Unanticipated Faults Research Articles

Unanticipated Fault Diagnosis Method of Telescope Drive System Based on Latent Variable Mining

A Latent Variable Mining strategy for unanticipated state diagnosis in a telescope drive system has been proposed in this study. Compared with the existing analysis methods of explicit variables commonly used in fault diagnosis, the latent variable mining method proposed in this paper has the advantage of mining the deep hidden information of the system. First, a strategy for extracting latent variables with Stable Kernels Representation as the detection statistics is established in this paper. Then, principal component regression is used to construct an unanticipated fault diagnose strategy. Finally, an experimental platform for unanticipated load variation of the telescope drive system is established in the article, and the method proposed in this article is used to perform diagnostic tests on it. The test results show that for the unanticipated faults that cannot be correctly identified by the explicit variable analysis, the latent variable analysis method can clearly distinguish them to achieve the purpose of unanticipated fault diagnosis. All of the above show a good performance of the proposed unanticipated diagnosis strategy based on Latent Variable Mining with telescope drive system.

An Optimization-Based Reliability Enhancement Scheme for Active Distribution Systems Utilizing Electric Vehicles

Unanticipated faults can hinder the operation of the utility grid, consumers, and businesses. This study aims to ensure the continued power supply to the consumers during outages and peak hours from the stored energy in the batteries of electric vehicles (EVs). Considering the bi-directional charging feature, the energy capacity model of the EV-batteries in a centralized parking lot (PL) and distributed EVs is developed. Various factors that affect the energy capacity of EVs are considered and modeled. The optimal location of PL and charging/discharging time and rate are found for reliability improvement, losses reduction, and voltage regulation of the distribution system. The problem is formulated as a mixed-integer multi-objective optimization problem. The first objective function is formulated to maximize the energy output from the EVs-batteries during blackouts or to minimize the energy not supplied (ENS) to the consumers, whereas the second objective function minimizes the losses of the distribution system. Moreover, an IEEE 37 Node-Test Feeder and seven test cases are considered for this study. Simulation results show that PL of EVs could be employed as a power source during the islanded and grid-connected mode for improvement of reliability in the power systems.

Unanticipated Fault Detection Technology for Tip/Tilt Mirror Control System of Large Aperture Telescope

Large Aperture telescopes are usually placed in some special environment. Once a malfunction occurs, it’s inconvenient for maintenance. This paper presents those unanticipated failures in tip/tilt mirror control system of large aperture telescopes, and tries to propose a detection strategy based on BP neural network. Firstly, the paper analyses the failure mechanism of the software and the hardware of the tip/tilt mirror control system. Then the known fault data are used to train the BP neural network in order to obtain an accurate detection model. Finally, the unanticipated data are mixed into the known fault data, and the model is used to detect unanticipated faults. The simulation results show that the model can be used to detect the unanticipated fault in tip/tilt mirror control system effectively.

Design and tests of a super real‐time simulation‐based power system real‐time decision‐making emergency control system

In many China's enterprise-owned power grids (EPGs), the frequently changing operation state and the poor reliability of some outdated facilities may lead to inaccuracy or even malfunctions of the traditional pre-decision-making emergency control systems, which have already caused several blackouts in EPGs. To solve this problem, a real-time decision-making emergency control system based on the detailed time-domain transient stability simulation is designed. Based on the trajectory characteristics of the stability restoration process of EPGs, the stability restoration criteria are proposed to significantly shorten the simulation time needed to judge the elimination of a certain stability problem. Fast decision-making algorithms for frequency instability, rotor angle instability and branch overload with relatively low computational cost are introduced. Control strategies can be determined within a specific time after faults are detected. The corresponding countermeasures are directly executed when the strategy is obtained. The system configuration is designed to be capable of dealing with complicated and unanticipated faults, such as cascading outages and unexpected grid splitting. The test results of a 31-node practical EPG show that the proposed algorithms and the system configuration are practical and efficient. This work is a meaningful exploration of the real-time decision-making emergency control system.

