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Quantitative Damage Detection for Planetary Gear Sets Based on Physical Models

Chinese Journal of Mechanical Engineering 2012年第1期25卷 190-196页

作者： CHENG Zhe hu niaoqing Institute of Mechatronics Engineering National University of Defense TechnologyChangsha 410073China

Planetary gear set is the critical component in helicopter transmission train, and an important problem in condition monitoring and health management of planetary gear set is quantitative damage detection. In order to resolve this problem, an approach based on physical models is presented to detect damage quantitatively in planetary gear set. A particular emphasis is put on a feature generation and selection method, which is used for sun gear tooth breakage damage detection quantitatively in planetary gear box of helicopter transmission system. In this feature generation procedure, the pure torsional dynamical models of 2K-H planetary gear set is established for healthy case and sun gear tooth-breakage case. Then, a feature based on the spectrum of simulation signals of the dynamical models is generated. Aiming at selecting the best feature suitable for quantitative damage detection, a two-sample Z-test procedure is used to analyze the performance of features on damage evolution tracing. A feature named SR, which had better performance in tracking damage, is proposed to detect damage in planetary gear set. Meanwhile, the sun gear tooth-chipped seeded experiments with different severity are designed to validate the method above, and then the test vibration signal is picked up and used for damage detection. With the results of several experiments for quantitative damage detection, the feasibility and the effect of this approach are verified. The proposed method can supply an effective tool for degradation state identification in condition monitoring and health management of helicopter transmission system.

关键词： planetary gear sets physical model quantitative detection feature extraction feature selection

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Turbopump Condition Monitoring Using Incremental Clustering and One-class Support Vector Machine

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Chinese Journal of Mechanical Engineering 2011年第3期24卷 474-479页

作者： hu Lei hu niaoqing QIN Guojun GU Fengshou College of Mechatronics Engineering andAutomation National University of Defense Technology Changsha 410073 China University ofhuddersfield Huddersfield QueensgateHD13DH UK

Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition *** it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection ***-class support vector machine （OCSVM） that has been commonly used for novelty detection cannot deal well with large scale *** order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump,a monitoring method that integrates OCSVM with incremental clustering is *** this method,the incremental clustering is used for sample reduction by extracting representative vectors from a large training *** representative vectors are supposed to distribute uniformly in the object region and fulfill the *** training OCSVM on these representative vectors yields a novelty *** applying this method to the analysis of the turbopump＇s historical test data,it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors,and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding,rub-impact and sensor *** monitoring method does not need fault samples during training as classical recognition *** method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump.

关键词： novelty detection condition monitoring incremental clustering one-class support vector machine turbopump

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Enhanced Detection of Rolling Element Bearing Fault Based on Stochastic Resonance

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Chinese Journal of Mechanical Engineering 2012年第6期25卷 1287-1297页

作者： ZHANG Xiaofei hu niaoqing CHENG Zhe hu Lei Key Laboratory of Science and Technology on Integrated Logistics Support National University of Defense TechnologyChangsha 410073China

Early bearing faults can generate a series of weak impacts. All the influence factors in measurement may degrade the vibration signal. Currently, bearing fault enhanced detection method based on stochastic resonance（SR） is implemented by expensive computation and demands high sampling rate, which requires high quality software and hardware for fault diagnosis. In order to extract bearing characteristic frequencies component, SR normalized scale transform procedures are presented and a circuit module is designed based on parameter-tuning bistable SR. In the simulation test, discrete and analog sinusoidal signals under heavy noise are enhanced by SR normalized scale transform and circuit module respectively. Two bearing fault enhanced detection strategies are proposed. One is realized by pure computation with normalized scale transform for sampled vibration signal, and the other is carried out by designed SR hardware with circuit module for analog vibration signal directly. The first strategy is flexible for discrete signal processing, and the second strategy demands much lower sampling frequency and less computational cost. The application results of the two strategies on bearing inner race fault detection of a test rig show that the local signal to noise ratio of the characteristic components obtained by the proposed methods are enhanced by about 50% compared with the band pass envelope analysis for the bearing with weaker fault. In addition, helicopter transmission bearing fault detection validates the effectiveness of the enhanced detection strategy with hardware. The combination of SR normalized scale transform and circuit module can meet the need of different application fields or conditions, thus providing a practical scheme for enhanced detection of bearing fault.

