Error Injection Research Articles

Intermittent failure detection of multiple electrical connectors in EWIS

Electrical wiring interconnection system (EWIS) of civil aircraft has been paid more attention in recent years, and intermittent failure detection of electrical connectors in EWIS is a challenging problem. This paper presents a sliding mode observer (SMO) approach for the intermittent failure detection of an aircraft electrical system with multiple connector failures. The mathematical model of the aircraft electrical system which contains multiple connector failures is established for transforming the intermittent failure detection problem into observer-based multiplicative faults isolation and estimation problems. A set of adaptive sliding mode observers are designed to locate the failure connectors preliminarily, the observers can adapt the unknown upper bound of the faults. Furthermore, a fault-reconstruction scheme applying the equivalent output error injection principle is proposed for fault estimation, where the characteristic parameters of connecters are reconstructed to identify the failures. Finally, a numerical example is provided to show the effectiveness of the proposed method.

On the development of a sliding mode observer-based fault diagnosis scheme for a wind turbine benchmark model

This paper addresses the design of an observer-based fault diagnosis scheme, which is applied to some of the sensors and actuators of a wind turbine benchmark model. The methodology is based on a modified sliding mode observer (SMO) that allows accurate reconstruction of multiple sensor or actuator faults occurring simultaneously. The faults are reconstructed using the equivalent output error injection signal. A well-known validated wind turbine benchmark model, developed by Aalborg University and KK-electronic a/c, is utilized to evaluate the FDD scheme. Different sensors and actuator fault scenarios are simulated in the drive train, generator, and pitch & blade subsystems of the benchmark model, and attempts have been made to estimate these faults via the proposed modified SMO. The simulation results confirm the effectiveness of the proposed diagnosis scheme, and the faults are well detected, isolated, and reconstructed in the presence of the measurement noise.

A real-time multisensor fusion verification framework for advanced driver assistance systems

This paper presents a novel approach for the verification of multisensor data fusion algorithms in complex automotive sensor networks. Multisensor fusion plays a central role in enhancing the interpretation of traffic situations, facilitating inferences and decision making. It has therefore been instrumental in the ongoing innovation of Advanced Driver Assistance Systems (ADAS) which paves the way to autonomous driving. We introduce a real-time framework which can benchmark the performance of the fusion algorithms at the electronic system level using a Hardware-in-the-Loop (HiL) co-simulation. The presented research provides a quantitative approach for a trade-off between physical realism and computational efforts of the real-time synthetic simulation. The proposed framework illustrates a generic architecture of ADAS sensor error injection for robustness testing of the System under Test (SuT). We construct a lemniscate model for errors to find multivariate outliers with the Mahalanobis distance. A critical driving scenario considering road users in urban traffic describes the dynamic behaviour testability of the fusion algorithms. The industry-proven framework facilitates a functional verification of multisensor-fusion-based object detection precisely and more efficiently on the target electronic control unit (ECU) in the laboratory.

Robust fault reconstruction based on sliding mode observer for a class of uncertain time-varying delay systems: a rocket motor system application

In this paper, we present an extended sliding mode observer (SMO) design method for robust actuator fault reconstruction for a class of linear uncertain delayed systems. The uncertainty considered is matched and bounded. In meantime, the time-varying delay is unknown, bounded and affects simultaneously the input and the state of the system. Using the H1 concept and applying the equivalent output error injection idea from previous works in fault detection and isolation (FDI) scheme, the estimate actuator fault signal is designed with the proposed observer by minimising the uncertainty and the time-varying delay effects. Therefore, this problem is solved via a bounded real lemma (BRL) and the linear matrix inequalities (LMIs) optimisation. A rocket motor system is included to illustrate the efficiency of the proposed method. Even, in the presence of the uncertainty and the time-varying delay, the results of the simulation demonstrate that the proposed schemes can successfully estimate the system states and reconstruct the unknown actuator fault.

Descriptor reduced-order sliding mode observers design for switched systems with sensor and actuator faults

This paper investigates the state and fault estimation problem for linear continuous-time switched systems with simultaneous disturbances, sensor and actuator faults, and two types of observer approaches are presented to solve this design issue. The first one is a linear descriptor reduced-order observer, which generates the exact estimation of state and sensor faults together with disturbances by decoupling them from the state vector without any supplementary design. The second one refers to a descriptor sliding mode observer approach, where the state, disturbances, sensor and actuator faults can be reconstructed simultaneously. In the second method, the reachability of sliding surface is not necessary to be investigated, since the actuator faults can be estimated directly without using the equivalent output error injection scheme as in traditional sliding mode observers. Therefore, both developed observer approaches avoid the sliding surface switching problem of traditional sliding mode observers in application to switched systems. Finally, an example is given to demonstrate the effectiveness of the two designed observer methods.

