Guarantee System Safety Research Articles

A dynamic Bayesian network based reliability assessment method for short-term multi-round situation awareness considering round dependencies

During abnormal situations, the perception, comprehension, and projection of elements related to abnormality, i.e., situation awareness (SA), is one of the critical factors to guarantee system safety, and dynamically achieved in short-term multi-round interactions. The short-term multi-round SA (STMR-SA) experiences round dependencies due to anchoring effect (AE) and confirmation bias (CB). For SA in current round, AE biases judgement of system states towards previous cognition, and CB leads to neglect of information disproving previous opinion. Therefore, AE and CB mutually promote and impede the correction of STMR-SA errors. The effects of AE and CB on STMR-SA have been verified in qualitative research but disregarded in quantitative STMR-SA reliability assessments. This paper aims to propose a novel STMR-SA reliability assessment method considering AE-caused and CB-caused round dependencies. First, a round dependency model (RDM) is developed to quantify above-mentioned round dependencies. Subsequently, STMR-SA evolution is modeled with dynamic Bayesian network, where round dependencies are represented by connections between adjacent time slices and quantified by RDM. Case study on Boeing 737-8 (MAX) accident demonstrates the effectiveness of this method. Results indicates that improvement measures including adding angle of attack (AOA) disagree warning and training enhancement, could improve the STMR-SA reliability and system safety.

Runtime System Support for CPS Software Rejuvenation

Software rejuvenation, which was originally introduced to deal with performance degradation due to software aging, has recently been proposed as a mechanism to provide protection against run-time cyber attacks in cyber-physical systems (CPSs). Experiments have demonstrated that CPSs can be protected from attacks that corrupt run-time code and data by periodically restoring the run-time system with an uncorrupted image. Control theoretic and empirical methods have been developed to determine the timing and mode-switching conditions for CPS software rejuvenation (CPS SR) that will guarantee system safety. This paper presents the requirements that need to be met by the run-time system to support CPS SR. It also presents an implementation and demonstration of the run-time system for the PX4 autopilot system for autonomous vehicles.

Safe Decision Controller for Autonomous DrivingBased on Deep Reinforcement Learning inNondeterministic Environment.

Autonomous driving systems are crucial complicated cyber-physical systems that combine physical environment awareness with cognitive computing. Deep reinforcement learning is currently commonly used in the decision-making of such systems. However, black-box-based deep reinforcement learning systems do not guarantee system safety and the interpretability of the reward-function settings in the face of complex environments and the influence of uncontrolled uncertainties. Therefore, a formal security reinforcement learning method is proposed. First, we propose an environmental modeling approach based on the influence of nondeterministic environmental factors, which enables the precise quantification of environmental issues. Second, we use the environment model to formalize the reward machine's structure, which is used to guide the reward-function setting in reinforcement learning. Third, we generate a control barrier function to ensure a safer state behavior policy for reinforcement learning. Finally, we verify the method's effectiveness in intelligent driving using overtaking and lane-changing scenarios.

Optimal Safety-Critical Control of Epidemics

We present a generalized model for epidemic processes that partitions control into changes in linear and non-linear flow rates between compartments, respectively. We then define an optimal control problem that minimizes the weighted cost of rate control on the generalized model while maintaining conditions that guarantee system safety at any time using control barrier functions. Using this formulation, we prove that under homogeneous penalties the optimal controller will always favor increasing the linear flow out of an infectious process over reducing nonlinear flow in. Further, in the case of heterogeneous penalties, we provide necessary and sufficient conditions under which the optimal controller will set control of non-linear rates (i.e., the reduction of flow rate into the infection process) to zero. We then illustrate these results through the simulation of a bi-virus SEIQRS model.

Non-Cascaded Model-Free Predictive Speed Control of SMPMSM Drive System

The cascaded structure is generally adopted in the permanent magnet synchronous motor drive system, however, the speed dynamic performance is always deteriorated due to the limited bandwidth of speed loop when the linear controller is employed. Therefore, in order to realize quick dynamic response, the speed loop is eliminated in the non-cascaded control structure, and the non-cascaded model-free predictive speed control is proposed in this paper. First, the ultra-local model of SMPMSM drive system is established with a compound variable which implicitly unifies the speed and current of different time scales, and then the Lyapunov function is designed to derive control law and generate the inverter reference voltage automatically by considering the delay of digital control. Moreover, the inverter reference voltage is further modified under voltage and current constraints to guarantee system safety and reliable operation. Finally, the feasibility and validity of the proposed method has been verified by system simulation and experimental researches.

