Faulty Behavior Research Articles

Fault Identification of Discrete Event Systems Modeled by Petri Nets With Unobservable Transitions

This paper deals with the identification problem of faulty behavior in a discrete event system, assuming that the fault-free model of a system is given in terms of Petri nets, where the set of transitions is divided into two disjoint subsets: 1) observable and 2) unobservable ones. The observed system output is defined as a transition-marking sequence, i.e., each transition is followed by a marking. First, a nonlinear integer programming model that characterizes the faults modeled by fault transitions is built according to the abnormal behavior extracted from the observed sequence. Then, it is converted into an integer linear programming (ILP) problem and a faulty net that preserves the structure of the fault-free one is obtained by solving this ILP model. In addition, an algorithm is developed to ensure acyclicity of the resulting unobservable subnet whose transition set is composed of the unobservable transitions of the fault-free net and the identified fault transitions.

Fast adaptive comparator offset calibration in pipeline ADC with self‐repairing thermometer to binary encoder

SummaryThis paper presents a fast background calibration method for comparator offsets in pipeline ADCs and analyzes the practical considerations in a 1.8 V 0.18 μm 100Msps pipeline ADC with 15‐bit resolution (74 dB‐Signal‐to‐noise plus Distortion Ratio [SNDR]). A self‐repairing (SR) thermometer‐to‐binary encoder is developed to deal with malfunctioning in presence of high comparator offsets greater than one‐half least‐significant bit (LSB). In this situation, the effective thresholds between two adjacent comparators could be inverted leading to a faulty behavior with conventional architectures. The proposed solution allows a dynamic assignment of the calibration code associated to each comparator improving convergence speed. As demonstrator, its application to a 15‐bit pipeline ADC using a novel calibrated dynamic‐latch comparator (DLC) with internal threshold reference generation and no preamplifier is presented, showing a reduction on the total power consumption of 22% with respect to a design without calibration targeting the same specifications.

Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?

Identification of faulty appliance behaviour in real time can signal energy wastage and the need for appliance servicing or replacement leading to energy savings. The problem of appliance fault or anomaly detection has been tackled vastly in relation to submetering, which is not scalable since it requires separate meters for each appliance. At the same time, for applications such as energy feedback, Non-intrusive load monitoring (NILM) has been recognised as a scalable and practical alternative to submetering. However, the usability of NILM for anomaly detection has not yet been investigated. Since the goal of NILM is to provide energy consumption estimate, it is unclear if the signal fidelity of appliance signatures generated by state-of-the-art NILM is sufficient to enable accurate appliance fault detection. In this paper, we attempt to determine whether appliance signatures detected by NILM can be used directly for anomaly detection. This is carried out by proposing an anomaly detection algorithm which performs well for submetering data and evaluate its ability to identify the same faulty behaviour of appliances but with NILM-generated appliance power traces. Our results on a dataset of six residential homes using four state-of-the-art NILM algorithms show that, on average, NILM traces are not as robust to identification of faulty behaviour as compared to using submetered data. We discuss in detail observations pertaining to the reconstructed appliance signatures following NILM and their fidelity with respect to noise-free submetered data.

Quad Bike Riders’ Attitudes toward and Use of Safety Technologies

Worldwide, the number of quad bike-related injuries and fatalities has increased over the last 30 years and, although quad bike safety has become increasingly important over the last decade, greater understanding of the risks associated with these vehicles is required. This study aimed to improve understanding of the interrelationships between risk perception, risk management, and the use of safety technologies in quad bike operation. A total of 70 quad bike riders were interviewed in two sets of interviews: one targeting riders visiting two forestry fairs, and the other targeting riders with quad bikes equipped with ROPS. The interviews showed that the quad bike riders had a tendency for externalization of risk. This result possibly derives from the articulation and allocation of blame; the majority of respondents saw the problem of quad bike safety as a result of faulty rider behavior, thus shifting focus away from the technology as well as from themselves. This also applies to many of the interviewed ROPS owners, as one of their prime motivators in acquiring ROPS was to improve the safety of others rather than their own safety.

Fault Detection of Wind Turbine Sensors Using Artificial Neural Networks

This paper proposes a method for sensor validation and fault detection in wind turbines. Ensuring validity of sensor measurements is a significant part in overall condition monitoring as sensor faults lead to incorrect results in monitoring a system’s state of health. Although identifying abrupt failures in sensors is relatively straightforward, calibration drifts are more difficult to detect. Therefore, a detection and isolation technique for sensor calibration drifts on the purpose of measurement validation was developed. Temperature sensor measurements from the Supervisory Control and Data Acquisition system of a wind turbine were used for this aim. Low output rate of the measurements and nonlinear characteristics of the system drive the necessity to design an advanced fault detection algorithm. Artificial neural networks were chosen for this purpose considering their high performance in nonlinear environments. The results demonstrate that the proposed method can effectively detect existence of calibration drift and isolate the exact sensor with faulty behaviour.

