Search Results (19)

With rapid urbanization, deep foundation pit clusters (DFPCs) have become increasingly common, introducing complex and significant construction risks. To improve risk evaluation under such complexity and uncertainty, this study proposes a hierarchical assessment framework. First, fault tree analysis is used to systematically identify and decompose DFPC-related risks. Second, a Bayesian network (BN) is constructed based on the fault tree to model interactions among risks, and structural learning techniques are applied to optimize the BN structure. An analytic hierarchy process (AHP) is then used to assign prior probabilities, enabling the identification of critical risk factors. To validate the framework, numerical simulations are used to analyze the impact of support failures on pit stability. The results show that mid-span support failures have the greatest influence. Two DFPC layouts are simulated to assess the effects of failure location and pit spacing. When the spacing is 0.10H (H = excavation depth), failures in a subpit’s mid-support cause the most severe impact on adjacent pits. These results confirm the framework’s effectiveness in evaluating DFPC risk. Full article

Unsafe behavior among air traffic controllers is a significant causal factor in civil aviation safety incidents. To explore the risks and pathways associated with controller-induced aviation accidents, this study develops an analytical model of controller unsafe behavior based on association rules and fault tree Bayesian networks. First, the Human Factors Analysis and Classification System (HFACS) was applied to identify and categorize aviation incident reports attributed to controller errors. Next, association rule algorithms were employed to uncover potential associations between controller unsafe behaviors and related risk factors, and a fault tree Bayesian network (FT-BN) model of controller unsafe behaviors was constructed based on these associations. The results revealed that the most likely unsafe behaviors were: improper allocation of aircraft spacing (30.5%), failure to take necessary intervention measures (28.4%), and improper transfer of control (27.8%). Backward analysis of the FT-BN indicated that improper allocation of aircraft spacing was most likely triggered by failure to provide adequate controller training, failure to take necessary intervention measures was most often caused by forgotten information, and improper transfer of control was most frequently associated with controller fatigue and failure to put risk management efforts in place. This study provides an important framework for the analysis and evaluation of controller behavior management and offers key insights for improving air traffic safety. Full article

Soft robots demonstrate significant advantages in applications within complex environments due to their unique material properties and structural designs. However, they also face challenges in fault diagnosis, such as nonlinearity, time variability, and the difficulty of precise modeling. To address these issues, this paper proposes a fault diagnosis method based on multimodal spatiotemporal features and ensemble learning. First, a sliding-window Kalman filter is utilized to eliminate noise interference from multi-source signals, constructing separate temporal and spatial representation spaces. Subsequently, an adaptive weight strategy for feature fusion is applied to train a heterogeneous decision tree model, followed by a dynamic weighted voting mechanism based on confidence levels to obtain diagnostic results. This method optimizes the feature extraction and fusion process in stages, combined with a dynamic ensemble strategy. Experimental results indicate a significant improvement in diagnostic accuracy and model robustness, achieving precise identification of faults in soft robots. Full article

In this paper, a new methodology for fault detection and diagnosis in photovoltaic systems is proposed. This method employs a novel Euclidean distance-based tree algorithm to classify various considered faults. Unlike the decision tree, which requires the use of the Gini index to split the data, this algorithm mainly relies on computing distances between an arbitrary point in the space and the entire dataset. Then, the minimum and the maximum distances of each class are extracted and ordered in ascending order. The proposed methodology requires four attributes: Solar irradiance, temperature, and the coordinates of the maximum power point (Impp, Vmpp). The developed procedure for fault detection and diagnosis is implemented and applied to classify a dataset comprising seven distinct classes: normal operation, string disconnection, short circuit of three modules, short circuit of ten modules, and three cases of string disconnection, with 25%, 50%, and 75% of partial shading. The obtained results demonstrate the high efficiency and effectiveness of the proposed methodology, with a classification accuracy reaching 97.33%. A comparison study between the developed fault detection and diagnosis methodology and Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbors algorithms is conducted. The proposed procedure shows high performance against the other algorithms in terms of accuracy, precision, recall, and F1-score. Full article

As a critical component of the engine, the failure of aviation fuel pumps can lead to serious safety accidents, necessitating the development of effective maintenance programs. Fault Tree Analysis (FTA) has a clear structure and strong interpretability in maintenance decision making. However, it heavily relies on expert knowledge, which is subject to uncertainty and incoherence. Therefore, this paper proposes the NOA (Nutcracker Optimization Algorithm)–GNN (Graph Neural Network) model to enhance the accuracy and robustness of FTA by mitigating the uncertainty and inconsistency in expert knowledge. The NOA algorithm efficiently searches the solution space to identify globally optimal solutions. An FTA-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) maintenance decision-making framework has also been developed. By integrating FTA with TOPSIS, the proposed method provides a comprehensive and systematic approach that combines qualitative and quantitative analyses, thereby improving the effectiveness and reliability of maintenance decision making. Full article

