The accurate degradation prediction of Lithium-ion batteries is beneficial to the reliability and safety of battery-driven systems. In this paper, a long short-term memory network (LSTM) model is utilized to predict the capacity degradation trend using partial charge and discharge features of Lithium-ion batteries. Firstly, significant features are extracted from the original charge and discharge data. Then the Pearson correlation coefficient is adopted to filter the features with high correlation coefficients. Selected features are subsequently treated as the input of the prediction model. Finally, a LSTM model is developed and associated hyperparameters are established by Adam algorithm. The proposed method is validated by experimental results on the NASA battery dataset.
Common photoelectric pods often have composite photoelectric sensing functions, such as visible light-sensing components, infrared night vision components, distance sensing components, etc. Among them, visible light sensing component is the most basic and widely used image sensing component. With the rapid development of modern optoelectronic technology, the reliability level of a visible light camera has become to be one of the most critical technical requirements in an optoelectronic pod, which greatly determine the success of a mission. For the entire equipment, the optoelectronic pod is like the eye of human beings. Once it fails, it may bring a devastating threat to the safety of the entire equipment and the performance of one task. This paper focuses on the reliability analysis of the visible light camera used in the optoelectronic pod. Through the analysis of its internal structure and performance indicators, the key and weak design links are obtained, which can provide important theoretical support for the reliability design of optoelectronics.
A reliability qualification test (RQT) is used to judge whether a batch of products meets pre-specified reliability requirements. For high reliable systems, traditional RQT plans often require long test time or pose high risks to both producer and consumer. To cope with this problem, this paper proposes a new method to derive RQT plans by making use of subsystem data, when both system and subsystems are following exponential distributions. Compared with conventional RQT plans that construct system test based on which decisions are made, the proposed method enables deriving system test plans with much shorter test time and keeping producer and consumer risks under control at the same time. A case study is presented to prove the validity of the proposed method.
To effectively and quickly solve the robust redundancy allocation problem under the Min-Max regret framework, a sequential two-stage approach is proposed. The proposed metaheuristic approach includes two stages: the first stage is to solve an optimization problem with regard to various scenarios, and the second stage is to search for the optimal solution of decision variables. These two stages are executed sequentially and iteratively until the preset termination condition is satisfied. The listed example demonstrates the performance of the proposed approach.
Under the conditions of systematic warfare, the diversified mission scenarios and the structure of complex equipment have led to problems such as difficulties in clarifying equipment health management objects and in implementing the allocation of support resources. To solve those problems under the influence of different mission profiles, this paper proposes a mission-oriented prioritization method for health management objects of complex equipment. Firstly, using the graph theory, function-dependent network models are constructed according to the composition of equipment system and mission sessions under the mission profile; Secondly, the PageRank algorithm is used to measure the importance of each system; Finally, combined with the characteristics of the mission profile, the priority list is derived by updating the PR values with expert knowledge, by which the key health management objects are determined. The proposed method is verified with a case study on the design of an equipment health management system. The results show that the method can obtain the priority of each sub-system of equipment efficiently, so as to provide appropriate objects for equipment health management and the distribution of support resources.
With the wide application of machine learning algorithms in various fields, feature selection becomes more and more important as a data preprocessing method which can not only solve the problem of dimension disaster, but also improve the generalization ability of algorithms. Based on this, the main work of this paper is as follows. Firstly, the importance measures and Bayesian network were combined to solve the problem that Bayesian network could not rank the importance of features. At the same time, a recursive feature elimination algorithm based on importance degree theory is proposed with importance degree as the screening index. Finally, the prognostic model of gallbladder cancer was established, which shows that the proposed algorithm has good performance.
Equipment support plays an important role in the formation of equipment combat capability and the development of war. Meanwhile, Equipment support in the complex and changeable system combat requires more precision and intelligence. Combining the concept of enterprise data assets with equipment support, this paper proposes a framework for data system based on equipment support elements. This paper proposes the constructing approach for equipment support knowledge graph. And thoroughly, different methods of designing maintenance decision system are discussed. By analyzing the values and logical relationship among of equipment design data, usage data and support data, the capability of equipment health status evaluation and maintenance decision are improved. Finally, equipment support capability and combat effectiveness are elevated.
To study the influence of compound wind field disturbance on the stability of multi-rotor UAV(unmanned aerial vehicle), a dynamics modeling and simulation method for multi-rotor UAV under the compound wind field model is proposed. Firstly, based on the idea of equivalent modeling in practical engineering, the models of uniform wind, gust, turbulence and wind shear are constructed respectively. The numerical analysis and simulation research are then carried out. Second, the dynamic model of multi rotor UAV under the action of composite wind field is deduced, and the attitude control model is derived. Finally, the numerical analysis software is utilized to simulate and analyze the attitude response of the multi-rotor UAV under the condition of wind and no wind, and analyze its control performance under the interference of compound wind field. The simulation results show that the dynamic model of the multi-rotor UAV under the influence of the wind field can accurately reflect its dynamic performance under the action of the wind field, and the output attitude angle fluctuation is less than 2°. The obtained results show that the established wind field model can be effectively applied to the research on the flight performance of multi-rotor UAV under the interference of compound wind field.