Distributed Calculation of Edge-Disjoint Spanning Trees for Robustifying Distributed Algorithms Against Man-in-the-Middle Attacks

In this paper, we provide a distributed methodology to allow a network of agents, tasked to execute a distributed algorithm, to overcome Man-in-the-Middle (MITM) attacks that aim at steering the result of the algorithm toward inconsistent values or dangerous configurations. We want the agents to be able to restore the correct result of the algorithm despite the attacks. To this end, we provide a distributed algorithm to let the set of agents, interconnected by an undirected network topology, construct several edge-disjoint spanning trees by assigning labels to their incident edges. The ultimate objective is to use these spanning trees to run multiple instances of the same distributed algorithm in parallel, in order to detect MITM attacks or other faulty or malicious link behavior (e.g., when the instances yield different results) and to restore the correct result (when the majority of instances is unaffected). The proposed algorithm is lightweight and asynchronous, and is based on iterated depth-first visits on the graph. We complement this paper with a thorough analysis of the performance of the proposed algorithms.

Towards increasing nanosatellite subsystem robustness

Short development life cycle and low cost of cubesat-based mission have motivated the growing number of nanosatellite launched in the last decade around the world. Fast and cheaper space project do not guarantee success in orbit. The lack of good practices on design, assembly and tests has been pointed out as one of the major causes to nanosatellite mission failures. Efforts on the use of verification and validation techniques are required. Because the increased use of nanosatellites missions for technology qualification of payloads on orbit, faulty behavior of those payloads can be expected. However, such malfunction shall not represent a risk to the whole mission. Robustness is an important property of reactive critical system not addressed properly in the cubesat standardization. Although significant mitigation of the interface failures has been observed at hardware level in the integration phase of the payloads with the nanosatellite platform, behavior aspects of the communicating subsystems on the use of these interfaces shall be verified. The test systematization of CubeSat-based nanosatellites supported by proper tools is necessary to reduce the mission development cycle in terms of the time consumed by the verification & validation activities. In this paper we present a failure emulator mechanism framework, named FEM, for robustness testing of interoperable software-intensive subsystems onboard nanosatellite. FEM acts in the communication channel being part of the integration test workbench in two phases of nanosatellite design: (i) robustness requirement specification using model in the loop (MIL) and (ii) robustness validation using hardware in the loop (HIL). The architectural aspects of the proposed FEM framework support its instantiation to any communication channel of the CubeSat standard. As an example, FEM prototype was instantiated to I2C communication channel to support NanosatC-BR2 testing. NanosatC-BR2 is a Cubesat based scientific mission, under development and integration at Brazilian Institute for Space Research (INPE), which uses I2C communication channel for its payloads interactions with the On-Board Data Handling computer subsystem (OBC). FEM prototype was used to support OBC integration testing with each payload subsystem at MIL scenario aiming at anticipating the robustness requirement verification on the development lifecycle. Moreover, the FEM prototype was also validated at HIL scenario using Test Cases automatically generated. Results of these two scenarios executions are reported demonstrating in a case study the effectiveness of FEM framework in detecting the lack or noncompliance of robustness requirements by the interoperated subsystems under testing.

Resilient distributed state estimation with mobile agents: overcoming Byzantine adversaries, communication losses, and intermittent measurements

Applications in environmental monitoring, surveillance and patrolling typically require a network of mobile agents to collectively gain information regarding the state of a static or dynamical process evolving over a region. However, these networks of mobile agents also introduce various challenges, including intermittent observations of the dynamical process, loss of communication links due to mobility and packet drops, and the potential for malicious or faulty behavior by some of the agents. The main contribution of this paper is the development of resilient, fully-distributed, and provably correct state estimation algorithms that simultaneously account for each of the above considerations, and in turn, offer a general framework for reasoning about state estimation problems in dynamic, failure-prone and adversarial environments. Specifically, we develop a simple switched linear observer for dealing with the issue of time-varying measurement models, and resilient filtering techniques for dealing with worst-case adversarial behavior subject to time-varying communication patterns among the agents. Our approach considers both communication patterns that recur in a deterministic manner, and patterns that are induced by random packet drops. For each scenario, we identify conditions on the dynamical system, the patrols, the nominal communication network topology, and the failure models that guarantee applicability of our proposed techniques. Finally, we complement our theoretical results with detailed simulations that illustrate the efficacy of our algorithms in the presence of the technical challenges described above.