关键词： bearing fault stochastic resonance normalized scale transform nonlinear bistable system

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Multi-scale bistable stochastic resonance array: A novel weak signal detection method and application in machine fault diagnosis

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Science China(Technological Sciences) 2013年第9期56卷 2115-2123页

作者： ZHANG XiaoFei hu niaoqing hu Lei CHENG Zhe Key Laboratory of Science and Technology on Integrated Logistics Support School of Mechatronics Engineering & Automation National University of Defense Technology

Weak signal detection based on stochastic resonance （SR） can hardly succeed when noise intensity exceeds the optimal value of SR. This paper explores a novel parallel bistable SR array mechanism by decomposed multi-scale noises from input signal. A smoother output with lower noise is obtained from the combination of colored noise SR ellect and parallel bistable SR array. The influence of noise intensity and array size on the SR effect and output noise intensity is analyzed through numerical simu- lation. A signal detection method based on the new SR mechanism and normalized scale transform is proposed for the case of heavy background noise. Simulation is conducted to confirm the effectiveness of parameter tuning and amplitude tuning of normalized scale transform on the proposed SR model. The proposed method has three advantages： the input noise intensity of each unit is reduced by wavelet decomposition; the output noise level decreases due to array ensemble average; the SR effect of each unit is optimized by normalized scale transform for high frequency signal. Experiment on bearing inner and outer race fault diagnosis has verified the effectiveness and advantages of the proposed SR model in comparison with traditional SR method and kurlogram.

关键词： weak signal detection stochastic resonance multi-scale array fault diagnosis bearing

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FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

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Chinese Journal of Mechanical Engineering 2007年第5期20卷 92-95页

作者： LIU Guanjun LIU Xinmin QIU Jing hu niaoqing College of Mechatronics Engineering and Automation National University of Defense Technology Changsha 410073 China

Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox＇s faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.

关键词： Hidden Markov model Support vector machine Fault diagnosis

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Research on Remaining Useful Life Prediction Method of Rolling Bearing Based on Health Indicator Extraction and Trajectory Enhanced Particle Filter

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Journal of Dynamics, Monitoring and Diagnostics 2022年第2期1卷 66-83页

作者： Peng Luo Jiao hu Lun Zhang niaoqing hu Zhengyang Yin Laboratory of Science and Technology on Integrated Logistics Support National University of Defense TechnologyChangsha 410073China College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China

Aiming at the difficulty of mining fault prognosis starting points and constructing prognostic models for remaining useful life(RUL)prediction of rolling bearings,a RUL prediction method is proposed based on health indicator(HI)extraction and trajectory-enhanced particle filter(TE-PF).By extracting a HI that can accurately track the trending of bearing degradation and combining it with the early fault enhancement technology,early abnormal sample nodes can be mined to provide more samples with fault information for the construction and training of subsequent prediction *** at the problem that traditional degradation rate models based on PF are vulnerable to HI mutations,a TE-PF prediction method is proposed based on comprehensive utilization of historical degradation information to timely modify prediction model *** from a rolling bearing prognostic study show that prediction starting points can be accurately detected and a reasonable prediction model can be conveniently constructed by the RUL prediction method based on HI amplitude abnormal detection and ***,aiming at the RUL prediction problem under the condition of HI mutation,RUL prediction with probability and statistics characteristics under a confidence interval can be obtained based on the method proposed.

关键词： health indicator prediction model prediction starting point remaining useful life trajectory-enhanced particle filter

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退化速率跟踪粒子滤波在剩余使用寿命预测中的应用

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国防科技大学学报 2015年第3期37卷 161-166页

作者：范彬胡雷胡茑庆国防科技大学装备综合保障技术重点实验室湖南长沙410073

毋庸置疑,剩余使用寿命预测对于设备的健康管理越来越重要。近年来粒子滤波方法被越来越多地应用到设备寿命预测技术当中,这是因为粒子滤波方法能更好地解决非线性非高斯系统滤波问题,而且能够获得不确定度信息。但该方法的预测性能却... 详细信息

毋庸置疑,剩余使用寿命预测对于设备的健康管理越来越重要。近年来粒子滤波方法被越来越多地应用到设备寿命预测技术当中,这是因为粒子滤波方法能更好地解决非线性非高斯系统滤波问题,而且能够获得不确定度信息。但该方法的预测性能却过度依赖于预测模型,并且对于模型参数的初始分布也比较敏感,这在一定程度上限制了粒子滤波预测方法的进一步发展。针对基本粒子滤波预测方法的不足,提出了一种基于退化速率跟踪粒子滤波的通用预测框架,以历史观测数据的退化速率统计规律作为指导来跟踪目标数据的退化速率,实现对粒子滤波预测方法的简化,并将该方法用于轴承和锂离子电池的剩余使用寿命预测,验证了方法的有效性。