Preserving stabilization while practically bounding state space using incorruptible partially synchronized clocks

Stabilization is a key dependability property for dealing with unanticipated transient faults, as it guarantees that even in the presence of such faults, the system will recover to states where it satisfies its specification. One of the desirable attributes of stabilization is the use of bounded space for each variable. In this paper, we present an algorithm that transforms a stabilizing program that uses variables with unbounded domain into a stabilizing program that uses bounded variables by using partially synchronized physical time. Specifically, our algorithm relies on bounded clock drift $$\epsilon $$ among processes and message delivery that either delivers the message in time $$\delta $$ or loses it. If we let $$\epsilon $$ to be as much as 100 s and $$\delta $$ to be as much as 1 h, this property is satisfied by any practical system. While non-stabilizing programs (that do not handle transient faults) can deal with unbounded variables by assigning large enough but bounded space, stabilizing programs—that need to deal with arbitrary transient faults—cannot do the same since a transient fault may corrupt the variable to its maximum value. We show that our transformation algorithm is applicable to several problems including logical clocks, vector clocks, mutual exclusion, diffusing computations, and so on. Moreover, our approach can also be used to bound counters used in an earlier work by Katz and Perry for adding stabilization to a non-stabilizing program. By combining our algorithm with that work by Katz and Perry and by assuming incorruptible partially synchronized clocks, it would be possible to provide stabilization for a rich class of problems, by assigning large enough but bounded space for variables.

Detection of unanticipated faults for autonomous underwater vehicles using online topic models

AbstractFor robots to succeed in complex missions, they must be reliable in the face of subsystem failures and environmental challenges. In this paper, we focus on autonomous underwater vehicle (AUV) autonomy as it pertains to self‐perception and health monitoring, and we argue that automatic classification of state‐sensor data represents an important enabling capability. We apply an online Bayesian nonparametric topic modeling technique to AUV sensor data in order to automatically characterize its performance patterns, then demonstrate how in combination with operator‐supplied semantic labels these patterns can be used for fault detection and diagnosis by means of a nearest‐neighbor classifier. The method is evaluated using data collected by the Monterey Bay Aquarium Research Institute's Tethys long‐range AUV in three separate field deployments. Our results show that the proposed method is able to accurately identify and characterize patterns that correspond to various states of the AUV, and classify faults at a high rate of correct detection with a very low false detection rate.

Model-based fault-tolerant control with robustness to unanticipated faults

Fault-tolerant control is important for the autonomous operation of complex processes. When model predictive control is used, fault detection and diagnosis is often based on the available process model used by the controller. Unanticipated faults can however cause misdiagnosis of faults, and consequently incorrect compensation actions. A fault-tolerant model predictive controller is presented in this article and tested on a grinding mill circuit simulator. The fault diagnosis algorithm quickly and accurately detects anticipated faults based on the generalized likelihood ratio test. Unanticipated faults are isolated when the process data do not sufficiently match the most probable anticipated fault data. The scheme is applicable to nonlinear multiple-input multiple-output systems.

Identification of Cable Faults Onboard Ship using Power Line Communication

The integrity of an electrical cable is vital for the reliability and safety of a system. The change in operating conditions of the electrical cable often leads to unanticipated faults or premature ageing and any such defects has to be monitored so that corrective measures can be taken within the time limit. This is relevant particularly in the case of ships since they cannot afford discontinuity in power supply. In this paper, an experimental setup is developed to create and monitor the intermittent faults in ship electrical system using power line communication. The advantage of this technique is its non-intrusive nature in monitoring the faults.

