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A hybrid deep neural network based on multi-time window convolutional bidirectional LSTM for civil aircraft APU hazard identification

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Chinese Journal of Aeronautics 2022年第4期35卷 344-361页

作者： Di ZHOU Xiao ZHUANG hongfu zuo Civil A viation Key Laboratory of Aircraft Health Monitoring and Intelligent Maintenance College of Civil AviationNanjing University of Aeronautics and AstronauticsNanjing 210016China Department of Mechanical and Industrial Engineering University of TorontoToronto M5S 3G8Canada College of Science Nanjing University of Aeronautics and AstronauticsNanjing 210016China

Safety is one of the important topics in the field of civil aviation. Auxiliary Power Unit(APU) is one of important components in aircraft, which provides electrical power and compressed air for aircraft. The hazards in APU are prone to cause economic losses and even casualties. So,actively identifying the hazards in APU before an accident occurs is necessary. In this paper, a Hybrid Deep Neural Network(HDNN) based on multi-time window convolutional neural network-Bidirectional Long Short-Term Memory(CNN-Bi LSTM) neural network is proposed for active hazard identification of APU in civil aircraft. In order to identify the risks caused by different types of failures, the proposed HDNN simultaneously integrates three CNN-Bi LSTM basic models with different time window sizes in parallel by using a fully connected neural network. The CNN-Bi LSTM basic model can automatically extract features representing the system state from the input data and learn the time information of irregular trends in the time series data. Nine benchmark models are compared with the proposed HDNN. The comparison results show that the proposed HDNN has the highest identification accuracy. The HDNN has the most stable identification performance for data with imbalanced samples.

关键词： Civil aviation Convolutional neural networks Deep neural networks Hazard identification Long short-term memory

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Time Sequence Change-Point Model of Electrostatic State Parameters of Aircraft Engine

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Transactions of Nanjing University of Aeronautics and Astronautics 2018年第1期35卷 126-134页

作者： Fu Yu Wei Dongdong Zuo Hongfu Feng Zhengxing College of Aeronautical Engineering Civil Aviation University of China College of Civil Aviation Nanjing University of Aeronautics and Astronautics

Electrostatic monitoring technology of particle charging information can facilitate online monitoring of aero-engine,which effectively enhances engine fault diagnosis and health managements.Unlike traditional engine state monitoring technologies,aircraft engine monitoring by gas path electrostatic monitoring not only covers the predicted information source itself,but also detects the information that can provide an early warnings for initial fault states through gas path charging levels.This paper establishes a non-stationary time sequence change-point model for anomaly recognition of electrostatic signals based on change-point theory combined with difference method of non-stationary time series.Finally,electrostatic induction data were utilized by the engine life test for a particular aircraft to validate the proposed algorithm.The results indicate that the activity level and the event rate were0.5—0.8(nc)and 50%,respectively,which were far greater than 4—12(pc)and 0—4% under normal working conditions of the engine.

关键词： aero-engine state identification electrostatic monitoring technology time series change-point statistics

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Remaining useful life prognostics for aeroengine based on superstatistics and information fusion

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Chinese Journal of Aeronautics 2014年第5期27卷 1086-1096页

作者： Liu Junqiang Zhang Malan Zuo Hongfu Xie Jiwei College of Civil Aviation Nanjing University of Aeronautics and Astronautics

Remaining useful life（RUL） prognostics is a fundamental premise to perform conditionbased maintenance（CBM） for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.

关键词： Degradation Information fusion Kalman filtering Performance Prognostics Remaining useful life Superstatistics

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Study on segmented distribution for reliability evaluation

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Chinese Journal of Aeronautics 2017年第1期30卷 310-329页

作者： Li Huaiyuan Zuo Hongfu Su Yan Xu Juan Yin Yibing School of Software Engineering Jinling Institute of Technology College of Civil Aviation Nanjing University of Aeronautics and Astronautics

In practice, the failure rate of most equipment exhibits different tendencies at different stages and even its failure rate curve behaves a multimodal trace during its life cycle. As a result,traditionally evaluating the reliability of equipment with a single model may lead to severer errors.However, if lifetime is divided into several different intervals according to the characteristics of its failure rate, piecewise fitting can more accurately approximate the failure rate of equipment. Therefore, in this paper, failure rate is regarded as a piecewise function, and two kinds of segmented distribution are put forward to evaluate reliability. In order to estimate parameters in the segmented reliability function, Bayesian estimation and maximum likelihood estimation（MLE） of the segmented distribution are discussed in this paper. Since traditional information criterion is not suitable for the segmented distribution, an improved information criterion is proposed to test and evaluate the segmented reliability model in this paper. After a great deal of testing and verification,the segmented reliability model and its estimation methods presented in this paper are proven more efficient and accurate than the traditional non-segmented single model, especially when the change of the failure rate is time-phased or multimodal. The significant performance of the segmented reliability model in evaluating reliability of proximity sensors of leading-edge flap in civil aircraft indicates that the segmented distribution and its estimation method in this paper could be useful and accurate.