Structural Fault Detection and Isolation in Hybrid Systems

This paper develops a structural diagnosis approach for fault detection and isolation in hybrid systems. Hybrid systems are characterized by continuous behaviors that are interspersed with discrete mode changes in the system, making the analysis of behaviors quite complex. In this paper, we address the mode detection problem in hybrid systems as the first step in diagnoser design. The proposed method uses analytic redundancy methods to detect the operating mode of the system even in the presence of system faults. We define hybrid minimal structurally overdetermined (HMSO) sets for hybrid systems. For residual generation, we develop the HMSO selection problem, formulated as a binary integer linear programming optimization problem to minimize the number of selected HMSOs and reduce online computational costs of the diagnosis algorithm. The proposed structural approach does not require preenumeration of all possible modes in the diagnoser design step. Therefore, our approach is feasible for hybrid systems with a large number of switching elements, implying that the system can have a large number of operating modes. The case study demonstrates the effectiveness of our approach. We discuss the results of our case study, and present directions for future work. Note to Practitioners —Developing feasible approaches for online monitoring, fault detection, and fault isolation of complex hybrid and embedded systems, such as automobiles, aircraft, power plants, and manufacturing processes, is essential in securing their safe, reliable, and efficient operation. Frequent changes in the operational modes of these systems because of operator actions, such as changing gears in an automobile, or environmental changes, such as driving on a wet or icy road make the fault detection and isolation task in these systems challenging. It is important to detect and isolate faults in all the operating modes, and at the same time, not mistake a mode change as a fault in the system. In this paper, we propose an approach that exploits the equation structure of hybrid systems behavior to combine mode detection and diagnosis in nonlinear hybrid systems. The proposed algorithm is scalable and efficient. We demonstrate its effectiveness using a case study of a reverse osmosis subsystem in an advances life support system for long duration manned space missions. Important challenges that can affect the success of our approach include the need for sufficiently detailed hybrid models that capture nominal and faulty behavior, and a sufficient number of sensors to make simultaneous mode detection and fault isolation possible.

A Simple-to-Implement Fault Diagnosis Method for Open Switch Fault in Wind System PMSG Drives without Threshold Setting

The conversion system is a major contributor to failure rates. These faults lead to time and cost consuming. Fault diagnosis capabilities pay as a solver to achieve a steady system. This paper presents a full analysis of permanent magnet synchronous generator wind system (PMSGWS) and proposes a special RMS voltage-based fault diagnosis method. The full analysis presents a comprehensive knowledge of faulty behaviors especially under arm current flowing or cutting off. Due to enough knowledge of faulty behaviors, the implementation of the detection method without threshold setting is contributed by the special RMS voltage. Its sample period is set longer than the time of the maximum pulse width ratio (MPR) and shorter than the fault show time of lower tube voltage. Due to this, the detection speed and robustness are achieved. By these simple settings for the fault diagnosis method, the faulty switch is detected in less than 1/4 of the period. Simulation and experimental results confirm the validity and feasibility of the new proposed fault detection method.

Interpretable Unsupervised Feature Extraction and Learning of Abnormal System State Transitions in Aircraft Sensor Data

Commercial aircrafts generate a huge amount of data during each flight by sampling hundreds of variables at different resolutions during all phases of flight. While having this enormous source of data is useful for learning of faulty system behavior, its huge dimensionality and size can be an impeding factor to such analysis.
 To address this problem, we have devised a data-driven process that automatically extracts persistent, underlying latent states that can succinctly describe the data and thereby reduce its dimensionality, while preserving the most salient aspects important for fault or potential fault analysis. By analyzing how these latent states transition in time by computing a transition matrix for every leg, which we use as features, we can classify certain precursors which are indicative of a potential fault. Specifically, this is achieved by supervised and unsupervised learning of hundreds of latent state transitions for a given subsystem. Analysis of temporal dynamics of state transitions allows us to pinpoint at what time the sensor variables were behaving atypically in a flight leg, thus allowing airline maintainers to fix the faulty component quicker and avoid flight delays due to unplanned maintenance. We demonstrate our method of supervised and unsupervised classification over temporal dynamics of system state transition on two subsystems, the Fan Air Modulating Valve (FAMV) and the Flow Control Valve (FCV), and have obtained 100% true positive rate (for both systems) and a false positive rate of 0.05-0.08%.

