A Cascading Fault Path Prediction Method for Integrated Energy Distribution Networks Based on the Improved OPA Model under Typhoon Disasters

In recent times,the impact of typhoon disasters on integrated energy active distribution networks(IEADNs)has received increasing attention,particularly,in terms of effective cascading fault path prediction and enhanced fault recovery performance.In this study,we propose a modified ORNL-PSerc-Alaska(OPA)model based on optimal power flow(OPF)calculation to forecast IEADN cascading fault paths.We first established the topology and operational model of the IEADNs,and the typical fault scenario was chosen according to the component fault probability and information entropy.The modified OPA model consisted of two layers:An upper-layer model to determine the cascading fault location and a lower-layer model to calculate the OPF by using Yalmip and CPLEX and provide the data to update the upper-layer model.The approach was validated via the modified IEEE 33-node distribution system and two real IEADNs.Simulation results showed that the fault trend forecasted by the novel OPA model corresponded well with the development and movement of the typhoon above the IEADN.The proposed model also increased the load recovery rate by>24%compared to the traditional OPA model.

On-line least squares support vector machine algorithm in gas prediction

Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions.The Support Vector Machine(SVM) is a new machine learning algorithm that has excellent properties.The least squares support vector machine(LS-SVM) algorithm is an improved algorithm of SVM.But the common LS-SVM algorithm,used directly in safety predictions,has some problems.We have first studied gas prediction problems and the basic theory of LS-SVM.Given these problems,we have investigated the affect of the time factor about safety prediction and present an on-line prediction algorithm,based on LS-SVM.Finally,given our observed data,we used the on-line algorithm to predict gas emissions and used other related algorithm to compare its performance.The simulation results have verified the validity of the new algorithm.

Analysis of radar fault prediction based on combined model

Based on modeling principle of GM（1,1）model and linear regression model,a combined prediction model is established to predict equipment fault by the fitting of two models.The new prediction model takes full advantage of prediction information provided by the two models and improves the prediction precision.Finally,this model is introduced to predict the system fault time according to the output voltages of a certain type of radar transmitter.

A New Approach to Prediction of Fault of Manufacturing Quality for FMS

This paper makes a study of the cause of manufacturing fault,develops the and/or- fault-tree of manufacturing quality fault for MC,and presents a new concept of faint manufacturing quality fault(FMQF)and the decision making tree with which the fault of manufacturing system would be found out from FMQF.An approach to identification of FMQF,based on fuzzy set theory,is presented,which can be used for estimating the status of equipment with the deviation of control charts.Based on the study above,an expert system for the flexible manufacturing system's FMQF detection and prediction is built.

Prediction of mechanical properties for deep drawing steel by deep learning

At present,iron and steel enterprises mainly use“after spot test ward”to control final product quality.However,it is impossible to realize on-line quality predetermining for all products by this traditional approach,hence claims and returns often occur,resulting in major eco-nomic losses of enterprises.In order to realize the on-line quality predetermining for steel products during manufacturing process,the predic-tion models of mechanical properties based on deep learning have been proposed in this work.First,the mechanical properties of deep drawing steels were predicted by using LSTM(long short team memory),GRU(gated recurrent unit)network,and GPR(Gaussian process regression)model,and prediction accuracy and learning efficiency for different models were also discussed.Then,on-line re-learning methods for transfer learning models and model parameters were proposed.The experimental results show that not only the prediction accuracy of optimized trans-fer learning models has been improved,but also predetermining time was shortened to meet real time requirements of on-line property prede-termining.The industrial production data of interstitial-free(IF)steel was used to demonstrate that R2 value of GRU model in training stage reaches more than 0.99,and R2 value in testing stage is more than 0.96.

Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes

This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.

Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System

Since the efficiency of photovoltaic(PV) power is closely related to the weather,many PV enterprises install weather instruments to monitor the working state of the PV power system.With the development of the soft measurement technology,the instrumental method seems obsolete and involves high cost.This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data.By this method,the weather types are deduced by data analysis,instead of weather instrument A better fault detection is obtained by using the support vector machines(SVM) and comparing the predicted and the actual weather.The model of the weather prediction is established by a direct SVM for training multiclass predictors.Although SVM is suitable for classification,the classified results depend on the type of the kernel,the parameters of the kernel,and the soft margin coefficient,which are difficult to choose.In this paper,these parameters are optimized by particle swarm optimization(PSO) algorithm in anticipation of good prediction results can be achieved.Prediction results show that this method is feasible and effective.