关键词： Bayesian estimates Gibbs sampling method Maximum likelihood estima-tion Proximity sensors in leadingedge flap in aircraft Reliability Segmented distribution Weibull distribution

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Visual inspection of aircraft skin:Automated pixel-level defect detection by instance segmentation

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Chinese Journal of Aeronautics 2022年第10期35卷 254-264页

作者： Meng DING Boer WU Juan XU Abdul Nasser KASULE hongfu zuo College of Civil Aviation Nanjing University of Aeronautics and AstronauticsNanjing 210016China Key Laboratory of Civil Aircraft Health Monitoring and Intelligent Maintenance of CAAC Nanjing 210016China

Skin defect inspection is one of the most significant tasks in the conventional process of aircraft inspection.This paper proposes a vision-based method of pixel-level defect detection,which is based on the Mask Scoring R-CNN.First,an attention mechanism and a feature fusion module are introduced,to improve feature representation.Second,a new classifier head—consisting of four convolutional layers and a fully connected layer—is proposed,to reduce the influence of information around the area of the defect.Third,to evaluate the proposed method,a dataset of aircraft skin defects was constructed,containing 276 images with a resolution of 960×720 pixels.Experimental results show that the proposed classifier head improves the detection and segmentation accuracy,for aircraft skin defect inspection,more effectively than the attention mechanism and feature fusion module.Compared with the Mask R-CNN and Mask Scoring R-CNN,the proposed method increased the segmentation precision by approximately 21%and 19.59%,respectively.These results demonstrate that the proposed method performs favorably against the other two methods of pixellevel aircraft skin defect detection.

关键词： Aircraft skin Automatic non-destructive testing Defect inspection Instance segmentation Machine vision

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Evaluation of aileron actuator reliability with censored data

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Chinese Journal of Aeronautics 2015年第4期28卷 1087-1103页

作者： Li Huaiyuan Zuo Hongfu Liu Ruochen Liu Junqiang Jing Cai College of Civil Aviation Nanjing University of Aeronautics and Astronautics

For the purpose of enhancing reliability of aileron of Airbus new-generation A350 XWB,an evaluation of aileron reliability on the basis of maintenance data is presented in this paper.Practical maintenance data contains large number of censoring samples, information uncertainty of which makes it hard to evaluate reliability of aileron actuator.Considering that true lifetime of censoring sample has identical distribution with complete sample, if censoring sample is transformed into complete sample, conversion frequency of censoring sample can be estimated according to frequency of complete sample.On the one hand, standard life table estimation and product limit method are improved on the basis of such conversion frequency, enabling accurate estimation of various censoring samples.On the other hand, by taking such frequency as one of the weight factors and integrating variance of order statistics under standard distribution, weighted least square estimation is formed for accurately estimating various censoring samples.Large amounts of experiments and simulations show that reliabilities of improved life table and improved product limit method are closer to the true value and more conservative; moreover, weighted least square estimate（WLSE）, with conversion frequency of censoring sample and variances of order statistics as the weights, can still estimate accurately with high proportion of censored data in samples.Algorithm in this paper has good effect and can accurately estimate the reliability of aileron actuator even with small sample and high censoring rate.This research has certain significance in theory and engineering practice.

关键词： Ailerons Civil aimraft Non-parametric data distri-butions Order statistics Parameter estimation Reliability estimation Weight

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Joint optimization of sampling interval and control for condition-based maintenance using availability maximization criterion

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Journal of Systems Engineering and Electronics 2018年第1期29卷 203-215页

作者： LI Xin CAI Jing Zuo Hongfu LIU Ruochen CHEN Xi GUO Jiachen College of Civil Aviation Nanjing University of Aeronautics and AstronauticsNanjing 211106China School of Automobile and Traffic Engineering Jiangsu University of TechnologyChangzhou 213001China Shanghai Aircraft Customer Service Co. Ltd.Shanghai 200241China