The stable operation of aerospace equipment is important for space safety, and the fault diagnosis of aerospace equipment is of practical significance. A fault diagnosis system needs to establish clear causal relationships and provide interpretable determination results. Fuzzy fault tree analysis (FFTA) is a flexible and powerful fault diagnosis method, which can deeply understand causes and fault mechanisms. The interval belief rule base (IBRB) can describe uncertainty. In this paper, an interpretable fault diagnosis model (FFDI) for aerospace equipment based on FFTA and the IBRB is presented for the first time. Firstly, the initial FFDI is constructed with the assistance of FFTA. Second, a model inference is implemented based on an evidential reasoning (ER) parsing algorithm. Then, a projection covariance matrix adaptive evolutionary strategy algorithm with an interpretability constraints (IP-CMA-ES) optimization algorithm is used for optimization. Finally, the effectiveness of the FFDI is verified by a flywheel dataset. This method ensures the completeness of the rule base and the interpretability of the model, avoids the problem of exploding certain combinations of rules, and is suitable for the fault diagnosis of aerospace equipment. Full article

The control strategy of an underdriven unmanned underwater vehicle (UUV) equipped with front sonar and actuator faults in a continuous task environment is investigated. Considering trajectory tracking and local path planning in complex-obstacle environments, we propose a task transition strategy under the event-triggered mechanism and design the corresponding state space and action space for the trajectory tracking task under the deep reinforcement learning framework. Meanwhile, a feed-forward compensation mechanism is designed to counteract the effects of external disturbances and actuator faults in combination with a reduced-order extended state observer. For the path planning task under the rapidly exploring random tree (RRT) framework, a reward component and angular factors are introduced to optimize the growth and exploration points of the extended tree under the consideration of the shortest distance, optimal energy consumption, and steering angle constraints. The effectiveness of the proposed method was verified through continuous task simulations of trajectory tracking and local path planning. Full article

In the distributed storage system, when data need to be recovered after node failure, the erasure code redundancy method occupies less storage space than the multi-copy method. At present, the repair mechanism using erasure code to reconstruct the failed node only considers the improvement of link bandwidth on the repair rate and does not consider the impact of the selection of data providing node-set on the repair performance. A single node fault data reconstruction method based on the Software Defined Network (SDN) using the erasure code method is designed to solve the above problems. This method collects the network link-state through SDN, establishes a multi-attribute decision-making model of the data providing node-set based on the node performance, and determines the data providing nodes participating in providing data through the ideal point method. Then, the data recovery problem of a single fault node is modeled as the optimization problem of an optimal repair tree, and a hybrid genetic algorithm is designed to solve it. The experimental results show that under the same erasure code scale, after selecting the nodes of the data providing node-set, compared with the traditional tree topology and star topology, the repair delay distribution of the designed single fault node repair method for a distributed storage system is reduced by 15% and 45% respectively, and the repair flow is close to the star topology, which is reduced by 40% compared with the traditional tree repair. Full article

Machines designed to perform the same tasks using different technologies can be organized into families based on their similarities or differences. We are interested in identifying common properties and differences of such machines from raw sensor data for analysis and fault diagnostics. The usual first step is a feature extraction process that requires an understanding of the machine’s harmonics, bearing frequencies, etc. In this paper, we present a model-free path from the raw sensor data to statistically meaningful feature vectors. This is accomplished by defining a transform independent of the operating frequency and performing statistical reductions to identify the components with the largest variances, resulting in a low dimensional statistically meaningful feature space. To obtain an insight into the family relationships we perform a clustering. As the data set has some labeled characteristics we define an entropy-based measure to evaluate a clustering using the a priori-known labels, resulting in a symmetric measurement uniquely defining the clustering goal. Applying this hierarchically we obtain the family tree. The methods are presented can be applied in general situations. As a case study we apply them to a real data set of vibrating screens. Full article

The development of the basic theory of safety science is relatively short compared with other disciplines, and the corresponding basic theory is weaker. However, with the development of science and technology, more and more complex systems have emerged. These systems are significantly different from earlier systems, including in complexity, factor changes, data information, and system control. Traditional reliability and fault analysis methods are difficult to solve. In order to manage these problems, the author proposes a space fault tree theory to study system reliability and system fault evolution process. At present, the space fault tree theory is divided into four parts, the space fault tree theory foundation, the intelligent space fault tree, the space fault network, the system motion space and the system mapping theory. The space fault tree is combined with factor space, a cloud model, fuzzy structure element, and system stability and information ecology methodology. The objective is for the space fault tree theory to complete the system reliability and fault analysis, as well as fault big data analysis, fault logic relationship reasoning, system fault evolution process research and system motion change measurement capability. In order to demonstrate the results of the space fault tree research, this paper is written to briefly introduce the four major parts and main contents and results of the space fault tree theory. Full article

