摘要
Modern agricultural mechanization has put forward higher requirements for the intelligent defect diagnosis.However,the fault features are usually learned and classified under all speeds without considering the effects of speed fluctuation.To overcome this deficiency,a novel intelligent defect detection framework based on time-frequency transformation is presented in this work.In the framework,the samples under one speed are employed for training sparse filtering model,and the remaining samples under different speeds are adopted for testing the effectiveness.Our proposed approach contains two stages:1)the time-frequency domain signals are acquired from the mechanical raw vibration data by the short time Fourier transform algorithm,and then the defect features are extracted from time-frequency domain signals by sparse filtering algorithm;2)different defect types are classified by the softmax regression using the defect features.The proposed approach can be employed to mine available fault characteristics adaptively and is an effective intelligent method for fault detection of agricultural equipment.The fault detection performances confirm that our approach not only owns strong ability for fault classification under different speeds,but also obtains higher identification accuracy than the other methods.
现代农业机械化对农机使用过程中的故障诊断提出了更高的要求。然而,故障特征通常是在所有转速下进行学习和分类的,而没有考虑转速波动的影响。为了克服这一缺陷,本文提出了一种基于时频变换的智能故障诊断新框架。在该框架中,一种转速下的样本用来训练稀疏滤波,然后其他转速下的样本用来测试稀疏滤波的性能。本文提出的方法包括两个阶段:1)对机械原始振动数据进行短时傅里叶变换(STFT),得到时频域信号,然后利用稀疏滤波模型从时频信号中提取故障特征。2)基于学习到的故障特征,利用softmax回归对不同的机械健康状况进行分类。提出方法可以用来自适应的提取故障特征,是一种可对农业机械进行有效故障诊断的智能方法。故障诊断结果表明,该方法不仅在不同转速下的故障诊断中下具有较强优势,而且比其他方法具有更高的分类准确率。
作者
ZHANG Zhong-wei
CHEN Huai-hai
LI Shun-ming
WANG Jin-rui
张忠伟;陈怀海;李舜酩;王金瑞(State Key Laboratory of Mechanics and Control of Mechanical Structures,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;College of Energy and Power Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
基金
Project(51675262)supported by the National Natural Science Foundation of China
Project(2016YFD0700800)supported by the National Key Research and Development Program of China
Project(6140210020102)supported by the Advance Research Field Fund Project of China
Project(NP2018304)supported by the Fundamental Research Funds for the Central Universities,China
Project(2017-IV-0008-0045)supported by the National Science and Technology Major Project
作者简介
Corresponding author:CHEN Huai-hai,PhD,Professor;Tel:+86-13705161051;E-mail:chhnuaa@nuaa.edu.cn.
