JUST回转支承故障试验数据分析

doi:10.12382/bgxb.2023.0756

摘要/Abstract

摘要：

回转支承作为军用雷达的关键部件,对保证设备安全使用、提高效益具有重大作用。通过数据驱动方法对回转支承运行状态进行实时监测与诊断,已成为该技术领域的研究热点。然而回转支承面临服役工况复杂、故障试验样本稀少等问题,使其故障诊断技术研究一直饱受数据不足的困扰,也严重制约了军用机械装备寿命预测与健康管理技术的发展与应用。为此开展了回转支承故障试验,通过对回转支承运转过程中的多向振动、声发射信号进行采集,形成了故障试验数据集。该数据集包含回转支承在9种工况下的多向振动信号和声发射信号,且明确标注了回转支承的信号采集时间、故障标签、转速、负载、采集次数等多种相关信息,为基于数据驱动的回转支承故障诊断与雷达伺服系统健康管理提供数据支撑和技术保障。

关键词: 回转支承, 雷达, 数据驱动, 故障试验, 故障预测与健康管理

Abstract:

As a key component of military radar, the slewing bearing plays a significant role in ensuring the safe use of an equipment and improving its efficiency. The real-time monitoring and diagnosis of slewing bearing operation state through data-driven methods have become a hot research topic in this technical field. However, the slewing bearing is faced with the problems of complex service conditions and scarce failure test samples so that its fault diagnosis technology research has been plagued by the lack of data and the development and application of the prediction and health management technology of military machinery and equipment are seriously constrained. For this reason, the slewing bearing failure is tested, and a failure test data set is generated by collecting the multidirectional vibration and acoustic emission signals during the operation process of slewing bearing. The data set contains the vibration signals and acoustic emission signals of slewing bearing under nine working conditions, and the signal acquisition time, fault label, rotational speed, load, number of acquisitions and other related information of slewing bearing are clearly labeled, which provides data support and technical guarantee for the fault diagnosis of data-driven slewing bearing and the health management of radar servo system.

Key words: slewing bearing, radar, data-driven, failure test, failure prediction and health management

中图分类号:

TH133

周宏根, 任小蝶, 孙丽, 李国超, 文思钊, 彭展, 刘寅飞. JUST回转支承故障试验数据分析[J]. 兵工学报, 2024, 45(10): 3744-3753.

ZHOU Honggen, REN Xiaodie, SUN Li, LI Guochao, WEN Sizhao, PENG Zhan, LIU Yinfei. Study of JUST Slewing Bearing Failure Test Data[J]. Acta Armamentarii, 2024, 45(10): 3744-3753.

图/表 16

图1 回转支承故障试验平台

Fig.1 Slewing bearing failure test rigs

图2 传感器具体布局

Fig.2 Specific sensor layout

表1 111.10.100单排交叉圆柱式滚子回转支承具体参数

Table 1 Specific parameters of 111.10.100 single-row crossed cylindrical roller slewing rings

参数	数值	参数	数值
外径/mm	135	总高度/mm	22
内径/mm	65	内外圈高度/mm	20
外孔距/mm	120	孔数	8
内孔距/mm	80	丝孔/mm	M8
齿宽/mm	20	齿顶圆直径/mm	144
模数	2	重量/kg	1.5
齿数	70	滚子个数	52

表2 回转支承故障试验设计工况

Table 2 Fault test and design condition of slewing bearing

工况编号	输出转速/(r·min^-1)	倾覆力/N
1	2	0
2	2	30
3	2	60
4	6	0
5	6	30
6	6	60
7	12	0
8	12	30
9	12	60

表3 传感器性能指标

Table 3 Sensor performance indexes

传感器类型	传感器型号	性能参数
单向传感器	8702B100	x轴灵敏度:49.57mV/g、 49.38mV/g、48.93mV/g; 测量范围:±100g 温度范围:-54~100℃
三向传感器	8763B100BB	x轴灵敏度:51.40mV/g; y轴灵敏度:50.13mV/g; z轴灵敏度:51.01mV/g; 测量范围:±100g; 温度范围:-54~100℃
声发射传感器	8152C0050511	灵敏度:57dB; 频率范围:50~400kHz; 温度范围:-55~165℃
声发射安装磁座	8443B	电压:24V

图3 DEWESOFT X3信号采集器

Fig.3 DEWESOFT X3 signal collector

图4 回转支承4种不同状态类型

Fig.4 Four different state types of slewing bearing

表4 回转支承故障试验数据集一览表

Table 4 List of data set of slewing bearing fault test

工况编号	回转支承状态	采样次数	总采样数
	N
1	B1	5	20
	I
	O
	N
2	B1	5	20
	I
	O
	N
3	B1	5	20
	I
	O
	N
4	B1	5	20
	I
	O
	N
5	B1	5	20
	I
	O
	N
6	B1	5	20
	I
	O
	N
7	B1	5	20
	I
	O
	N
8	B1	5	20
	I
	O
	N
9	B1	5	20
	I
	O

图5 工况9水平方向振动信号的特征提取

Fig.5 Feature extraction of horizontal vibration signal in Condition 9

图6 N状态回转支承水平方向振动信号时域波形图

Fig.6 Time-domain waveform of horizontal vibration signal of slewing bearing under Condition N

图7 I状态回转支承水平方向振动信号时域波形图

Fig.7 Time-domain waveform of horizontal vibration signal of slewing bearing under Condition I

图8 O状态回转支承水平方向振动信号时域波形图

Fig.8 Time-domain waveform of horizontal vibration signal of slewing bearing under Condition O

图9 B1状态回转支承水平方向振动信号时域波形图

Fig.9 Time-domain waveform of horizontal vibration signal of slewing bearing under Condition B1

图10 B1状态回转支承水平振动信号频谱分析

Fig.10 Frequency spectrum analysis of horizontal vibration signal of slewing bearing under Condition B1

图11 不同算法识别准确率柱状图

Fig.11 Histogram of recognition accuracies of different algorithms

图12 训练样本特征的2D可视化

Fig.12 2D Visualization of training sample features

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