Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Flight mission modeling based on BDI Petri net

Goals reasoning and management of pilot is a key issue to monitor pilot's behavior and intention. Traditional modeling methods are based on scenarios or situations, such methods will cause the,covering problem due to redundancy and are incapable of depicting interactions among various goals and plans of pilot. Petri net integrated with belief, desire and intention (BDI) theory (BDI Petri net) is designed to solve this problem. Focusing on the BDI theory, goal states of agent are discussed firstly. Belief, desire and intention are modeled by places and transitions based on the Petri net theory. In order to simplify the network, colored token is introduced to depict various states of belief, and the hierarchy transition is applied to model the intention, together with tokens' flow derrionstrating the interaction among various goals and relationship among belief, desire and intention. A search and rescue mission is used to validate the proposed method and the result indicates that the model can be used to monitor goals and behaviors of pilots.

Combined proportional navigation law for interception of high-speed targets

A new proportional navigation(PN) guidance law,called combined proportional navigation(CPN),is proposed.The guidance law is designed to intercept high-speed targets,which is a common case for ballistic targets.The range of target-to-interceptor speed ratio during target interception is derived when guidance laws are applied in high-speed targets interception,and the effectiveness of negative navigation ratio in the PN-based guidance law is proven analytically in some lemmas.Based on the lemmas,the lateral acceleration command of CPN is defined,and the solution to the appearance of singularity in time-varying navigation ratio is given.The simulation results show that CPN can determine headon engagement(as PN) or tail-chase engagement(as RPN) through initial path angle compared with PN and retro proportional navigation(RPN),and can adjust the value of navigation ratio for head-on engagement or tail-chase engagement.Therefore,the capture region of CPN is larger than that of other guidance laws using PN-based methods.

Residual Fatigue Life Prediction of Ball Bearings Based on Paris Law and RMS

Paris law can reflect the failure mechanism of materials and is usually used to be a method to predict fatigue life or residual fatigue life.But the variable which can represent the health of machine is hardly measured on line.To a degree,the difficulty of on-line application restricts the scope of application of Paris law.The relationship between characteristic values of vibration signals and the variable in the Paris equation which can describe the health of machine is investigated by taking ball bearings as investigative objects.Based on 6205 deep groove ball bearings as a living example,historical lives and vibration signals are analyzed.The feasibility of describing that variable in the Paris equation by the characteristic value of vibration signals is inspected.After that vibration signals decomposed by empirical mode decomposition（EMD）,root mean square（RMS） of intrinsic mode function（IMF） involving fault characteristic frequency has a consistent trend with the diameter of flaws.Based on the trend,two improved Paris models are proposed and the scope of application of them is inspected.These two Paris Models are validated by fatigue residual life data from tests of rolling element bearings and vibration signals monitored in the process of operation of rolling element bearings.It shows that the first improved Paris Model is simple and plain and it can be easily applied in actual conditions.The trend of the fatigue residual life predicted by the second improved Paris model is close to the actual conditions and the result of the prediction is slightly greater than the truth.In conclusion,after the appearance of detectable faults,these improved models based on RMS can predict residual fatigue life on line and a new approach to predict residual fatigue life of ball bearings on line without disturbing the machine running is provided.

Experimental investigation on plasma jet deflection with magnetic fluid control based on PIV measurement

This paper is devoted to experimentally investigating the influence of magnetic field intensity and gas temperature on the plasma jet deflection controlled by magneto hydrodynamics. The catalytic ionization seed CS_2CO_3 is injected into combustion gas by artificial forced ionization to obtain plasma fluid on a high-temperature magnetic fluid experimental platform. The plasma jet was deflected under the effect of an external magnetic field, forming a thrust-vector effect.Magnesium oxide was selected as a tracer particle, and a two-dimensional image of the jet flow field was collected using the particle image velocimetry(PIV) measurement method. Through image processing and velocity vector analysis of the flow field, the value of the jet deflection angle was obtained quantitatively to evaluate the thrust-vector effect. The variation of the jet deflection angle with the magnetic field intensity and gas temperature was studied under different experimental conditions. Experimental results show that the jet deflection angle increased gradually with a rise in gas temperature and then increased substantially when the gas temperature exceeded 2300 K. The jet deflection angle also increased with an increase in magnetic induction intensity. Experiments demonstrate it is feasible to use PIV test technology to study the thrust vector under magnetic control conditions.

