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摘要

在现代工业生产设备不断朝着结构化、自动化和智能化方向发展的过程中，

电机仍是主要的动力输出设备。若电机在运行过程中出现故障，会导致其运行效

率降低，系统能耗上升等问题，严重时甚至造成电机损坏，使整体系统设备长时

间停机维修，造成严重的经济损失。因此，研究电机智能故障诊断技术，对保障

生产设备高效运行的稳定性、可靠性具有重要意义。随着科技的不断创新和发展，

信号处理、人工智能等技术不断取得突破，故障诊断技术也更加精确化、智能化。

本文结合实际生产过程中常见的电机变工况和强噪声环境下的故障诊断问题，在

分析故障产生机理的基础上对电机智能故障诊断方法展开深入研究。

(1) 首先利用试验台采集的振动信号对电机不同故障的产生机理进行分析，

探究其处于故障状态时的振动频率特性。在此基础上，研究基于信号处理和机器

学习算法的故障诊断方法，分析经验模态分解 (Empirical Mode

Decomposition,EMD)存在的优势和不足，利用其改进算法集合经验模态分解

(Ensemble Empirical Mode Decomposition,EEMD)对信号进行分解，得到反映原始

信号不同频率成分的本征模态函数(Intrinsic Mode Function.IMF)分量，通过相关

性与原始信号相关系数较高前 4 阶 IMF 分量，最后对其进行谱分析得到多个序

列作为样本信号用于特征提取。

(2) 对多序列样本信号中 9 种不同的时、频域统计特征进行计算，得到原始

特征集，并在对其采用聚类算法分析的基础上，提出一种基于调整互信息和标准

差的敏感特征选择方法，从原始特征集筛选特征构建敏感特征集用于电机故障诊

断。并且，针对特征集中存在的特征干扰、冗余等问题，提出利用特征降维方法

实现对特征的维数约简。最后分别利用支持向量机(Support Vector Machines,SVM)

和极限学习机(Extreme Learning Machine,ELM)两种较为流行的机器学习算法实

现电机故障诊断，并通过对比实验进行验证。

(3) 针对基于信号处理的传统智能故障诊断方法中存在的流程复杂、人为干

预过多的问题，研究端到端式的卷积神经网络算法用于电机故障诊断。针对一维

时序信号的特点，分析一维卷积神经网络(One-Dimensional Convolutional Neural

Network,1D-CNN)在以原始一维振动信号为基础进行故障诊断的优势。为了提取

III

Abstract

In the process of modern industrial production equipment continuously moving

towards the direction of structure, automation and intelligence, motors are still the

main power output equipment. Failure of the motor during operation will cause

problems such as reduced operating efficiency and increased system energy

consumption. In severe cases, the motor will be damaged, causing the overall system

equipment to be stopped for maintenance for a long time, causing serious economic

losses. Therefore, the research of motor intelligent fault diagnosis technology is of

great significance for ensuring the stability and reliability of efficient operation of

production equipment. With the continuous innovation and development of science

and technology, breakthroughs have been made in signal processing, artificial

intelligence and other technologies, and fault diagnosis technology has become more

precise and intelligent. The thesis combines the common fault diagnosis problems of

the motor in the actual production process and the fault diagnosis in the strong noise

environment. Based on the analysis of the failure mechanism, the intelligent fault

diagnosis method of the motor is studied in depth.

(1) Firstly, the intelligent fault diagnosis method based on signal processing is

studied, the mechanism of motor fault generation based on vibration signals is

explored. Based on the analysis of the motor fault characteristics under different

operating states, the signal analysis method based on empirical mode decomposition

is studied, and aimed at it Existing modal aliasing is proposed to use set empirical

modal decomposition to analyze the motor vibration signals, select the first 4 orders

of IMF components through correlation analysis and calculate the corresponding

envelope spectrum and marginal spectrum as the feature extracted signal sequence.

(2) Calculate 9 different time and frequency domain statistical features in the

multi-sequence sample signal to obtain the original feature set. Based on the analysis

of the clustering algorithm, a sensitive feature based on adjusting mutual information

and standard deviation is proposed. Select a method to filter features from the original

feature set to construct a sensitive feature set for motor fault diagnosis. In addition, in

view of the problems of feature interference and redundancy in the feature set, a

feature dimension reduction method is proposed to reduce the feature dimension.

Finally, two more popular machine learning algorithms, Support Vector Machines and

Extreme Learning Machine, are used to implement motor fault diagnosis and verified

by comparison experiments.

(3) Aiming at the problems of complicated processes and excessive human

intervention in traditional intelligent fault diagnosis methods based on signal

processing, the advantages of one-dimensional convolutional neural networks on

one-dimensional time series are analyzed, and faults based on end-to-end 1DCNN

networks are studied. A diagnostic method and a multi-scale feature fusion framework

using convolutional layers with different scale kernels are proposed. A multi-scale

one-dimensional convolutional neural network based motor fault diagnosis method is

constructed. Finally, the superiority of the proposed method under variable operating

conditions and noise interference is verified through experiments.

(4) In order to improve the recognition efficiency and accuracy of the fault

diagnosis method under variable operating conditions and strong noise interference

environment, a multi-scale feature fusion framework is built on the basis of the

residual network structure. Study the algorithm implementation principle of attention

mechanism, design the attention module suitable for one-dimensional residual

network, and embed it in the residual module to build a multi-scale attention residual

network model. Finally, the validity and superiority of the proposed model are

verified using experimental bench data.

The article has 40 figures, 18 tables and 92 references.

Keywords: motor; fault diagnosis; multi-scale features; convolutional neural network;

attention mechanism

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