Search Results (9)

Ensuring the reliability and robustness of spacecraft systems remains a key challenge, particularly given the limited feasibility of continuous real-time monitoring during on-orbit operations. In the domain of Fault Detection, Isolation, and Recovery (FDIR), no universal strategy has yet emerged. Traditional approaches often rely on precise, model-based methods executed onboard. This study explores data-driven alternatives for self-diagnosis and fault detection using Machine Learning techniques, focusing on spacecraft Guidance, Navigation, and Control (GNC) subsystems. A high-fidelity functional engineering simulator is employed to generate realistic datasets from typical onboard signals, including sensor and actuator outputs. Fault scenarios are defined based on potential failures in these elements, guiding the data-driven feature extraction and labeling process. Supervised learning algorithms, including Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), are implemented and benchmarked against a simple threshold-based detection method. Comparative analysis across multiple failure conditions highlights the strengths and limitations of the proposed strategies. Results indicate that Machine Learning techniques are best applied not as replacements for classical methods, but as complementary tools that enhance robustness through higher-level self-diagnostic capabilities. This synergy enables more autonomous and reliable fault management in spacecraft systems. Full article

Accurate and stable positioning is significant for vehicle navigation systems, especially in complex urban environments. However, urban canyons and dynamic interference make vehicle sensors prone to disturbance, leading to vehicle positioning errors and even failures. To address these issues, an adaptive loosely coupled IMU/GNSS/LiDAR integrated navigation system based on factor graph optimization with sensor weight optimization and fault detection is proposed. First, the factor nodes and system framework are constructed based on error models of sensors, and the optimization method principle is derived. Second, the interactive multiple-model algorithm based on factor graph optimization (IMMFGO) is utilized to calculate and adjust sensor weights for global optimization, which will reduce the impact of disturbed sensors. Finally, a multi-stage fault detection, isolation, and recovery (MSFDIR) strategy is implemented based on the IMMFGO results and IMU pre-integration measurements, which can detect significant sensor faults and optimize the system structure. Vehicle experiments show that our IMMFGO method generally obtains better performance in positioning accuracy by 23.7% compared to adaptive factor graph optimization (AFGO) methods, and the MSFDIR strategy possesses the capability of fault sensor detection, which provides an essential reference for multi-source vehicle navigation systems in urban canyons. Full article

Leptospirosis is a neglected zoonotic disease that commonly affects cattle, pigs, horses, and dogs in many countries. Infection in dogs is usually subclinical, but acute cases of leptospirosis may occur along with systemic failure, which may become fatal. After recovery from an acute infection, dogs may become asymptomatic carriers and shed pathogenic leptospires through urine for long periods of time. Here, a study of ten different cases of leptospirosis is presented, showing the relevance of dogs as asymptomatic carriers of pathogenic Leptospira. The diagnosis was confirmed via isolation and further serological and genetic identification. Four Leptospira isolates (LOCaS28, 31, 34, and 46) were obtained from the kidneys and urine samples of 58 dogs destined for destruction (6.89%) at a Canine Control Center in Mexico City. No spirochetes were observed in the urine samples of those Leptospira-positive dogs examined under dark-field microscopy, and no clinical signs of disease were observed either. Six additional isolates were obtained: two came from asymptomatic carrier dogs (CEL60 and UADY22); another isolate came from an asymptomatic dog that was a pack companion of a clinically ill dog with fatal leptospirosis (AGFA24); and finally, three isolates were taken from dogs that died of leptospirosis (LOCaS59, Citlalli, and Nayar1). Nine out of the ten isolates were identified as being from the serogroup Canicola via cross-absorption MAT using reference strains and specific antisera, and their identity was genetically confirmed as Canicola ST34 via multi-locus sequencing typing (MLST). In contrast, the isolate Nayar1 was identified as serovar Copenhageni ST2. Interestingly, the asymptomatic dogs from which Leptospira isolates were recovered consistently showed high antibody titers in the microscopic agglutination test (MAT), revealing values of at least 1:3200 against serogroup Canicola and lower titer values against other serogroups. Isolates showed different virulence levels in the hamster model. Taken as a whole, all these findings confirmed that dogs may act as asymptomatic carriers of pathogenic leptospires and possibly spread them out to the environment, thus representing an active public health risk. The results also showed that the Canicola ST34 clone is the most prevalent Leptospira serovar in dogs in Mexico, and finally that the old-fashioned MAT is a good alternative for the detection of presumptive Leptospira asymptomatic carrier dogs. Full article

The main objective of this research work was to develop reliable and intelligent power sources for the future. To achieve this objective, a modular stand-alone solar energy-based direct current (DC) power supply was designed and implemented. The converter topology used is a two-stage interleaved boost converter, which is monitored in closed loop. The diagnosis method is based on analytic redundancy relations (ARRs) deduced from the bond graph (BG) model, which can be used to detect the failures of power switches, sensors, and discrete components such as the output capacitor. The proposed supervision scheme including a passive fault-tolerant cascade proportional integral sliding mode control (PI-SMC) for the two-stage boost converter connected to a solar panel is suitable for real applications. Most model-based diagnosis approaches for power converters typically deal with open circuit and short circuit faults, but the proposed method offers the advantage of detecting the failures of other vital components. Practical experiments on a newly designed and constructed prototype, along with simulations under PSIM software, confirm the efficiency of the control scheme and the successful recovery of a faulty stage by manual isolation. In future work, the automation of this reconfiguration task could be based on the successful simulation results of the diagnosis method. Full article