Data driven CAN node reliability assessment for manufacturing system

The reliability of the Controller Area Network(CAN) is critical to the performance and safety of the system. However, direct bus-off time assessment tools are lacking in practice due to inaccessibility of the node information and the complexity of the node interactions upon errors. In order to measure the mean time to bus-off(MTTB) of all the nodes, a novel data driven node bus-off time assessment method for CAN network is proposed by directly using network error information. First, the corresponding network error event sequence for each node is constructed using multiple-layer network error information. Then, the generalized zero inflated Poisson process(GZIP) model is established for each node based on the error event sequence. Finally, the stochastic model is constructed to predict the MTTB of the node. The accelerated case studies with different error injection rates are conducted on a laboratory network to demonstrate the proposed method, where the network errors are generated by a computer controlled error injection system. Experiment results show that the MTTB of nodes predicted by the proposed method agree well with observations in the case studies. The proposed data driven node time to bus-off assessment method for CAN networks can successfully predict the MTTB of nodes by directly using network error event data.

Sensor Fault Detection, Isolation, and Estimation in Lithium-Ion Batteries

In battery management systems (BMSs), real-time diagnosis of sensor faults is critical for ensuring the safety and reliability of the battery. For example, a current sensor fault leads to erroneous estimates of state of charge and other parameters, which in turn affects the control actions in the BMS. A temperature sensor fault may lead to ineffective thermal management. In this brief, a model-based diagnostic scheme is presented that uses sliding mode observers designed based on the electrical and thermal dynamics of the battery. It is analytically shown how the extraction of the equivalent output error injection signals on the sliding manifolds enables the detection, the isolation, as well as the estimation of the temperature, voltage, and current sensor faults. This brief includes simulation and experimental studies to demonstrate and evaluate the effectiveness of the proposed scheme. Discussions are also included on the effects of uncertainty and on threshold design.

Robust Model-Based Fault Diagnosis for PEM Fuel Cell Air-Feed System

In this paper, the design of a nonlinear observer-based fault diagnosis approach for polymer electrolyte membrane (PEM) fuel cell air-feed systems is presented, taking into account a fault scenario of sudden air leak in the air supply manifold. Based on a simplified nonlinear model proposed in the literature, a modified super-twisting (ST) sliding mode algorithm is employed to the observer design. The proposed ST observer can estimate not only the system states, but also the fault signal. Then, the residual signal is computed online from comparisons between the oxygen excess ratio obtained from the system model and the observer system, respectively. Equivalent output error injection using the residual signal is able to reconstruct the fault signal, which is critical in both fuel cell control design and fault detection. Finally, the proposed observer-based fault diagnosis approach is implemented on the MATLAB/Simulink environment in order to verify its effectiveness and robustness in the presence of load variation.

Using instruction result locality and re-execution to mitigate silent data corruptions

In this paper, a method to mitigate silent data corruptions (SDCs) is proposed. This paper, first, shows and characterizes instruction result locality based on several simulation results and next, proposes an architecture called instruction value history table (VHT) to detect SDCs. In the case of fault detection, extra instruction redundant execution is utilized to assure fault existence. If outcome of the new redundant execution is different from that of previous one, a fault occurred, otherwise the first execution will be correct. In order to correct any detected faults, third redundant execution of the instruction is performed. Having three values from three redundant instruction executions, makes the correction of the fault feasible. The main advantage of this method is to detect any error which is not detectable by traditional protection codes like parity and SEC-DED. In other words, this method detects SDCs or any multiple faults which are not detectable by protection codes. Various soft error injections have been applied on Alpha processor for several PARSEC benchmarks. Experimental results show that the method can detect up to 70% of injected SDCs.

Electrochemical Model Based Fault Diagnosis of Lithium Ion Battery

A Multiple Model Adaptive Estimation (MMAE) based approach of fault diagnosis for Li-Ion battery is illustrated in this paper. Electrochemical modelling approach is integrated with MMAE for fault diagnosis. This real physics based model of Li-ion battery (with Li-Co-O2 cathode chemistry) with nominal model parameters is considered as the healthy battery model. Battery fault conditions such as aging, overcharge and over discharge causes significant variations of parameters from nominal values and can be considered as separate models. Output error injection based Partial Differential Algebraic Equation (PDAE) observers are used to generate the residual voltage signals. These residuals are then used in MMAE algorithm to detect the ongoing fault conditions of the battery. Simulation results show that the fault conditions can be detected and identified accurately which indicates the effectiveness of the proposed battery fault detection method.