Fault Pattern Recognition Based on Hidden Markov Model

Because performance parameters of gear have degradation,a method is proposed to recognize and analyze its faults using the hidden Markov model( HMM). In this method,firstly,the delayed correlation-envelope method is used to extract features from vibration signals. Then,HMMs are trained respectively using data under normal condition,gear root crack condition and gear root breaking condition. Further,the trained HMMs are used in pattern recognition and model assessment. Finally,the results from standard HMM and the proposed method are compared, which shows that the proposed methodology is feasible and effective.

Route Guidance for Visually Impaired Based on Haptic Technology and Their Spatial Cognition

Haptic(vibration)information expression is an effective human-computer interaction mode and information transfer method.It makes up shortcomings of sound under certain conditions and is an important channel of information transfer for route guidance field.As a special kind of walkers,the visually impaired pedestrians have a specific type of cognition or perception of route guidance environment(including spatial orientation,distance and walking speed etc.)and they have sharp sense of touch resources form the ordinary.This work integrated application of GPS,GIS and Haptic(vibration)technology to develop more reliable mode route guidance for the visually impaired.It provides different vibration to the user under two circumstances:key nodes of roads ahead and deviation planning path.It resists environmental noise,reflects timely and has high effectiveness.Based on Google Legend phone,we developed the prototype and realized the designed functions,and verified the effectiveness of the system.We initially determined the thresholds of deviating from the path,those at road junctions and other nodes through experiments,interviews etc.And we used the thresholds for experiments testing and guiding.Then they were inspected,corrected and improved in the field practice.Finally,more reasonable thresholds were drawn out for future applications in reality.

Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks

Precise fault diagnosis is an important part of prognostics and health management. It can avoid accidents, extend the service life of the machine, and also reduce maintenance costs. For gas turbine engine fault diagnosis, we cannot install too many sensors in the engine because the operating environment of the engine is harsh and the sensors will not work in high temperature, at high rotation speed, or under high pressure. Thus, there is not enough sensory data from the working engine to diagnose potential failures using existing approaches. In this paper, we consider the problem of engine fault diagnosis using finite sensory data under complicated circumstances, and propose deep belief networks based on information entropy, IE-DBNs, for engine fault diagnosis. We first introduce several information entropies and propose joint complexity entropy based on single signal entropy. Second, the deep belief networks （DBNs） is analyzed and a logistic regression layer is added to the output of the DBNs. Then, information entropy is used in fault diagnosis and as the input for the DBNs. Comparison between the proposed IE-DBNs method and state-of-the-art machine learning approaches shows that the IE-DBNs method achieves higher accuracy.

A Study on Height Reassignment for the AMV Products of the FY-2C Satellite

A method for using height reassignment to improve the quality of satellite-derived atmospheric motion vectors （AMVs） is presented. The rationale underlying height reassignment is explored, and the technical details are studied by applying three height reassignment schemes that use NCEP reanalysis winds. The quality of the AMVs is generally improved following reassignment, although the magnitude of the improve- ment differs according to the scheme applied. Scheme 3 provides the best quality and stability, followed by Scheme 1 and Scheme 2. The negative biases in the zonal components of the AMVs decrease from [ 5, 4] m s^-1 to 〈- 1 m s 1 following reassignment. The meridional components also improve. The AMVs derived from the infrared and water vapor channels improve by 58.7% and 25%, respectively, The feasibility of using Scheme 3 in the operational derivation of AMVs is studied by incorporating the forecast wind field predicted by a T511 medium-range numerical weather prediction （NWP） system. Incorporating the 12-h forecast reduces the negative biases in zonal winds and positive biases in meridional winds retrieved from the water vapor channel, improving the overall quality of the AMVs by 26.7%. Extending the validity period of the forecast field linearly reduces the improvement in retrieved AMVs, but the magnitude of this reduction is small. Incorporating the 120-h forecast field still results in a 13% improvement, although it may eliminate a larger number of AMVs of good quality.