A photovoltaic (PV) system is one of the renewable energy resources that can help in meeting the ever-increasing energy demand. However, installation of PV systems is prone to faults that can occur unpredictably and remain challenging to detect. Major PV faults that can occur are line-line and open circuits faults, and if they are not addressed appropriately and timely, they may lead to serious problems in the PV system. To solve this problem, this study proposes a voting-based ensemble learning algorithm with linear regression, decision tree, and support vector machine (EL-V_LR-DT-SVM) for PV fault detection and diagnosis. The data acquisition is performed for different weather conditions to trigger the nonlinear nature of the PV system characteristics. The voltage-current characteristics are used as input data. The dataset is studied for a deeper understanding, and pre-processing before feeding it to the EL-V_LR-DT-SVM. In the pre-processing step, data are normalized to obtain more feature space, making it easy for the proposed algorithm to discriminate between healthy and faulty conditions. To verify the proposed method, it is compared with other algorithms in terms of accuracy, precision, recall, and F-1 score. The results show that the proposed EL-V_LR-DT-SVM algorithm outperforms the other algorithms. Full article

The Taiwan Strait, to the west of Taiwan, is rich in wind energy resources and has the greatest offshore wind power potential in the world. Therefore, Taiwan has been actively expanding its offshore wind power industry in this area in recent years and expects to achieve the total installed capacity to 15.6 GW by 2035. Due to the large vessel traffic flow in Western Taiwan’s sea area, wind farms will inevitably reduce the navigable space and shadow some existing marine aids to navigation, thus worsening navigation safety. An approach using a fault tree analysis was used to carry out analysis of collision risk between ship-to-ship and ship-to-turbine. The vessel density distribution and traffic flow within the open sea of offshore wind farms would further increase to curtail the available navigable space. The shadowing effects along navigation channels would thereafter be worsened to raise the probability of collision risks in the sea. The results of the fault tree analysis revealed that if the ship is out of control, the time allowed to provide assistance is rather short, leading to the increase of collision risk extent between ships and wind turbines. Moreover, the study also found that unfit functions of the Vessel Traffic Service System and navigation aids and frequently and arbitrarily crossing the navigation channel of fishery vessels are the main causes of ship collisions. In order to effectively improve the navigation safety, competitive strategies for navigation safety are investigated and evaluated in this study. These strategies include making a complete plan for utilizing the whole sea, integrating the offshore vessel traffic service and management system, providing remote pilotage services, and building salvage vessels. The above promising strategies would enhance the navigation safety within the open sea. Collision risk might occur once marine accident occurs and no salvage vessel is available. Full article

The ability of a switch-mode AC/DC power supply to shrink supplies is a benefit and a requirement for most electronic devices with limited space. Major failures in switch-mode power supply (SMPS) during adverse working conditions are subject to mostly the switching devices and capacitors. For effective condition monitoring of the SMPS, dual (or multiple) sensing provides a more reliable standpoint against the traditional single sensing techniques as it provides a more comprehensive paradigm for accurate condition monitoring. This study proposes an integrated approach to SMPS condition monitoring by exploiting statistically extracted features from current and voltage signals for system fault diagnosis based on electrical stress. Following a correlation-based feature selection approach, salient features were utilized for improved fault detection and isolation (FDI) using ML-based classifiers. Diagnostic results by the classifiers reveal that the random forest and gradient boosting classifiers are highly reliable but computationally expensive when compared with the others while the decision tree was quite cost-efficient with reliable diagnostic results. The proposed framework is effectively applicable for use in diagnosing the switching devices and classification at different states. Full article

Compared to the BeiDou regional navigation satellite system (BDS-2), the BeiDou global navigation satellite system (BDS-3) carried out a brand new integrity concept design and construction work, which defines and achieves the integrity functions for major civil open services (OS) signals such as B1C, B2a, and B1I. The integrity definition and calculation method of BDS-3 are introduced. The fault tree model for satellite signal-in-space (SIS) is used, to decompose and obtain the integrity risk bottom events. In response to the weakness in the space and ground segments of the system, a variety of integrity monitoring measures have been taken. On this basis, the design values for the new B1C/B2a signal and the original B1I signal are proposed, which are 0.9 × 10⁻⁵ and 0.8 × 10⁻⁵, respectively. The hybrid alarming mechanism of BDS-3, which has both the ground alarming approach and the satellite alarming approach, is explained. At last, an integrity risk analysis and verification work were carried out using the operating data of the system in 2019. The results show that the actual operation of the system is consistent with the conceptual design, which satisfies the integrity performance promised by BDS-3 in the ICAO SAPRs. Full article

In this study, we proposed a method to improve the safety level of control software (CSW) by managing the CSW’s design information and safety analysis results, and combining failure mode and effects analysis (FMEA) and fault tree analysis (FTA). Here, the CSW is developed using structured analysis and design methodology. In the upper stage of the CSW’s development process, as the input of the preliminary design information (data flow diagrams (DFDs) and control flow diagrams (CFDs)), the causes of undesirable events of the CSW are clarified by FMEA, and the countermeasures are reflected in the preliminary design information. In the lower stage of the CSW’s development process, as the inputs of the detailed design information (DFDs and CFDs in the lower level) and programs, the causes of the specific undesirable event are clarified by FTA, and the countermeasures are reflected in the detailed design specifications and programs. The processes are repeated until the impact of undesirable events become the acceptable safety level. By applying the proposed method to the CSW installed into a communication control equipment on the space system, we clarified several undesirable events and adopted adequate countermeasures. Consequently, a safer CSW is developed by applying the proposed method. Full article