Fault-Tolerant Strategy for Real-Time System Based on Evolvable Hardware

The evolvable hardware (EHW) is widely used in the design of fault-tolerant system. Fault-tolerant system is really a real-time system, and the recovery time is necessary in fault detection and recovery. However, when applying EHW, real-time characteristic is usually ignored. In this paper, a fault-tolerant strategy based on EHW is proposed. The recovery time, predicted by the fault tree analysis (FTA), is considered as a constraint condition. A configuration library is set up in the design phase to accelerate the repair process of the anticipated faults. An evolvable algorithm (EA) based on similarity is applied to evolve the repair circuit for the unanticipated faults. When the library reaches the upper, the target system is reconfigured by the EA-repair technology. Extensive experiments are conducted to show that our method can improve the fault-tolerance of the system while satisfying the real-time requirement on FPGA platform. In a long run system, our method can keep a higher fault recovery rate.

Safety performance monitoring of autonomous marine systems

The marine environment is vast, harsh, and challenging. Unanticipated faults and events might lead to loss of vessels, transported goods, collected scientific data, and business reputation. Hence, systems have to be in place that monitor the safety performance of operation and indicate if it drifts into an intolerable safety level. This article proposes a process for developing safety indicators for the operation of autonomous marine systems (AMS). The condition of safety barriers and resilience engineering form the basis for the development of safety indicators, synthesizing and further adjusting the dual assurance and the resilience based early warning indicator (REWI) approaches. The article locates the process for developing safety indicators in the system life cycle emphasizing a timely implementation of the safety indicators. The resulting safety indicators reflect safety in AMS operation and can assist in planning of operations, in daily operational decision-making, and identification of improvements. Operation of an autonomous underwater vehicle (AUV) exemplifies the process for developing safety indicators and their implementation. The case study shows that the proposed process leads to a comprehensive set of safety indicators. It is expected that application of the resulting safety indicators consequently will contribute to safer operation of current and future AMS.

Controller Integrity Monitoring in Adaptive Learning Systems Towards Trusted Autonomy

This paper presents a controller integrity monitoring method for a class of second-order nonlinear uncertain systems incorporating neural network-based adaptive control algorithms. The adaptive neural network model is employed to ensure robust tracking performance in the presence of certain modeling uncertainty under consideration during the offline controller design process. Based on Lyapunov stability analysis, an online controller integrity monitoring scheme is developed to detect the occurrence of controller software/algorithm faults and unanticipated physical component faults, which may lead to unstable learning behaviors and malfunctions of the adaptive controller. Adaptive thresholds for detecting controller malfunctions are derived, ensuring the robustness with respect to modeling uncertainty and neural network approximation error. Additionally, the detectability conditions are investigated, characterizing the class of detectable software faults and unanticipated physical faults. An upper bound on the fault detection time is also established. Simulation results are shown to illustrate the effectiveness of the proposed method.

Architectural Approaches for Self-Healing Systems Based on Multi Agent Technologies

Self-healing systems are able to adapt themselves at runtime time in response to changing environmental or operational circumstances, shifting user requirements, and unanticipated faults without human intervention. Conceptually, a self-managing system is composed of four key capabilities; Monitoring, performing Analysis, Planning and Executing the plan. The preferred way to enable repair in a self-healing system is to use externalized repair/adaptation architecture. Adaptability, dynamicity, awareness, observability, autonomy, robustness, distributability, mobility and traceability are requirements that an architecture style for self-healing system should satisfy. In this paper we discuss Multi agent based self-healing system has a characteristics that can satisfy mentioned requirement. We define associations between architecture style requirements for self-healing system and MAS characteristics. As a case study in a real project we have designed Automated Teller Machine (ATM) combination with biometric sensors based on multi-agent architecture. DOI: http://dx.doi.org/10.11591/ijece.v3i6.3952

