• 论文 •

### 基于故障树和混沌粒子群算法的锻压机床故障诊断方法

1. 1. 安徽科技学院机械工程学院,凤阳 233100; 2. 安徽工业大学机械工程学院,马鞍山 243000
• 出版日期:2020-01-25 发布日期:2020-04-29

CHU Kun, ZHANG Chunyu, CHEN Lei, CAO Qiang. Fault Diagnosis Method of Forging Machine Based on Fault Tree and Chaos Particle Swarm Algorithm[J]. Journal of Systems Science and Mathematical Sciences, 2020, 40(1): 180-190.

### Fault Diagnosis Method of Forging Machine Based on Fault Tree and Chaos Particle Swarm Algorithm

CHU Kun1, ZHANG Chunyu1 ,CHEN Lei2 ,CAO Qiang1

1. 1. College of Mechanical Engineering, University of Science and Technology of Anhui, Fengyang 233100; 2. School of Mechanical Engineering, Anhui University of Technology, MaAnshan 243000
• Online:2020-01-25 Published:2020-04-29

Forging machine is widely used in various fields due to its high production efficiency and high utilization rate of material. However, when the forging machine breaks down, it is very difficult to diagnose the fault source of the machine because of the wide variety of faults and the large amount of fault data. To solve this problem, a new diagnostic method combining a fusion algorithm of fault tree and chaotic particle swarm algorithm was proposed, using which the fault source of forging machine can be diagnosed quickly and accurately. Firstly, the fault mode and probability of the forging machine are obtained by fault tree analysis, and then the learning samples of the fault pattern can be summarized according to the fault pattern and the known fault maintenance experience. Finally, the exact location of forging machine faults can be determined quickly using the probability of faults and the ergodic of the particle swarm optimization algorithm. The fault diagnosis experiments were performed on the servo system of forging machine by using the proposed method, and the experimental results was compared with that obtained both from the genetic algorithm and the particle swarm optimization algorithm. It was found that the proposed method has higher accuracy and faster speed in fault diagnosis of the servo system of forging machine.

()
 [1] 谢佩军, 高婷婷, 叶宏武. 量子粒子群优化核极限学习机的船舶变压器故障诊断[J]. 系统科学与数学, 2021, 41(7): 1807-1816. [2] 王春兰，甘旭升，李双峰，孟祥伟. 基于KSLPP特征提取的ELM模拟电路软故障诊断[J]. 系统科学与数学, 2020, 40(9): 1662-1671. [3] 吴迪，林国汉，胡慧，杜先君. 基于指数正则化零空间鉴别分析的故障识别[J]. 系统科学与数学, 2018, 38(10): 1128-1139.