Fault prediction of fighter based on nonparametric density estimation

Fighters and other complex engineering systems have many characteristics such as difficult modeling and testing, multiple working situations, and high cost. Aim at these points, a new kind of real-time fault predictor is designed based on an improved k-nearest neighbor method, which needs neither the math model of system nc, the training data and prior knowledge. It can study and predict while system＇s running, so that it can overcome the difficulty of data acquirement. Besides, this predictor has a fast prediction speed, and the false alarm rate and missing alarm rate can be adjusted randomly. The method is simple and universalizable. The result of simulation on fighter F-16 proved the effidency.

DESCRIPTION AND PREDICTION OF CATASTROPHE OF VIBRATION STATE FOR FAULTY ROTORS

A sudden increase of vibration amplitude with no foreboding often results in an abrupt breakdown of a mechanical system.The catastrophe of vibration state of a faulty rotor is a typical nonlinear phenomenon,and very difficult to be described and predicted with linear vibration theory.On the basis of nonlinear vibration and catastrophe theory,fhe eatastrophe of the vibration amplitude of the faulty rotor is described;a way to predict its emergence is developed.

Fault prediction method for nuclear power machinery based on Bayesian PPCA recurrent neural network model

Early fault warning for nuclear power machinery is conducive to timely troubleshooting and reductions in safety risks and unnecessary costs. This paper presents a novel intelligent fault prediction method, integrated probabilistic principal component analysis(PPCA), multi-resolution wavelet analysis, Bayesian inference, and RNN model for nuclear power machinery that consider data uncertainty and chaotic time series. After denoising the source data, the Bayesian PPCA method is employed for dimensional reduction to obtain a refined data group. A recurrent neural network(RNN) prediction model is constructed, and a Bayesian statistical inference approach is developed to quantitatively assess the prediction reliability of the model. By modeling and analyzing the data collected on the steam turbine and components of a nuclear power plant, the results of the goodness of fit, mean square error distribution, and Bayesian confidence indicate that the proposed RNN model can implement early warning in the fault creep period. The accuracy and reliability of the proposed model are quantitatively verified.

A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process

With the increase in the complexity of industrial system, simply detecting and diagnosing a fault may be insufficient in some cases, and prognosing the fault ahead of time could have a certain necessity. Accurate prediction of key alarm variables in chemical process can indicate the possible change to reduce the probability of abnormal conditions. According to the characteristics of chemical process data, this work proposed a key alarm variables prediction model in chemical process based on dynamic-inner principal component analysis(DiPCA) and long short-term memory(LSTM). DiPCA is used to extract the most dynamic components for prediction. While LSTM is used to learn the relationship and predict the key alarm variables. This work used a simulation data set and a real hydrogenation process data set for applications and explained the model validity from the essential characteristics. Comparison of results with different models shows that our model has better prediction accuracy and performance, which can provide the basis for fault prognosis and health management.

Research on information technology of state monitoring and fault prediction for mechatronics system

The safety and reliability of mechatronics systems,particularly the high-end,large and key mechatronics equipment in service,can strongly influence on production efficiency,personnel safety,resources and environment.Based on the demands of development of modern industries and technologies such as international industry 4.0,Made-in-China 2025 and Internet + and so on,this paper started from revealing the regularity of evolution of running state of equipment and the methods of signal processing of low signal noise ratio,proposed the key information technology of state monitoring and earlyfault-warning for equipment,put forward the typical technical line and major technical content,introduced the application of the technology to realize modern predictive maintenance of equipment and introduced the development of relevant safety monitoring instruments.The technology will play an important role in ensuring the safety of equipment in service,preventing accidents and realizing scientific maintenance.

Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks

Time-series prediction is one of the major methodologies used for fault prediction. The methods based on recurrent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping ability. As a new recurrent neural network, reservoir neural network can effectively process the time-series prediction. However, the ill-posedness problem of reservoir neural networks has seriously restricted the generalization performance. In this paper, a fault prediction algorithm based on time-series is proposed using improved reservoir neural networks. The basic idea is taking structure risk into consideration, that is, the cost function involves not only the experience risk factor but also the structure risk factor. Thus a regulation coefficient is introduced to calculate the output weight of the reservoir neural network. As a result, the amplitude of output weight is effectively controlled and the ill-posedness problem is solved. Because the training speed of ordinary reservoir networks is naturally fast, the improved reservoir networks for time-series prediction are good in speed and generalization ability. Experiments on Mackey–Glass and sunspot time series prediction prove the effectiveness of the algorithm. The proposed algorithm is applied to TE process fault prediction. We first forecast some timeseries obtained from TE and then predict the fault type adopting the static reservoirs with the predicted data.The final prediction correct rate reaches 81%.