关键词：粒子滤波退化速率跟踪剩余使用寿命预测框架

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变工况下轴承健康监测的相关向量机与自适应阈值模型方法

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国防科技大学学报 2016年第1期38卷 168-174页

作者：胡雷范彬胡茑庆高明国防科技大学机电工程与自动化学院湖南长沙410073 国防科技大学装备综合保障技术重点实验室湖南长沙410073

工况在旋转机械运行过程中通常是变化的。变化的工况和故障一样,也会引起机械振动特征发生改变,从而引起诊断误差。为此,提出一种用于变工况下轴承健康监测的新方法。该方法使用相关向量机拟合振动特征的统计量随工况参数的变化,得到特... 详细信息

工况在旋转机械运行过程中通常是变化的。变化的工况和故障一样,也会引起机械振动特征发生改变,从而引起诊断误差。为此,提出一种用于变工况下轴承健康监测的新方法。该方法使用相关向量机拟合振动特征的统计量随工况参数的变化,得到特征统计量与工况参数之间的连续函数关系;基于不同工况下的特征统计,构建自适应阈值模型。将该方法用于不同转速下的轴承健康监测,结果表明,当转速超过某一个较小的值时,该方法有效。

关键词：健康监测相关向量机自适应阈值变工况轴承

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基于FSDP图像和DCNN的无人机起落架关键件结构损伤智能检测方法

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中国科学：技术科学 2023年第8期53卷 1372-1384页

作者：程哲杨翼胡茑庆国防科技大学智能科学学院长沙410073 国防科技大学装备综合保障技术重点实验室长沙410073

随着无人机的应用越来越广泛,与之相适应的保障手段也需与时俱进.在人工智能快速发展的情况下,故障诊断与人工智能方法相结合为无人机保障关键技术的跨越式发展提供了重要契机,也为无人机的使用安全性与运行可靠性水平的提升提供了重要... 详细信息

随着无人机的应用越来越广泛,与之相适应的保障手段也需与时俱进.在人工智能快速发展的情况下,故障诊断与人工智能方法相结合为无人机保障关键技术的跨越式发展提供了重要契机,也为无人机的使用安全性与运行可靠性水平的提升提供了重要机遇.本文以结构健康监测中的超声信号分析方法为基础,针对无人机起落架关键结构件的损伤检测问题开展研究,结合深度学习模型,对结构件的损伤进行智能化检测,提高检测效率和检测精度.针对结构健康监测中损伤信号的非线性特点,提出了频谱对称点阵图案(frequency symmetrized dot pattern,FSDP)特征提取方法.在此基础上,提出了基于FSDP特征与深度卷积神经网络的损伤智能检测方法.在起落架T型构件实验环境中开展了结构损伤模拟与检测实验,通过实验数据验证了所提出的智能检测方法的有效性.

关键词：无人机起落架损伤检测 FSDP图像深度卷积神经网络

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Jeffcott转子碰摩故障试验研究

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振动工程学报 2001年第1期14卷 96-99页

作者：刘耀宗胡茑庆国防科技大学机电工程研究所长沙410073

针对工程实际中遇到的机组转子碰摩故障开展了试验研究。通过大量的试验 ,观测到了工作转速低于一阶临界转速时 ,转定子局部碰摩引起的倍频振动 ;高于一阶临界转速时 ,局部碰摩引起的分频振动以及某些转速段内出现的异频伪共振现象。试... 详细信息

针对工程实际中遇到的机组转子碰摩故障开展了试验研究。通过大量的试验 ,观测到了工作转速低于一阶临界转速时 ,转定子局部碰摩引起的倍频振动 ;高于一阶临界转速时 ,局部碰摩引起的分频振动以及某些转速段内出现的异频伪共振现象。试验结果与基于碰摩力模型的仿真结果定性一致。试验还表明 ,异频伪共振是高速工作的转子发生碰摩时的主要振动成分 ,这一结论对诊断和抑制高速转子的异常超标振动有积极意义。

关键词： Jeffcott转子碰摩故障试验非线性振动倍频振动分频振动

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