Software health management with Bayesian networks

Software health management (SWHM) is an emerging field which addresses the critical need to detect, diagnose, predict, and mitigate adverse events due to software faults and failures. These faults could arise for numerous reasons including coding errors, unanticipated faults or failures in hardware, or problematic interactions with the external environment. This paper demonstrates a novel approach to software health management based on a rigorous Bayesian formulation that monitors the behavior of software and operating system, performs probabilistic diagnosis, and provides information about the most likely root causes of a failure or software problem. Translation of the Bayesian network model into an efficient data structure, an arithmetic circuit, makes it possible to perform SWHM on resource-restricted embedded computing platforms as found in aircraft, unmanned aircraft, or satellites. SWHM is especially important for safety critical systems such as aircraft control systems. In this paper, we demonstrate our Bayesian SWHM system on three realistic scenarios from an aircraft control system: (1) aircraft file-system based faults, (2) signal handling faults, and (3) navigation faults due to inertial measurement unit (IMU) failure or compromised Global Positioning System (GPS) integrity. We show that the method successfully detects and diagnoses faults in these scenarios. We also discuss the importance of verification and validation of SWHM systems.

Fault-tolerant sliding mode control for the interferometer system under the unanticipated faults

The important precision control of the interferometer suffers from various faults in the complex space environment. However, the studies for the fault-tolerant control (FTC) in the interferometer are few. This paper first presents an active FTC scheme for the interferometer system in the presence of micro-gravity, micro-vibration and thermal disturbances in space. Kinds of unanticipated faults are quickly detected by the residue detection. Neural network is used to estimate the unanticipated faults online. The sliding mode control with a reaching law is designed for the normal control with a fixed upper bound and for the FTC with an adaptive upper bound adjusted by the neural network to recover the system performance under the faults. Stability analysis is performed by the Lyapunov theorem. Numerical simulations are used to verify the significant improved performance of the proposed FTC scheme.

Intelligent fault-tolerant control for swing-arm system in the space-borne spectrograph

Fault-tolerant control (FTC) for the space-borne equipments is very important in the engineering design. This paper presents a two-layer intelligent FTC approach to handle the speed stability problem in the swing-arm system suffering from various faults in space. This approach provides the reliable FTC at the performance level, and improves the control flow error detection capability at the code level. The faults degrading the system performance are detected by the performance-based fault detection mechanism. The detected faults are categorized as the anticipated faults and unanticipated faults by the fault bank. Neural network is used as an on-line estimator to approximate the unanticipated faults. The compensation control and intelligent integral sliding mode control are employed to accommodate two types of faults at the performance level, respectively. To guarantee the reliability of the FTC at the code level, the key parts of the program codes are modified by control flow checking by software signatures (CFCSS) to detect the control flow errors caused by the single event upset. Meanwhile, some of the undetected control flow errors can be detected by the FTC at the performance level. The FTC for the anticipated fault and unanticipated fault are verified in Synopsys Saber, and the detection of control flow error is tested in the DSP controller. Simulation results demonstrate the efficiency of the novel FTC approach.

Fault-Tolerant Subspace Predictive Control Applied to a Boeing 747 Model

This paper presents a fault-tolerant control system based on a combination of predictive control and subspace identification called subspace predictive control. In this control method, the mathematical model used by conventional predictive controllers to predict the future output is replaced by a subspace predictor. Because the subspace predictor is continuously updated in a closed-loop setting based on new input-output data, it can naturally adapt the controller after a fault has occurred. This property is very useful for fault-tolerant control because faults might be unanticipated. A novel feature of the presented subspace predictive control algorithm is that the predictor is recursively updated in a computationally efficient way. A multiple-model fault classification scheme is used to distinguish between anticipated and unanticipated faults. For anticipated faults the control system is configured such that it can accommodate these faults more quickly. The proposed fault-tolerant control system is evaluated on a detailed model of a Boeing 747.