Fault Prediction of Elevator Door System Based on PSO-BP Neural Network

Nowadays, the elevator has become an indispensable means of indoor transportation in people’s life, but in recent years this kind of traffic tools has caused many casualties because of the gate system fault. In order to ensure the safe and reliable operation of the elevator, the failure of elevator door system was predicted in this paper. Against the fault type of elevator door system: elevator door opened, excessive vibration when elevator door opened or closed, elevator door did not open or closed when reached the specified level. Three fault types were used as the output of the prediction model. There were 8 reasons for the failure, used them as input. A model based on particle swarm optimization (PSO) and BP neural network was established, using MATLAB to emulation;the results showed that: PSO-BP neural network algorithm was feasible in the fault prediction of the elevator door system.

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.

Quantitative multiparameter prediction of fault-related fractures： a case study of the second member of the Funing Formation in the Jinhu Sag, Subei Basin

In this paper, the analysis of faults with different scales and orientations reveals that the distribution of fractures always develops toward a higher degree of similarity with faults, and a method for calculating the multiscale areal fracture density is proposed using fault-fracture self-similarity theory. Based on the fracture parameters observed in cores and thin sections, the initial apertures of multiscale fractures are determined using the constraint method with a skewed distribution. Through calculations and statistical analyses of in situ stresses in combination with physical experiments on rocks, a numerical geomechanical model of the in situ stress field is established. The fracture opening ability under the in situ stress field is subsequently analyzed. Combining the fracture aperture data and areal fracture density at different scales, a calculation model is proposed for the prediction of multiscale and multiperiod fracture parameters, including the fracture porosity, the magnitude and direction of maximum permeability and the flow conductivity. Finally, based on the relationships among fracture aperture,density, and the relative values of fracture porosity and permeability, a fracture development pattern is determined.

A fault prediction method for catenary of high-speed rails based on meteorological conditions

Fault frequency of catenary is related to meteo-rological conditions. In this work, based on the historical data, catenary fault frequency and weather-related fault rate are introduced to analyse the correlation between catenary faults and meteorological conditions, and further the effect of meteorological conditions on catenary oper-ation. Moreover, machine learning is used for catenary fault prediction. As with the single decision tree, only a small number of training samples can be classified cor-rectly by each weak classifier, the AdaBoost algorithm is adopted to adjust the weights of misclassified samples and weak classifiers, and train multiple weak classifiers. Finally, the weak classifiers are combined to construct a strong classifier, with which the final prediction result is obtained. In order to validate the prediction method, an example is provided based on the historical data from a railway bureau of China. The result shows that the mapping relation between meteorological conditions and catenary faults can be established accurately by AdaBoost algorithm. The AdaBoost algorithm can accurately predict a catenary fault if the meteorological conditions are provided.

Fault Prediction and Diagnosis of Warship Equipment Field Programmable Gate Array Software

In order to solve the current high failure rate of warship equipment field programmable gate array( FPGA) software,fault detection is not timely enough and FPGA detection equipment is expensive and so on. After in-depth research,this paper proposes a warship equipment FPGA software based on Xilinx integrated development environment( ISE) and ModelSim software.Functional simulation and timing simulation to verify the correctness of the logic design of the FPGA,this method is very convenient to view the signal waveform inside the FPGA program to help FPGA test engineers to achieve FPGA fault prediction and diagnosis. This test method has important engineering significance for the upgrading of warship equipment.

Dynamic Fault Model for On-Line Evaluation of Yarn End Breakage during Ring Spinning

In this paper, a dynamic fault model is proposed to predict yarn end breakage in the spinning procedure through investigation of fault characteristics. In view of the principle that uniformity bad in raw material causes iustable yarn formation, the investigation focuses on the fault characteristic existing in the dynamic tension. Analyzing the dynamic spinning system, the phenomenon of over random shock in a spinning triangle is discovered to be the main physical event prior to yarn end breakage. The fault characteristic is further confirmed by dynamic tests and signal processing, and can be used to make an approach to predicting yarn end breakage. A relative energy feature is defined for evaluating the tendency of yarn end breakage, and its effectiveness is verified by on.line monitoring tests in the laboratory. The research results show that the proposed dynamic fault model has not only an advantage in indicating the presence of fault characteristics, but also great potentials in quantitating fault in online spinning monitoring.