Small satellite platforms are experiencing increasing interest from the space community, because of the reduced cost and the performance available with current technologies. In particular, the hardware composing the attitude and orbit control system (AOCS) has reached a strong maturity level, and the dimensions of the components allow redundant sets of sensors and actuators. Thus, the software shall be capable of managing these redundancies with a fault tolerant structure. This paper presents an attitude and orbit determination system (AODS) architecture, with embedded failure detection and isolation functions, and autonomous redundant component management and reconfiguration for basic failure recovery. The system design and implementation has been sized for small satellite platforms, characterized by limited computing capacities, and reduced autonomy level. The discussion describes the system architecture, with particular emphasis on the failure detection and isolation blocks at the component level. The set of functions managing failure detection at system level is also described in the paper. The proposed system is capable of reconfiguring and autonomously recalibrating after various failures had occurred. Attention is also dedicated to the achieved performance, satisfying stringent requirements for a small satellite platform. In these regards, the simulation results used to verify the performance of the proposed system at the model-in-the-loop (MIL) level are also reported. Full article

Azole-resistant Aspergillus fumigatus is an emerging worldwide problem with increasing reports of therapy failure cases produced by resistant isolates. A case of azole-resistant A. fumigatus hospital colonization in a patient is reported here. Investigations of the hospital environment led to the recovery of A. fumigatus strains harboring the TR34/L98H and the G448S Cyp51A azole resistance mechanisms. Isolate genotyping showed that one strain from the environment was isogenic with the patient strains. These are the first environmental A. fumigatus azole resistant strains collected in a hospital in Spain; it supports the idea of the hospital environment as a source of dissemination and colonization/infection by azole resistant A. fumigatus in patients. The isolation of an azole-resistant strain from an azole-naïve patient is an interesting finding, suggesting that an effective analysis of clinical and environmental sources must be done to detect azole resistance in A. fumigatus. The emergence and spread of these resistance mechanisms in A. fumigatus is of major concern because it confers high resistance to voriconazole and is associated with treatment failure in patients with invasive aspergillosis. Full article

Bayesian Networks (BN) are probabilistic models that are commonly used for the diagnosis in numerous domains (medicine, finance, transport, robotics, …). In the case of autonomous vehicles, they can contribute to elaborate intelligent monitors that can take the environmental context into account. We show in this paper some main abilities of BN that can help in the elaboration of fault detection isolation and recovery (FDIR) modules. One of the main difficulty with the BN model is generally to elaborate these ones according to the case of study. Then, we propose some automatic generation techniques from failure mode and effects analysis (FMEA)-like tables using the pattern design approach. Once defined, these modules have to operate online for autonomous vehicles. In a second part, we propose a design methodology to embed the real-time and non-intrusive implementations of the BN modules using FPGA-SoC support. We show that the FPGA implementation can offer an interesting speed-up with very limited energy cost. Lastly, we show how these BN modules can be incorporated into the decision-making model for the mission planning of unmanned aerial vehicles (UAVs). We illustrate the integration by means of two models: the Decision Network model that is a straightforward extension of the BN model, and the BFM model that is an extension of the Markov Decision Process (MDP) decision-making model incorporating a BN. We illustrate the different proposals with realistic examples and show that the hybrid implementation on FPGA-SoC can offer some benefits. Full article

The coordinated scheduling of plug-in electric vehicle (PEV) charging should be constructed in distributed architecture due to the growing population of PEVs. Since the information and communication technology makes the adversary more permeable, the distributed PEV charging coordination is vulnerable to cyber-attack which may degrade the performance of scheduling and even cause the failure of scheduler task. Considering the tradeoff between system-wide economic efficiency, distribution level limitations and PEV battery degration, this paper investigates the resilient distributed coordination of PEV charging to resist cyber-attack, where the steps of detection, isolation, updating and recovery are designed synthetically. Under the proposed scheduling scheme, the misbehaving PEVs suffering from cyber-attack are gradually marginalized and finally isolated, and the remaining well-behaving PEVs obtain their own optimal charging strategy to minimize the total system cost in distributed architecture. The simulation results verify the effectiveness of theoretical method. Full article

Security enhancement and cost reduction have become crucial goals for second-generation reusable launch vehicles (RLV). The thruster is an important actuator for an RLV, and its control normally requires a valve capable of high-frequency operation, which may lead to excessive wear or failure of the thruster valve. This paper aims at developing a thruster fault detection method that can deal with the thruster fault caused by the failure of the thruster valve and play an emergency role in the cases of hardware sensor failure. Firstly, the failure mechanism of the thruster was analyzed and modeled. Then, thruster fault detection was employed by introducing an angular velocity signal, using a blended filter, and determining an isolation threshold. In addition, to support the redundancy management of the thruster, an evaluation method of the nonlinear model-based numerical control prediction was proposed to evaluate whether the remaining fault-free thruster can track the attitude control response performance under the failure of the thruster valve. The simulation results showed that the method is stable and allowed for the effective detection of thruster faults and timely evaluation of recovery performance. Full article