Intelligent on-line fault tolerant control for unanticipated catastrophic failures

As dynamic systems become increasingly complex, experience rapidly changing environments, and encounter a greater variety of unexpected component failures, solving the control problems of such systems is a grand challenge for control engineers. Traditional control design techniques are not adequate to cope with these systems, which may suffer from unanticipated dynamic failures. In this research work, we investigate the on-line fault tolerant control problem and propose an intelligent on-line control strategy to handle the desired trajectories tracking problem for systems suffering from various unanticipated catastrophic faults. Through theoretical analysis, the sufficient condition of system stability has been derived and two different on-line control laws have been developed. The approach of the proposed intelligent control strategy is to continuously monitor the system performance and identify what the system's current state is by using a fault detection method based upon our best knowledge of the nominal system and nominal controller. Once a fault is detected, the proposed intelligent controller will adjust its control signal to compensate for the unknown system failure dynamics by using an artificial neural network as an on-line estimator to approximate the unexpected and unknown failure dynamics. The first control law is derived directly from the Lyapunov stability theory, while the second control law is derived based upon the discrete-time sliding mode control technique. Both control laws have been implemented in a variety of failure scenarios to validate the proposed intelligent control scheme. The simulation results, including a three-tank benchmark problem, comply with theoretical analysis and demonstrate a significant improvement in trajectory following performance based upon the proposed intelligent control strategy.

PRODIAG: A Process-Independent Transient Diagnostic System—II: Validation Tests

The unique capabilities of the first-principles-based PRODIAG diagnostic system to identify unanticipated process component faults and to be ported across different processes/plants through modification of only input data files are demonstrated in two validation tests. The Braidwood Nuclear Power Plant full-scope operator training simulator is used to generate transient data for two plant systems used in the validation tests. The first test consists of a blind test performed with 39 simulated transients of 20 distinct types in the Braidwood chemical and volume control system. Of the 39 transients, 37 are correctly identified with varying precision within the first 40 s into the transient while the remaining two transients are not identified. The second validation test consists of a double-blind test performed with 14 simulated transients in the Braidwood component coolant water system. In addition to having no prior knowledge of the identity of the transients, in the double-blind test we also had no prior information regarding the identity of the component faults that the simulator was capable of modeling. All 14 transient events are correctly identified with varying precision within the first 30 s into the transient. The test results provide enough evidence to successfully confirm the unique capabilities of the plant-level PRODIAG diagnostic system.

PRODIAG: A Process-Independent Transient Diagnostic System—I: Theoretical Concepts

A novel first-principles-based diagnostic system called PRODIAG is proposed for on-line detection and identification of faulty components during incipient off-normal process conditions. The concepts of qualitative physics reasoning and function-oriented diagnostics are employed in the design of PRODIAG and result in two unique capabilities not found in other plant-level diagnostic systems. First, PRODIAG is fully portable as it requires only modification of the input files containing the appropriate process schematics information to be able to diagnose single-component failures in different processes/plants. Second, PRODIAG detects unanticipated faults. Hence, it does not require the prespecification and formulation of rules to cover every conceivable fault scenario, and unlike traditional approaches, it is not likely to misdiagnose unforeseen events. PRODIAG’s approach is to map process symptoms into component faults through a three-step mapping procedure with a knowledge base containing three distinct types of information: qualitative macroscopic balance equation rules, functional classification of process components, and the process piping and instrumentation diagram. The concepts introduced in the proposed diagnostic system are described, and an illustrative example shows how they are used in plant-level diagnostics.

Using learning techniques to accommodate unanticipated faults

Procedures exist to rapidly accommodate certain types of faults, based on a priori specification of the postfault dynamics. A methodology is presented for accommodating the remaining unanticipated faults. For these two approaches, a tradeoff exists between the time to attain a solution to the reconfiguration problem and the generality of the approach. Unanticipated faults are represented as unmodeled forces and torques. Models of these forces and torques are developed online using a hybrid estimation/learning approach. The hybrid system is designed for fast estimation during the initial transient when a fault occurs, with continually improving performance as postfault information is accumulated by the learning system. Fault accommodation is achieved by a feedforward/feedback control architecture that employs an actuator distribution system to convert desired forces into individual actuator commands. This approach is demonstrated on a simulated autonomous vehicle, where the addition of a hybrid estimation/learning capability is shown to increase performance greatly over time. >