The Quest for Materials-Based Hydrogels with Antimicrobial and Antiviral Potentialities.

In recent years, the Antimicrobial Resistance (AMR) or Multidrug Resistance (MDR) and viral infections have become serious health issues, globally. Finally, after decades of negligence, the AMR/MDR and viral infection issues have now captured a worldwide attention of the global leaders, public health community, legalization authorities, academia, research-based organizations, and medicinal sector of the modern world, alike. Aiming to resolve these issues, various methodological approaches have been exploited, in the past several years. Among them, biomaterials-based therapeutic hydrogels are of supreme interests for an enhanced and efficient delivery in the current biomedical sector. Depending on the regulatory authorities and practices, the antibiotics consumption was expedited than ever before driven by rising and increasing access, across the globe. Though the emergence of AMR/MDR in microorganisms and emergence/reemergence of viral infections are considered as a natural phenomenon, however, these concerning issues have been driven by those mentioned above faulty human behavior. In this context, many scientists, around the globe, are working at wider spectrum to resolve this problematic issue, efficiently. A proper understanding of biological mechanisms is essential to combat this global threat to the living beings. In this review, an effort has been made to highlight the potent features of materials based hydrogels possessing antimicrobial and antiviral potentialities. The information is also given on the potential research activities, and possible mechanisms of actions of hydrogels are discussed with a closeup look at the future recommendations.

Experimental evaluation of the performance of arrays of MEMS accelerometers

Abstract The performance of arrays of accelerometers is critical for various fields of engineering, but in contrast to that of isolated sensors, it cannot be easily evaluated. This is because it requires that both waveforms and instantaneous, especially extreme values of acceleration of all array sensors are accurately recorded without time lag. Although accelerometers are typically assumed to be characterized by white noise, this requirement is not easily satisfied, and there is evidence of serious problems with recorded accelerations, especially when used as input in structural analysis. For this reason, various techniques to overpass the problem of uncertainties, or even the faulty behavior of array sensors have been proposed. In our study we propose a simple and efficient method to test the performance of arrays of MEMS accelerometers which are currently used in various engineering applications. We made controlled experiments using identical, fully collocated MEMS sensors simultaneously recording source accelerations under common propagation effects, so that the timeseries of waveform differences unambiguously define sensor noise. Analysis of the sensors response indicates that dominant spectral frequencies are precisely defined by all sensors, but there were noticed significant waveform distortions due to dynamic noise of different types, mostly clipping below the saturation level of the sensor response. Dynamic noise tends to increase with the frequency level and to underestimate peak amplitude, while some sensors tend to be more prone to signal distortion than others. Such noise characteristics constrain some strategies to mitigate sensors errors.

Mutation operators for feature‐oriented software product lines

SummaryMutation testing is an approach to assess the quality of test cases. Mutants are modified versions of a system that ideally compose faulty behaviour. Test cases for a system are effective if they kill these mutants. For software product lines, several works have addressed mutation testing to inject variability faults, which may only exist in some variants. These works focus on variability models or specific implementation techniques. In contrast, feature‐oriented programming has been rarely investigated, wherefore, we (1) derive corresponding mutation operators, (2) investigate the feasibility of our proposed and conventional operators on 4 software product lines, and (3) discuss open challenges in mutation testing of software product lines. The results show that our proposed operators are suitable to cause variability faults and extend the capabilities of conventional operators. Nonetheless, mutation testing of software product lines is comparably expensive, due to a high number of variants and mutants—resulting in equivalence and redundancy.

Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development

While substantial effort has been invested in making robots more reliable, experience demonstrates that robots operating in unstructured environments are often challenged by frequent failures. Despite this, robots have not yet reached a level of design that allows effective management of faulty or unexpected behavior by untrained users. To understand why this may be the case, an in-depth literature review was done to explore when people perceive and resolve robot failures, how robots communicate failure, how failures influence people's perceptions and feelings toward robots, and how these effects can be mitigated. Fifty-two studies were identified relating to communicating failures and their causes, the influence of failures on human-robot interaction (HRI), and mitigating failures. Since little research has been done on these topics within the HRI community, insights from the fields of human computer interaction (HCI), human factors engineering, cognitive engineering and experimental psychology are presented and discussed. Based on the literature, we developed a model of information processing for robotic failures (Robot Failure Human Information Processing, RF-HIP), that guides the discussion of our findings. The model describes the way people perceive, process, and act on failures in human robot interaction. The model includes three main parts: (1) communicating failures, (2) perception and comprehension of failures, and (3) solving failures. Each part contains several stages, all influenced by contextual considerations and mitigation strategies. Several gaps in the literature have become evident as a result of this evaluation. More focus has been given to technical failures than interaction failures. Few studies focused on human errors, on communicating failures, or the cognitive, psychological, and social determinants that impact the design of mitigation strategies. By providing the stages of human information processing, RF-HIP can be used as a tool to promote the development of user-centered failure-handling strategies for HRIs.

