommodation approaches by an improvement in flight performance through metrics that capture trajectory tracking errors and control activity differences between nominal, abnormal, and accommodated cases

Data-driven fault detection using trending analysis

The objective of this research is to develop data-driven fault detection methods which do not rely on mathematical models yet are capable of detecting process malfunctions. Instead of using mathematical models for comparing performances, the methods developed rely on extensive collection of data to establish classification schemes that detect faults in new data. The research develops two different trending approaches. One uses the normal data to define a one-class classifier. The second approach uses a data mining technique, e.g. support vector machine (SVM) to define multi class classifiers. Each classifier is trained on a set of example objects. The one-class classification assumes that only information of one of the classes, namely the normal class, is available. The boundary between the two classes, normal and faulty, is estimated from data of the normal class only. The research assumes that the convex hull of the normal data can be used to define a boundary separating normal and faulty data. The multi class classifier is implemented through several binary classifiers. It is assumed that data from two classes are available and the decision boundary is supported from both sides by example objects. In order to detect significant trends in the data the research implements a non-uniform quantization technique, based on Lloyd’s algorithm and defines a special subsequence-based kernel. The effect of the subsequence length is examined through computer simulations and theoretical analysis. The test bed used to collect data and implement the fault detection is a six degrees of freedom, rigid body model of a B747 100/200 and only faults in the actuators are considered. In order to thoroughly test the efficiency of the approach, the test use only sensor data that does not include manipulated variables. Even with this handicap the approach is effective with the average of 79.5% correct detection and 16.7% missed alarm and 3.9% false alarms for six different faults

Performance-Driven Metamorphic Testing of Cyber-Physical Systems

Cyber-physical systems (CPSs) are a new generation of systems, which integrate software with physical processes. The increasing complexity of these systems, combined with the un certainty in their interactions with the physical world, makes the definition of effective test oracles especially challenging, facing the well-known test oracle problem. Metamorphic testing has shown great potential to alleviate the test oracle problem by exploiting the relations among the inputs and outputs of different executions of the system, so-called metamorphic relations (MRs). In this article, we propose an MR pattern called PV for the identification of performance-driven MRs, and we show its applicability in two CPSs from different domains, which are automated navigation systems and elevator control systems. For the evaluation, we as sessed the effectiveness of this approach for detecting failures in an open-source simulation-based autonomous navigation system, as well as in an industrial case study from the elevation domain. We derive concrete MRs based on the PV pattern for both case studies, and we evaluate their effectiveness with seeded faults. Results show that the approach is effective at detecting over 88% of the seeded faults, while keeping the ratio of FPs at 4% or lower.European Union's Horizon 2020 Research and Innovation Programme (Grant Number: 871319)Junta de Andalucía US-1264651 (APOLO)Junta de Andalucía P18-FR-2895 (EKIPMENT-PLUS)Ministerio de Ciencia e Innovación RTI2018-101204-B-C21 (HORATIO)Mondragon Unibertsitatea IT1519-2