摘要
针对复杂环境下农机设备的齿轮箱系统在故障诊断时存在易受现场噪声干扰和故障识别率低等问题,提出了一种基于改进的烟花算法和概率神经网络的齿轮箱智能故障诊断方法。为提高现有概率神经网络模式分类方法的性能,定义了一项样本相似度衡量指标以提高建模过程中训练样本的质量。将烟花算法与概率神经网络技术有机融合提出了一种改进的烟花算法-概率神经网络模式分类方法,利用烟花算法优化概率神经网络的平滑参数以确定网络参数的最优值,提高模式分类与识别精度。将改进的烟花算法-概率神经网络模式分类方法用于噪声环境下齿轮箱的故障诊断建模,构建故障特征参量与齿轮箱工作状况间的复杂非线性映射关系。应用结果表明,与基于BP神经网络、GABP(genetic algorithm back propagation)神经网络和概率神经网络的故障诊断模型相比,在不同程度噪声影响下烟花算法-概率神经网络模型均具有最高故障识别率。当噪声控制系数为0.01、0.02、0.04和0.06时,模型的故障识别率分别为100%、95.83%、93.33%和88.33%。该研究可为非线性复杂系统的故障诊断提供了一种可行的解决方案。
In the field of agricultural machine and equipment, gearbox was a key mechanical part that was widely applied in speed regulation and power transmission. The gearbox had a high fault rate in actual operating process due to the severe working condition and its complex configuration. State monitoring and fault diagnosing were of great significance to guarantee the safety and stability of gearbox. A fault diagnosis method based on the improved fireworks algorithm（FWA） and probabilistic neural network（PNN） was proposed to overcome the shortcomings, such as the sensitivity to environmental noise and low fault recognition rate, when conducting the fault diagnosis system of gearbox under complex operating conditions. To enhance the pattern classifying performance of traditional methods based on PNN, a new similarity measure for samples was defined, which made the quality of PNN training data increase in modeling process. An improved FWA-PNN classification method was proposed by combining FWA optimization algorithm with PNN technology. The FWA was applied to optimize the smoothing parameters of PNN to determine the optimal values of network parameters, and thus in some way the pattern classification and identification accuracy of PNN could be improved. The proposed FWA-PNN classification method was applied in fault diagnosis modeling for gearbox under noisy environment, and the complex non-linear mapping relationship between fault characteristic parameters and equipment working conditions was constructed. Experiments were carried out on JZQ250 gearbox in laboratory and the process of the fault diagnosis modeling was summarized as follows： At first, 6 working states of the gearbox that included normal state and 5 typical fault states were simulated during the experiments. And then the vibration signals of the gearbox were gained by using the accelerometers of signal acquisition system under different working conditions. After the pretreatment, time and frequency domain analysis of vibration signals were carried out and 27 time and frequency parameters reflecting the working status of the gearbox were obtained. Kernel principal component analysis（KPCA） method was applied to extract the features of the original high-dimensional data, and 7 characteristic parameters were selected as the fault feature vectors of gearbox at last. As a result, the original fault samples were generated according to the fault feature vectors. Given that the fault vibration signals of gearbox were easy to be interfered by different noises in practice, the fault modeling sample sets were regenerated by adding random noises of different levels in the original fault samples. Two thirds of the samples were randomly selected as the training data and the remaining samples were as the test data to establish fault diagnosis model for gearbox based on FWA-PNN. Next, in order to validate the effectiveness and robustness of this new model, BP（back propagation） neural network（BP-NN）, genetic algorithm based BP-NN（GABP-NN） and normal PNN methods were introduced to compare with the improved pattern classification method, and 4 different fault diagnosis models were built. The training results of different neural network models indicated that FWA-PNN had better performance in error convergence speed and precision than GABP-NN and BP-NN, which had an excellent fault tolerance and fault classification capability. Finally, 4 different models were applied in fault diagnosis and classification by using the noise samples as the test data. Comparison results indicated that FWA-PNN model could effectively improve the precision of fault detection due to the smooth parameters of all pattern categories optimized by FWA. Application results showed that by compared with the fault diagnosis models based on BPNN, GABPNN and traditional PNN, the FWA-PNN model had the highest fault recognition rate under different noise levels. In conclusion, a novel fault diagnosis program for nonlinear and complex mechanical systems is provided in this paper. It has good application prospects and popularized value in fault diagnosing for agricultural machinery and equipment.
作者
陈如清
李嘉春
尚涛
张俊
Chen Ruqing;Li Jiachun;Shang Tao;Zhang Jun(College of Mechanical and Electrical Engineering;Jiaxing University,Jiaxing 314001,Chin;2.College of Mathematics,Physics and Information Engineering,Jiaxing University,Jiaxing 314001,China;3.College of Biosystems Engineering and Food Science,Zhejiang University,Hangzhou 310058,Chin)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2018年第17期192-198,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
浙江省基础公益研究计划项目(LGG18F030011)
国家自然科学基金资助项目(61603154)
关键词
齿轮
算法
噪声
概率神经网络
故障诊断建模
gears
algorithms
noises
probabilistic neural network
fault diagnosis modeling