Online fault detection system for proportional hydraulic valves

This paper presents the development of a method for condition monitoring and online fault detection on proportional directional valves. The systems that such valves are part of might be sensitive for unexpected maintenance or long duration stops. Consequently, the implementation of a fault detection and monitoring system can reduce maintenance costs and increase safety. The method is based on monitoring both the valve supply current and spool position related to the spool positioning control signal. Therefore, it is applicable for valves with embedded electronics including spool position measurement and internal controller. The supply current and spool position behavior depends on the friction, flow forces, solenoid current, and valve closed loop controller performance. Furthermore, valve static and dynamic characteristics are influenced by the spool size, overlapping and manufacturing tolerances. The effectiveness of the method to monitor and detect faults in valves with different sizes and constructive parameters is shown experimentally using five different proportional valves. The proposed method requires reference parameters characterizing the valve operation without faults. Standard tests are proposed to determine healthy valve parameters. For the method valuation and validation, experimental results with the valve operating under healthy conditions and with induced faults were compared. Faults were added in a way to represent spool locking and increase of friction forces between the spool and sleeve. The obtained results show the capability the method for the detection of faults classified as severe even if the valve controller attempts to compensate the faulty behavior.

WNBLI - A Multifarious Liability Examination for Wireless Sensor Networks

Wireless communications has completely revolutionized the living environment, just as personal computers forever altered the way of working in the 1980’s and the Internet dramatically changed the obtainment and accessing way of information in the 1990’s. Wireless sensor networks are evolving to a new level. The Failure liability is a very prominent problem in Wireless Sensor Network (WSN) research. Failure liable nodes behave in different form and this faulty behavior is referred to as multifarious faults or heterogeneous faults in WSN. This paper presents a Multifarious Failure liability Investigation protocol called Wireless Nodal Behavior and Liability Investigation (WNBLI) protocol for wireless sensor networks. The proposed WNBLI protocol consists of three vital phases, such as Association phase, Apprehension phase and Assortment phase. The performance of the proposed Wireless Nodal Behavior and Liability Investigation protocol is evaluated using network simulator.

Data–Driven Techniques for the Fault Diagnosis of a Wind Turbine Benchmark

Abstract This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.

Automatic fault detection for Building Integrated Photovoltaic (BIPV) systems using time series methods

PurposeFaults in the actual outdoor performance of Building Integrated Photovoltaic (BIPV) systems can go unnoticed for several months since the energy productions are subject to significant variations that could mask faulty behaviors. Even large BIPV energy deficits could be hard to detect. The purpose of this paper is to develop a cost-effective approach to automatically detect faults in the energy productions of BIPV systems using historical BIPV energy productions as the only source of information that is typically collected in all BIPV systems.Design/methodology/approachEnergy productions of BIPV systems are time series in nature. Therefore, time series methods are used to automatically detect two categories of faults (outliers and structure changes) in the monthly energy productions of BIPV systems. The research methodology consists of the automatic detection of outliers in energy productions, and automatic detection of structure changes in energy productions.FindingsThe proposed approach is applied to detect faults in the monthly energy productions of 89 BIPV systems. The results confirm that outliers and structure changes can be automatically detected in the monthly energy productions of BIPV systems using time series methods in presence of short-term variations, monthly seasonality, and long-term degradation in performance.Originality/valueUnlike existing methods, the proposed approach does not require performance ratio calculation, operating condition data, such as solar irradiation, or the output of neighboring BIPV systems. It only uses the historical information about the BIPV energy productions to distinguish between faults and other time series properties including seasonality, short-term variations, and degradation trends.

A Novel Approach for Brushless DC Motors Characterization in Drones Based on Chaos

A novel technique named Signal Analysis based on Chaos using Density of Maxima (SAC-DM) is presented to analyze Brushless Direct Current (BLDC) motors behavior. These motors are vastly used in electric vehicles, especially in Drones. The proposed approach is compared with the traditional Fast-Fourier Transform (FFT) and the experiments analyzing a BLDC motor of a drone demonstrates similar results but computationally simpler than that. The main contribution of this technique is the possibility to analyze signals in time domain, instead of the frequency domain. It is possible to identify working and faulty behavior with less computational resources than the traditional approach.