High Performance Low Power Pulse-Clocked TMR Circuits for Soft-Error Hardness

The use of pulse-clocked latches has become ubiquitous in commercial unhardened integrated circuits (ICs) both for their performance and power benefits. In this paper, their use in soft-error hardened triple modular redundant (TMR) circuits is presented. The proposed multi-bit, self-correcting, TMR pulse-clocked latch macro provides a low power, high-speed design with high soft-error immunity. The macro includes test modes for delay testing of both individual and TMR copies with minimal area overhead, as well as a non-redundant operating mode. A physical design flow provides spatial separation of redundant logic copies to avoid upsets due to collection in multiple domains. A TMR, 128-bit data, 256-bit key, advanced encryption standard (AES) is fabricated on a 90-nm foundry low-standby power (LSP) process and its hardness verified using error injection simulations and proton beam testing.

An approximate model for pulsar navigation simulation

This paper presents an approximate model for the simulation of pulsar aided navigation systems. High fidelity simulations of these systems are computationally intensive and impractical for simulating periods of a day or more. Simulation of yearlong missions is done by abstracting navigation errors as periodic Gaussian noise injections. This paper presents an intermediary approximate model to simulate position errors for periods of several weeks, useful for building more accurate Gaussian error models. This is done by abstracting photon detection and binning, replacing it with a simple deterministic process. The approximate model enables faster computation of error injection models, allowing the error model to be inexpensively updated throughout a simulation. Testing of the approximate model revealed an optimistic performance prediction for non-millisecond pulsars with more accurate predictions for pulsars in the millisecond spectrum. This performance gap was attributed to noise which is not present in the approximate model but can be predicted and added to improve accuracy.

A Methodology to Emulate Single Event Upsets in Flip-Flops Using FPGAs through Partial Reconfiguration and Instrumentation

This paper presents a methodology to emulate Single Event Upsets (SEUs) in Field Programmable Gate Array (FPGA) flip-flops (FFs). Since the content of a FF is not modifiable through the FPGA configuration memory bits, a dedicated design is required for fault injection in the FFs. The method proposed in this paper is a hybrid approach that combines FPGA partial reconfiguration and extra logic added to the circuit under test, without modifying its operation. This approach has been integrated into a fault-injection platform, named NESSY (Non intrusive ErrorS injection SYstem), developed by our research group. Finally, this paper includes results on a Virtex-5 FPGA demonstrating the validity of the method on the ITC’99 benchmark set and a Feed-Forward Equalization (FFE) filter. In comparison with other approaches in the literature, this methodology reduces the resource consumption introduced to carry out the fault injection in FFs, at the cost of adding very little time overhead ( $1.6~\mu\hbox{s}$ per fault).

Nonlinear observer design for PEM fuel cell power systems via second order sliding mode technique

In this paper, a nonlinear observer is proposed for a PEMFC system, based on Second Order Sliding Mode (SOSM) techniques. The goal is to estimate the hydrogen partial pressure in the anode channel of the PEMFC, using the measurements of stack voltage, stack current, anode pressure and anode inlet pressure. The proposed observer employs a nonlinear error injection term, where the error is obtained from the difference between the system voltage output obtained from an experimental validated nonlinear model and estimated voltage output obtained from the designed observer. The robustness of this observer against parametric uncertainties and load variations is studied, and the finite time convergence property is proved via Lyapunov analysis. Simulation results illustrate the effectiveness and robustness of the proposed approach.

Adaptive Second-Order Sliding Mode Observer-Based Fault Reconstruction for PEM Fuel Cell Air-Feed System

This paper presents an observer-based fault reconstruction method for PEM fuel cells. This method extends the results of a class of nonlinear uncertain systems with Lipschitz nonlinearities. An adaptive-gain second-order sliding mode (SOSM) observer is developed for observing the system states, where the adaptive law estimates the uncertain parameters. The inherent equivalent output error injection feature of SOSM algorithm is then used to reconstruct the fault signal. The performance of the proposed observer is validated through a hardware-in-loop emulator. The experimental results illustrate the feasibility and effectiveness of the proposed approach for application to fuel cell air-feed systems.