Most of the maintenance optimization models in condition-based maintenance(CBM) consider the cost-optimal criterion, but few papers have dealt with availability maximization for maintenance applications. A novel optimal Bayesian control approach is presented for maintenance decision making. The system deterioration evolves as a three-state continuous time hidden semi-Markov process. Considering the optimal maintenance policy, the multivariate Bayesian control scheme based on the hidden semi-Markov model(HSMM) is developed, the objective is to maximize the long-run expected average availability per unit time. The proposed approach can optimize the sampling interval and control limit jointly. A case study using Markov chain Monte Carlo(MCMC)simulation is provided and a comparison with the Bayesian control scheme based on hidden Markov model(HMM), the age-based replacement policy, Hotelling’s T2, multivariate exponentially weihted moving average(MEWMA) and multivariate cumulative sum(MCUSUM) control charts is given, which illustrates the effectiveness of the proposed method.

关键词： condition-based maintenance(CBM) availability maximization Markov chain Monte Carlo(MCMC) hidden semiMarkov model(HSMM) Bayesian control sampling interval

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Real-Time Electrostatic Monitoring of Wear Debris for Wind Turbine Gearbox

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Transactions of Nanjing University of Aeronautics and Astronautics 2017年第2期34卷 195-204页

作者： Li Xin Zuo Hongfu Cai Jing Sun Jianzhong Liu Ruochen Xu Yutong College of Civil Aviation Nanjing University of Aeronautics and Astronautics School of Automobile and Traffic Engineering Jiangsu University of Technology

Engineering practice has shown that early faults of gearboxes are a leading maintenance cost driver that can easily lower the profit from a wind turbine operation.A novel oil-lubricated electrostatic monitoring of wear debris for a wind turbine gearbox is presented.The continuous wavelet transform(CWT)is used to eliminate the noises of the original electrostatic signal.The kurtosis and root mean square(RMS)values of the time domain signal are extracted as the characteristic parameters to reflect the deterioration of the gearbox.The overall tendency of electrostatic signals in accelerated life test is analyzed.In the eighth cycle,the abnormal wear in the wind turbine gearbox is detected by electrostatic monitoring.A comparison with the popular MetalScan monitoring is given to illustrate the effectiveness of the electrostatic monitoring method.The results demonstrate that the electrostatic monitoring method can detect the fault accurately.

关键词： wind turbine gearbox oil-lubricated system electrostatic monitoring characteristic parameter accelerated life test

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Using genetic/simulated annealing algorithm to solve disassembly sequence planning

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Journal of Systems Engineering and Electronics 2009年第4期20卷 906-912页

作者： Wu Hao Zuo Hongfu Coll. of Civil Aviation Nanjing Univ. of Aeronautics and Astronautics Nanjing 210016 P. R. China

Disassembly sequence planning （DSP） plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassembly sequence planning problems efficiently, a product disassembly hybrid graph model, which describes the connection, non-connection and precedence relationships between the product parts, is established based on the characteristic of disassembly. Farther, the optimization model is provided to optimize disassembly sequence. And the solution methodology based on the genetic/simulated annealing algorithm with binaxy-tree algorithm is given. Finally, an example is analyzed in detail, and the result shows that the model is correct and efficient.

关键词： disassembly sequence planning disassembly hybrid graph connection matrix precedence matrix binary-tree algorithms simulated annealing algorithm genetic algorithm.

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Optimizing combination of aircraft maintenance tasks by adaptive genetic algorithm based on cluster search

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Journal of Systems Engineering and Electronics 2016年第1期27卷 140-156页

作者： Huaiyuan Li hongfu zuo Kun Liang Juan Xu Jing Cai Junqiang Liu College of Civil Aviation Nanjing University of Aeronautics and AstronauticsNanjing 211106China Huaiyin Institute of Technology Huai'an 223003China

It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optimal combination under various constraints not only involves numerical calculations but also is an NP-hard combinatorial problem.To solve the problem,an adaptive genetic algorithm based on cluster search,which is divided into two phases,is put forward.In the first phase,according to the density,all individuals can be homogeneously scattered over the whole solution space through crossover and mutation and better individuals are collected as candidate cluster centres.In the second phase,the search is confined to the neighbourhood of some selected possible solutions to accurately solve with cluster radius decreasing slowly,meanwhile all clusters continuously move to better regions until all the peaks in the question space is searched.This algorithm can efficiently solve the combination problem.Taking the optimization on decision-making of aircraft maintenance by the algorithm for an example,maintenance which combines multiple parts or tasks can significantly enhance economic benefit when the halt cost is rather high.

关键词： cluster search genetic algorithm combinatorial optimization multi-part maintenance grouping maintenance.

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