With the advent of the era of big data and intelligent manufacturing, the structure of CNC machine tool has become more and more sophisticated and complex, and the risks of CNC machine tool during operation have become more and more diverse. Only by analyzing, evaluating and maintaining CNC machine operational risks activities to reduce its operational risk. Among the risks, substandard workpiece quality has gradually become the biggest risk in the operation of CNC machine tools, and this risk is usually ignored by people. Therefore, this paper proposes a modeling method for CNC machine tools based on operational risk that considers the quality of the workpiece. Firstly, define the operational risk system of CNC machine, and divide the operational risk of CNC machine into two parts: production risk and use risk. Secondly, the production risk and use risk of CNC machine are modeled separately, and the operational risk of CNC machine will be evaluated by Bayesian network for the CNC machine system. Finally, an example is given to illustrate the feasibility of the modeling method.
National-scale transportation systems are critical infrastructures to ensure the normal operation of the nation and offer essential services to modern societies. And they face a constant barrage of external stresses or threats that challenge their operation. This article analyzes the accessibility and vulnerability of the Chinese road, high-speed rail and airline systems. Firstly, it models the complementary relationship among road, high-speed rail and airline systems and takes them as an integrated transportation system (IRHAS). Then, the accessibility map from (to) the center of Beijing via IRHAS is displayed. Finally, modeling the extreme storm recently occurred in Zhengzhou as a reginal disruption, the vulnerability of the IRHAS is calculated. The findings in this paper provide guidance for city administrators to plan the road, high-speed rail, and airline systems as a whole.
Since the concept of reliability was put forward, how to improve the reliability of district heating system to effectively save energy and ensure economic benefits has become a research hotspot. By analyzing the hydraulic performance of district heating network and the possible flow in each pipe under normal and failure scenarios, we proposed the standby hydraulic design framework. Then, the topology and standby hydraulic design mathematical models of typical single-heat-source single-looped district heating network were established. The variation of hydraulic performance was studied by changing the determination method of design flow, the number, and distribution of users on the mainline. Finally, several design rules which can effectively alleviate the hydraulic imbalance under failure scenarios were proposed. The results show that using the method of weighted summation of the possible flow and its probability in each pipe under various operation scenarios to design pipe diameter has the advantages of high hydraulic performance without greatly increasing investment.
A transmission gear commonly has multiple dependent failure modes. The reliability analysis methods that ignore the dependence effect often have substantial errors. This paper constructs the limit state functions of failure modes based on the stress-strength interference theory, characterizes the dependence among multiple failure modes with the help of Copula theory, transforms the multidimensional dependence problem into several two-dimensional Copulas based on Vine Copula, and uses square Euclidean distance to identify the optimal function. Using this method, a D-Vine Copula model is established for the spur gear in an aero engine accessory transmission system to carry out the gear reliability analysis considering the dependence of multiple failure modes, which demonstrates the rationality and effectiveness of the proposed method.
Wind power is clean and renewable energy, which occupies an important position in the world's energy. This paper proposes a multi-regional fault detection method for wind turbines, which makes full use of multiple sensor data. Specifically, a voting-based Artificial Neural Network (ANN) is constructed to achieve 96.5% detection accuracy, which is a light-weighted model with high operating efficiency. The robustness of this model is confirmed by various numerical experiments as well. Meanwhile, the detailed experiments of several other benchmark methods illustrate that our method has far higher accuracy. Furthermore, we complete the improvement of the model referring to grid search and the accuracy of the model is enhanced to 97.5%.
Anomaly detection of satellite telemetry data is of great significance for on-orbit satellite health monitoring. However, owing to the complex inherent characteristics, satellite telemetry data anomaly detection may be very difficult. To improve the accuracy of detecting satellite telemetry data anomalies, a novel hybrid model, called EEMD-SE-GWO-SVM, is proposed in this paper. In this model, ensemble empirical mode decomposition (EEMD) and sample entropy (SE) theory are firstly cooperated to extract critical features embedded in the raw telemetry data. Support vector machine (SVM) optimized by grey wolf optimizer (GWO) is then served as the predictive technique for the prediction of the reconstructed critical features. The anomaly can be finally detected by observing whether the residual error between the actual and predicted values exceeds a certain threshold. Experiment with the telemetry data of a real-world satellite is carried out and the results demonstrate that the proposed hybrid model outperforms comparison models in terms of anomaly detection accuracy.