Potential Errors and Test Assessment in Software Product Line Engineering

Software product lines (SPL) are a method for the development of variant-rich software systems. Compared to non-variable systems, testing SPLs is extensive due to an increasingly amount of possible products. Different approaches exist for testing SPLs, but there is less research for assessing the quality of these tests by means of error detection capability. Such test assessment is based on error injection into correct version of the system under test. However to our knowledge, potential errors in SPL engineering have never been systematically identified before. This article presents an overview over existing paradigms for specifying software product lines and the errors that can occur during the respective specification processes. For assessment of test quality, we leverage mutation testing techniques to SPL engineering and implement the identified errors as mutation operators. This allows us to run existing tests against defective products for the purpose of test assessment. From the results, we draw conclusions about the error-proneness of the surveyed SPL design paradigms and how quality of SPL tests can be improved.

VM-μCheckpoint: Design, Modeling, and Assessment of Lightweight In-Memory VM Checkpointing

Checkpointing and rollback techniques enhance reliability and availability of virtual machines and their hosted IT services. This paper proposes VM-μCheckpoint, a light-weight pure-software mechanism for high-frequency checkpointing and rapid recovery for VMs. Compared with existing techniques of VM checkpointing, VM-μCheckpoint tries to minimize checkpoint overhead and speed up recovery by means of copy-on-write, dirty-page prediction and in-place recovery, as well as saving incremental checkpoints in volatile memory. Moreover, VM-μCheckpoint deals with the issue that latency in error detection potentially results in corrupted checkpoints, particularly when checkpointing frequency is high. We also constructed Markov models to study the availability improvements provided by VM-μCheckpoint (from 99 to 99.98 percent on reasonably reliable hypervisors). We designed and implemented VM-μCheckpoint in the Xen VMM. The evaluation results demonstrate that VM-μCheckpoint incurs an average of 6.3 percent overhead (in terms of program execution time) for 50 ms checkpoint intervals when executing the SPEC CINT 2006 benchmark. Error injection experiments demonstrate that VM-μCheckpoint, combined with error detection techniques in RMK, provides high coverage of recovery.

Demonstrating HW–SW Transient Error Mitigation on the Single-Chip Cloud Computer Data Plane

Transient errors are a major concern for the correct operation of low-level cache memories. Aggressive integration requires effective mitigation of such errors, without extreme overheads in power, timing, or silicon area. We demonstrate a hybrid (hardware-software) scheme that mitigates bit flips in data that reside in low-level caches. The methodology is shown to be applicable in streaming applications and we illustrate that with a video decoding case study on a state-of-the-art many-core chip. The single-chip cloud computer is an experimental processor created by Intel Labs. Dedicated on-chip memories are utilized to keep safe copies for key application data, thus allowing rollbacks upon error detection. The experimental results illustrate the tradeoff between application delay, consumed energy, and output fidelity as the injected errors are corrected. When output fidelity is considered as a hard constraint, application slack used for mitigation can be reclaimed with dynamic frequency scaling. Output fidelity is guaranteed regardless of the error injection intensity and the application's timing constraints are respected up to a certain upper bound of error injection.

A New Spatial Transformation Scheme for Preventing Location Data Disclosure in Cloud Computing

Because much interest in spatial database for cloud computing has been attracted, studies on preserving location data privacy have been actively done. However, since the existing spatial transformation schemes are weak to a proximity attack, they cannot preserve the privacy of users who enjoy location-based services in the cloud computing. Therefore, a transformation scheme is required for providing a safe service to users. We, in this paper, propose a new transformation scheme based on a line symmetric transformation (LST). The proposed scheme performs both LST-based data distribution and error injection transformation for preventing a proximity attack effectively. Finally, we show from our performance analysis that the proposed scheme greatly reduces the success rate of the proximity attack while performing the spatial transformation in an efficient way.

DryLab® optimised two-dimensional high performance liquid chromatography for differentiation of ephedrine and pseudoephedrine based methamphetamine samples

In-silico optimised two-dimensional high performance liquid chromatographic (2D-HPLC) separations of a model methamphetamine seizure sample are described, where an excellent match between simulated and real separations was observed. Targeted separation of model compounds was completed with significantly reduced method development time. This separation was completed in the heart-cutting mode of 2D-HPLC where C18 columns were used in both dimensions taking advantage of the selectivity difference of methanol and acetonitrile as the mobile phases. This method development protocol is most significant when optimising the separation of chemically similar chemical compounds as it eliminates potentially hours of trial and error injections to identify the optimised experimental conditions. After only four screening injections the gradient profile for both 2D-HPLC dimensions could be optimised via simulations, ensuring the baseline resolution of diastereomers (ephedrine and pseudoephedrine) in 9.7min. Depending on which diastereomer is present the potential synthetic pathway can be categorised.