Decentralized Control Synthesis for Air Traffic Management in Urban Air Mobility

Urban air mobility (UAM) refers to air transportation services within an urban area, often in an on-demand fashion. We study air traffic management (ATM) for vehicles in a UAM fleet, while guaranteeing system safety requirements such as traffic separation. Existing ATM methods for unmanned aerial systems, such as UAS traffic management, utilize alternative approaches which do not provide strict safety guarantees. No established infrastructure exists for providing ATM at scale for UAM. We provide a decentralized, hierarchical approach for UAM ATM that allows for scalability to high traffic densities as well as providing theoretical guarantees of correctness with respect to user-provided safety specifications. Our main contributions are two-fold. First, we propose a novel UAM ATM architecture that divides the control authority between vertihubs that are each in charge of all UAM vehicles in their local airspace. Each vertihub also contains a number of vertiports that are in charge of UAM vehicle takeoffs and landings. The resulting architecture is decentralized and hierarchical, which not only enables scalability, but also robustness in the event of any individual vertihub or vertiport no longer being operational. Second, we provide a contract-based correct-by-construction reactive synthesis approach that provably guarantees safety properties with respect to user-provided specifications in linear temporal logic. We demonstrate the approach on large-volume UAM air traffic data.

Multiscale deep bidirectional gated recurrent neural networks based prognostic method for complex non-linear degradation systems

Reliable estimation of the remaining useful life (RUL) of complex engineered systems plays a vital role in avoiding undue maintenance situations while guaranteeing system safety. However, efficient tracking of RUL often gets hampered by the prerequisite for prior knowledge regarding degradation characteristics of critical components, which are not available in most cases. Additionally, machine learning techniques face difficulties in adapting and modeling degradation trends in the presence of equipment’s complex working environments. To address these issues, we present a novel data-driven feature learning approach based on a multi-scale deep bidirectional gated recurrent neural network (MDBGRU). The MDBGRU network can: i) automatically learns both local and global information in addition to temporal variations in the multivariate sensor data; ii) capture salient discriminative features characterizing the system complexity; iii) overcome the pre-expertise requirement on multiple sub-components of the system; and iv) curb shortcomings of machine learning methods. Extensive experiments are performed on the C-MAPSS dataset to evaluate the prognostic capability of the proposed method. Compared with the existing works, our network attains enhanced prediction accuracy with an overall improvement of 13.54%, suggesting this as a new and promising RUL estimation approach.

RISE-Based Asymptotic Prescribed Performance Tracking Control of Nonlinear Servo Mechanisms

Most function approximator (e.g., neural network or fuzzy system) based control designs can only prove uniform ultimate boundedness of the controlled system due to the unavoidable approximation errors. Moreover, the transient response of conventional adaptive control may be sluggish because high-gain learning is not preferable for guaranteeing system safety. To address these issues, this paper proposes and experimentally validates an alternative robust adaptive control for servo mechanisms with unknown dynamics and bounded disturbances. This control can guarantee asymptotic tracking error convergence in the steady-state, while the transient response can also be prescribed by using an improved prescribed performance function. An echo state network augmented by a smooth friction model is used to accommodate the unknown nonlinearities. The residual approximation error and other bounded disturbances are compensated by using a robust integral of sign of the error term. Comparative experiments based on a practical turntable servo mechanism are conducted to validate the effectiveness of the proposed control scheme and show improved control performance.

A hybrid feature model and deep learning based fault diagnosis for unmanned aerial vehicle sensors

Abstract Fault diagnosis plays an important role in guaranteeing system safety and reliability for unmanned aerial vehicles (UAVs). In this study, a hybrid feature model and deep learning based fault diagnosis for UAV sensors is proposed. The residual signals of different sensor faults, including global positioning system (GPS), inertial measurement unit (IMU), air data system (ADS), were collected. This paper used short time fourier transform (STFT) to transform the residual signal to the corresponding time-frequency map. Then, a convolutional neural network (CNN) was used to extract the feature of the map and the fault diagnosis of the UAV sensors was implemented. Finally, the performance of the proposed methodology is evaluated through flight experiments of the UAV. From the visualization, the sensor faults information can be extracted by CNN and the fault diagnosis logic between the residuals and the health status can be constructed successfully.

Multisensor Data-Fusion-Based Approach to Airspeed Measurement Fault Detection for Unmanned Aerial Vehicles

Fault detection (FD) plays an important role in guaranteeing system safety and reliability for unmanned aerial vehicles (UAVs). This paper focuses on developing an alternative approach to FD for airspeed sensor in UAVs by using data from gyros, accelerometers, global positioning system, and wind vanes. Based on the kinematics model of the UAV, an estimator is proposed to provide analytical redundancy using information from the above-mentioned sensors, which are commonly implemented on UAVs. This filter process is independent of the airspeed measurement and the aircraft dynamics model. Furthermore, we employ the observability rank criterion based on Lie derivatives and prove that the nonlinear system describing the airspeed kinematics is observable. The $\chi ^{2}$ test and cumulative sum detector are employed to detect the occurrence of airspeed measurement faults together. Finally, the performance of the proposed methodology has been evaluated through flight experiments of UAVs.

