摘要
随着工业自动化水平的不断提升，电动机作为关键动力设备，其运行状态直接影响生产效率与安全性，因此对电动机故障进行实时监测和诊断具有重要意义。本研究旨在设计一种基于可编程逻辑控制器（PLC）的电动机故障诊断系统，通过集成信号采集、数据处理及故障识别功能，实现对电动机运行状态的高效监控为达成这一目标，系统采用模块化设计思路，利用PLC的高速数据采集能力结合现代信号处理算法，构建了从传感器数据获取到故障模式分析的完整技术链条具体而言，通过在电动机关键部位布置振动、温度等传感器，采集运行过程中的多维特征数据，并借助PLC内置程序完成初步的数据筛选与预处理在此基础上，引入支持向量机（SVM）等智能算法对数据进行深度挖掘，从而准确判断电动机是否存在故障及其类型实验结果表明，该系统能够以高精度识别多种常见故障类型，诊断准确率超过95%，且具备良好的实时性和稳定性此外，系统还支持远程监控与预警功能，便于操作人员及时采取措施综上所述，本研究提出的基于PLC的电动机故障诊断系统不仅有效提升了故障诊断的智能化水平，还为工业现场提供了实用性强的技术解决方案，具有重要的应用价值和推广前景
关键词：电动机故障诊断；可编程逻辑控制器（PLC）；支持向量机（SVM）；实时监测；信号处理算法
Abstract
With the continuous improvement of industrial automation, electric motors, as critical power equipment, have a direct impact on production efficiency and safety. Therefore, real-time monitoring and diagnosis of electric motor faults are of great significance. This study aims to design an electric motor fault diagnosis system based on a programmable logic controller (PLC), which integrates signal acquisition, data processing, and fault identification to achieve efficient monitoring of the motor's operating status. To achieve this goal, the system adopts a modular design approach, utilizing the high-speed data acquisition capability of the PLC combined with modern signal processing algorithms to construct a complete technical chain from sensor data acquisition to fault pattern analysis. Specifically, vibration, temperature, and other sensors are deployed at key locations of the motor to collect multi-dimensional feature data during operation, and preliminary data screening and preprocessing are performed using built-in PLC programs. On this basis, intelligent algorithms such as support vector machines (SVM) are introduced to conduct in-depth data mining, thereby accurately determining whether the motor has a fault and identifying its type. Experimental results indicate that the system can identify multiple common fault types with high precision, achieving a diagnostic accuracy rate exceeding 95%, while demonstrating excellent real-time performance and stability. Additionally, the system supports remote monitoring and early warning functions, facilitating timely action by operators. In summary, the PLC-based electric motor fault diagnosis system proposed in this study not only effectively enhances the level of intelligent fault diagnosis but also provides a highly practical technical solution for industrial applications, showcasing significant application value and potential for promotion.
Keywords：Electric Motor Fault Diagnosis; Programmable Logic Controller (Plc); Support Vector Machine (Svm); Real-Time Monitoring; Signal Processing Algorithm
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法概述 2
二、PLC技术与电动机故障诊断基础 2
(一) 电动机常见故障类型分析 2
(二) 故障诊断的核心技术要点 3
三、基于PLC的系统设计框架 3
(一) 系统设计目标与功能需求 3
(二) 系统硬件架构设计 4
(三) 系统软件逻辑设计 4
(四) 关键技术实现路径 5
四、系统测试与性能优化 6
(一) 测试方案设计与实施 6
(二) 测试结果分析与评估 6
(三) 性能优化策略探讨 7
(四) 系统可靠性验证 7
结 论 9
参考文献 10
随着工业自动化水平的不断提升，电动机作为关键动力设备，其运行状态直接影响生产效率与安全性，因此对电动机故障进行实时监测和诊断具有重要意义。本研究旨在设计一种基于可编程逻辑控制器（PLC）的电动机故障诊断系统，通过集成信号采集、数据处理及故障识别功能，实现对电动机运行状态的高效监控为达成这一目标，系统采用模块化设计思路，利用PLC的高速数据采集能力结合现代信号处理算法，构建了从传感器数据获取到故障模式分析的完整技术链条具体而言，通过在电动机关键部位布置振动、温度等传感器，采集运行过程中的多维特征数据，并借助PLC内置程序完成初步的数据筛选与预处理在此基础上，引入支持向量机（SVM）等智能算法对数据进行深度挖掘，从而准确判断电动机是否存在故障及其类型实验结果表明，该系统能够以高精度识别多种常见故障类型，诊断准确率超过95%，且具备良好的实时性和稳定性此外，系统还支持远程监控与预警功能，便于操作人员及时采取措施综上所述，本研究提出的基于PLC的电动机故障诊断系统不仅有效提升了故障诊断的智能化水平，还为工业现场提供了实用性强的技术解决方案，具有重要的应用价值和推广前景
关键词：电动机故障诊断；可编程逻辑控制器（PLC）；支持向量机（SVM）；实时监测；信号处理算法
Abstract
With the continuous improvement of industrial automation, electric motors, as critical power equipment, have a direct impact on production efficiency and safety. Therefore, real-time monitoring and diagnosis of electric motor faults are of great significance. This study aims to design an electric motor fault diagnosis system based on a programmable logic controller (PLC), which integrates signal acquisition, data processing, and fault identification to achieve efficient monitoring of the motor's operating status. To achieve this goal, the system adopts a modular design approach, utilizing the high-speed data acquisition capability of the PLC combined with modern signal processing algorithms to construct a complete technical chain from sensor data acquisition to fault pattern analysis. Specifically, vibration, temperature, and other sensors are deployed at key locations of the motor to collect multi-dimensional feature data during operation, and preliminary data screening and preprocessing are performed using built-in PLC programs. On this basis, intelligent algorithms such as support vector machines (SVM) are introduced to conduct in-depth data mining, thereby accurately determining whether the motor has a fault and identifying its type. Experimental results indicate that the system can identify multiple common fault types with high precision, achieving a diagnostic accuracy rate exceeding 95%, while demonstrating excellent real-time performance and stability. Additionally, the system supports remote monitoring and early warning functions, facilitating timely action by operators. In summary, the PLC-based electric motor fault diagnosis system proposed in this study not only effectively enhances the level of intelligent fault diagnosis but also provides a highly practical technical solution for industrial applications, showcasing significant application value and potential for promotion.
Keywords：Electric Motor Fault Diagnosis; Programmable Logic Controller (Plc); Support Vector Machine (Svm); Real-Time Monitoring; Signal Processing Algorithm
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法概述 2
二、PLC技术与电动机故障诊断基础 2
(一) 电动机常见故障类型分析 2
(二) 故障诊断的核心技术要点 3
三、基于PLC的系统设计框架 3
(一) 系统设计目标与功能需求 3
(二) 系统硬件架构设计 4
(三) 系统软件逻辑设计 4
(四) 关键技术实现路径 5
四、系统测试与性能优化 6
(一) 测试方案设计与实施 6
(二) 测试结果分析与评估 6
(三) 性能优化策略探讨 7
(四) 系统可靠性验证 7
结 论 9
参考文献 10