In engineering practice, phased mission systems (PMSs) may have phase redundancies. A failed task can be executed not only in the current phase, but also in its redundancy phases. Therefore, different from general PMSs, a PMS with phase redundancy (PMS-PR) has several mission execution sequences. Considering the complexity of the system, it is easy to make mistakes by manual modelling. Therefore, a certain tool is needed to support the modeling process. This paper proposes a specification model of PMS-PR based on system modeling language (SysML). Finally, a simplified PMS-PR is illustrated as an example to show the modeling process of the specification model.
As one of the key functional components of the CNC machine tool, the motorized spindle seriously affects the reliability level of the CNC machine tool. At present, the researches on reliability evaluation methods of motorized spindles based on degradation data are still in the preliminary stage, and there are some problems such as single data source and difficult calculation, which affect the accuracy of reliability evaluation results. To solve these problems, this paper presents a novel reliability evaluation method of motorized spindles based on dual-source data. Taking the motorized spindle of the CNC machine tool as the research object, the degradation models based on nonlinear Wiener process are established according to the degradation data of laboratory reliability bench tests and field data respectively. In order to fuse the laboratory data and field data into dual-source data, a reliability model of the performance degradation of the motorized spindle is established based on the Copula function, which realizes accurate and efficient reliability evaluation of the motorized spindle. The effectiveness of the proposed method is verified by the degradation tests of the motorized spindles.
Since the physical structure and mathematical models are more complex, reliability analysis in practical engineering can be expensive and difficult. A two-level multifidelity metamodel method for reliability analysis is introduced. Following the surrogate model in most of the relevant works, low-fidelity data and high-fidelity data are integrated by co-Kriging model. Besides, the co-Kriging model also provide an approximation for initial performance function. Bayesian method is adopted in model solution and a hybrid Markov chain Monte Carlo (MCMC) sampling algorithm is proposed. High-fidelity response of reliability performance function is estimated by the conditional distribution derivation based on Bayesian theory. Failure domain is identified by indicator function in sampling space that consists of samples derived from MCMC. Accordingly, failure probability estimations are obtained using Monte Carlo simulation (MCS). It is demonstrated through an illustrative example that the proposed method is valid and accurate.
With increasing requirements on reliability, maintainability and safety in modern ICT systems, fault detection, as an indispensable part of AIOps, has become essential in cloud computing or communication network environments. However, due to the lack of effective labels and class imbalance on faulty samples, fault detection performance based on the common classification model can't meet the system's operational requirements. Some recent approaches of SSL propose a consistency regularization loss to solve the problem of insufficient labels. However, these approaches are mainly for images based on artificial data augmentations but not feasible for all data types, and class-imbalance problem is not considered simultaneously. So, we propose a semi-supervised method for imbalanced fault detection with few labels, called SSLCR-IFD. In the method, we use a semi-supervised deep classifier based on consistency loss to solve the lack of labels, in which two sample augmentation methods based on clustering and GAN are used. Furthermore, a selective pseudo-labeling self-training strategy is proposed to solve the class-imbalance problem. Compared with the standard data augmentation, our methods alleviates the need for domain knowledge and can be used on multiple types of tasks. Finally, experiment results show that our method outperforms the baseline methods on two different AIOps tasks.
MOSFET is widely used in motor driver because of its high switching frequency, but it often fails for many reasons which may results in driver faults. This paper presents a diagnosis method based on the output characteristic dictionary for open/short circuit mixed cascading faults of the three-phase driver. The small signal model is used to quantitatively analyze the fault influence, and a multi-value fault dictionary is constructed based on the time-frequency domain characteristics of online measurable signals such as output current and output voltage. Based on the numerical simulation method, the typical single faults and cascading faults are simulated and injected, which proves the effectiveness of this developed method.
Maintenance planning is a significant part of predictive maintenance, which involves task planning, resource scheduling, and prevention. Many data points will be collected during the monitoring and maintenance of sophisticated equipment thanks to the large-scale sensor systems installed in contemporary factories. As a result, with the help of collected maintenance data, maintenance plans may be more detailed and timelier. A knowledge graph (KG) has recently been proposed to manage massive and unorganized maintenance data semantically, enhancing data usage. Despite the fact that previous research had utilized KG for maintenance planning, they had only used semantic searching or graph structure-based algorithms and had not included the prediction of new links. To fill this gap, a maintenance-oriented KG is established firstly based on the well-defined ontology schema and accumulated maintenance data. Then, an Attention-Based Compressed Relational Graph Convolutional Network is proposed to find the potential solutions and explain the fault, specifically for the heterogeneous and sparse graph structure of maintenance-orient KG. A maintenance case of oil drilling equipment is carried out, which compares the proposed model with other cutting-edge models to demonstrate its effectiveness in link prediction.