Fault detection and isolation for Unmanned Aerial Vehicle sensors by using extended PMI filter

Fault detection and isolation (FDI) plays an important role in guaranteeing system safety and reliability for unmanned aerial vehicles (UAVs). This paper focuses on developing a method for detecting UAV sensor faults by using existing sensors, such as pitot tube, gyro, accelerometer and wind angle sensor. We formulate the kinematics as a nonlinear state space system, which requires no dynamic information and thus is applicable to all aircraft. To illustrate the method, we investigate five fault-detection scenarios, namely, faulty pitot tube, angle-of-attack sensor, sideslip sensor, accelerometer and gyro, and design a FDI structure including five faulty sensors. Then, considering the unknown disturbance, the proportional and multiple integral (PMI) fault detection filter (FDF) is proposed for the state and input estimation. A structure including two residuals are employed to detect and isolate the faults of the proposed faulty sensors. Finally, the performance of the proposed methodology is evaluated through flight experiments of the UAV.

Integrated system health management-oriented maintenance decision-making for multi-state system based on data mining

ABSTRACTTo ensure a series of missions can be completed with only finite breaks, many systems are required to guarantee system safety and mission success. Of these, maintenance decision support is vital. One widely used maintenance strategy has been selective maintenance. Most traditional selective maintenance optimisation research has focused on binary state systems, which are subject to distribution deterioration or failure. However, a majority of systems used in aerospace or industrial applications are multi-state systems with more than two states deteriorating at the same time, meaning that real-time state distribution is needed to provide more timely and effective maintenance. This paper presents a novel integrated system health management-oriented maintenance decision support methodology and framework for a multi-state system based on data mining. An aero-engine system numerical example is given to illustrate the methodology, the results of which demonstrate the significant advantages of using data mining to efficiently obtain state distribution information, and the benefits of using a robust optimal model to choose suitable strategies. This methodology, which is applicable to multi-state systems of varying sizes, has the ability to solve maintenance problems when imperfect maintenance quality is considered.

A Framework for Trustworthiness Assessment based on Fidelity in Cyber and Physical Domains

We introduce a method for the assessment of trust for n-open systems based on a measurement of fidelity and present a prototypical implementation of a complaint architecture. We construct a MAPE loop which monitors the compliance between corresponding figures of interest in cyber- and physical domains; derive measures of the system's trustworthiness; and use them to plan and execute actions aiming at guaranteeing system safety and resilience. We conclude with a view on our future work.

Overload provisioning in mixed-criticality cyber-physical systems

Cyber-physical systems are an emerging class of applications that require tightly coupled interaction between the computational and physical worlds. These systems are typically realized using sensor/actuator interfaces connected with processing backbones. Safety is a primary concern in cyber-physical systems since the actuators directly influence the physical world. However, unexpected or unusual conditions in the physical world can manifest themselves as increased workload demands being offered to the computational infrastructure of a cyber-physical system. Guaranteeing system safety under overload conditions is therefore a prime concern in developing and deploying cyber-physical systems. In this work, we study this problem in the context of a radar surveillance system, where tasks have different levels of criticality or influence on system safety . In the face of overloads, we observe that the desirable property in such systems is that the more critical tasks continue to meet their timing requirements. We capture this mixed-criticality overload requirement using a formal overload-tolerance metric called ductility . Using this overload-tolerance metric, we first develop our solution in the context of uniprocessor systems, where we show that Zero-Slack scheduling (ZS) algorithms can be used to improve the overload behavior in mixed-criticality cyber-physical systems compared to existing fixed-priority scheduling algorithms like Rate-Monotonic Scheduling (RMS) and Criticality-As-Priority-Assignment (CAPA). Leveraging these results, we then develop a criticality-aware task allocation algorithm called Compress-on-Overload Packing (COP) for dealing with multiprocessor cyber-physical systems. Evaluation results show that COP achieves up to five times better ductility than traditional load balancing bin-packing algorithms like Worst-Fit Decreasing (WFD). Finally, we apply ZS and COP to the radar surveillance system to demonstrate the resulting improvement in system overload behavior. Our implementation of the Zero-Slack scheduler is available as a part of the Linux/RK project, which provides resource kernel extensions for Linux.

Psychology and Design Processes

Abstract. The most fundamental issue for any design is to pursue a strategy that guarantees that the final design product matches user expectations in terms of the product's usability, functionality, and requisite user competencies. This presentation illustrates this problem in reference to three separate but interrelated major themes: (1) the design of highly complex sociotechnical systems with high hazard potential; (2) the processes of everyday product design in teams; (3) the theoretical aspects of design and creative problem finding/solving. The first theme articulates the specific difficulties arising from the usual conflicts between purely technology-driven demands and the need to integrate the cognitive and action capacities, limitations, and needs of the human operator into the ultimate facility in order to guarantee system safety and reliability. What is called for here is more than a dialog between engineering sciences and psychology, but a genuine active cooperation of diverse disciplines in all design stages. The second theme addresses the social psychological processes of everyday product design under conditions of distributed decision making and the cooperative demands of diverse groups with heterogeneous professional socialization, divergent competencies, and diverse interests. The concluding section reflects on the theoretical dimensions of creativity and design. It discusses the research evidence concerning the social and organizational conditions under which innovative design solutions may be generated such as cognitive competencies and strategies, leadership, and organizational arrangements.