有限空间爆炸静态压力的温度补偿方法

张龙; 邹虹; 张宝国; 张继军; 张东亮; 孔德骞

doi:10.11883/bzycj-2019-0234

有限空间爆炸静态压力的温度补偿方法

doi: 10.11883/bzycj-2019-0234

西北核技术研究所，陕西西安 710024

详细信息

作者简介:
张　龙（1992－　），男，硕士，助理工程师，zhanglonglxy@163.com

中图分类号: O389；TP212
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- 被引次数: 0
出版历程
- 收稿日期: 2019-06-12
- 修回日期: 2019-08-17
- 刊出日期: 2020-03-01

A temperature compensation method for explosion static pressure in finite space

Northwest Institute of Nuclear Technology, Xi’an 710024, Shaanxi, China

摘要

摘要: 为改善压阻式压力传感器的温度漂移特性，构建了基于遗传算法和小波神经网络的压力传感器温度补偿模型。针对小波神经网络收敛速度慢且易陷入局部最优解的问题，采用遗传算法对小波神经网络的连接权值、伸缩参数和平移参数进行优化。基于压力传感器的标定数据，分别采用BP神经网络、小波神经网络和遗传小波神经网络对其进行温度补偿研究，结果表明：遗传小波神经网络兼容了小波分析的时频局部特性和神经网络的自学习能力，表现出良好的收敛速度和补偿精度，经补偿后传感器的输出值更接近于标定值，其最大误差由−17.44 kPa变至0.38 kPa，最大相对误差由−14.0%变至0.38%。将该模型应用于有限空间爆炸静态压力的温度补偿中，取得了较好的实际应用效果。
- 爆炸静态压力 /
- 压阻式压力传感器 /
- 温度补偿 /
- 遗传小波神经网络
Abstract: To improve the temperature drift characteristics of piezoresistive pressure sensors, a temperature compensation model for the pressure sensors was constructed based on genetic algorithm and wavelet neural networks. By considering the problems of slow convergence and high probability of the local optimal solutions of the wavelet neural networks, the genetic algorithm was applied to optimize the connection weights, expansion parameters and translation parameters of the wavelet neural networks. Based on the calibration data of the pressure sensors, the BP neural network, wavelet neural network and genetic wavelet neural network were used to study the temperature compensation, respectively. The results show that the genetic wavelet neural network was compatible with the time-frequency local characteristics of the wavelet analysis and the self-learning ability of the neural networks, showing high convergence speed and compensation accuracy. After the compensation, the output values of the sensors were closer to the calibration ones. The maximum error was changed from −17.44 kPa to 0.38 kPa, and the maximum relative error was changed from −14.0% to 0.38%. The constructed model is applied in the temperature compensation of explosion static pressure in finite space, and the practical effect is good.
- explosion static pressure /
- piezoresistive pressure sensor /
- temperature compensation /
- genetic wavelet neural network

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图 1 3层小波神经网络结构

Figure 1. Three-layer wavelet neural network

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图 2 传感器输出值的相对误差曲线

Figure 2. Relative error curves of sensor output values

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图 3 BP神经网络模型补偿误差

Figure 3. Compensation errors of the BP neural network model

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图 4 小波神经网络模型补偿误差

Figure 4. Compensation errors of the wavelet neural network model

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图 5 遗传小波神经网络模型补偿误差

Figure 5. Compensation errors of the genetic wavelet neural network model

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图 6 补偿后传感器输出值的相对误差曲线

Figure 6. Relative error curves of sensor output values after compensation

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图 7 传感器防护装置

Figure 7. The sensor protection device

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图 8 爆炸静态压力的温度补偿结果

Figure 8. Temperature compensation results of explosion static pressure

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表 1 传感器标定数据

Table 1. The sensor calibration data

标定压力/kPa	不同标定温度下的输出值/kPa
标定压力/kPa	20 ℃	30 ℃	40 ℃	50 ℃	60 ℃	70 ℃	80 ℃
100	99.56	98.19	97.25	95.69	94.13	90.69	86.00
150	149.44	148.19	146.94	145.38	143.50	140.06	135.69
200	199.13	198.19	197.25	195.38	193.19	189.75	185.06
250	249.13	248.19	247.25	245.38	243.19	239.75	234.75
300	299.13	298.50	297.25	295.38	293.19	289.44	284.75
350	349.13	348.19	346.94	345.38	342.88	339.13	334.13
400	399.13	398.50	396.94	395.38	392.88	389.13	383.81
450	448.81	448.19	446.94	445.38	442.88	439.13	433.50
500	499.13	498.19	496.94	495.38	492.56	488.81	483.18
550	548.81	548.50	546.94	545.38	542.56	538.50	533.18
600	599.13	598.19	596.94	595.38	592.56	588.50	582.56

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表 2 各标定温度下传感器输出误差比较

Table 2. Comparison of output errors of the sensor at each calibration temperature

标定温度/℃	最大误差/kPa	标准差/kPa	标定温度/℃	最大误差/kPa	标准差/kPa
20	−1.19	0.22	60	−7.44	0.47
30	−1.81	0.14	70	−11.50	0.67
40	−3.06	0.16	80	−17.44	1.10
50	−4.62	0.09

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表 3 3种模型补偿精度和收敛速度比较

Table 3. Comparison of compensation accuracy and convergence rate of three models

补偿模型	误差分布区间/kPa	误差标准差/kPa	迭代次数	收敛时间/s
BP神经网络	[−0.806 4，0.981 1]	0.351 2	94	3.627 1
小波神经网络	[−0.697 0，0.507 3]	0.192 2	65	2.542 3
遗传小波神经网络	[−0.360 3，0.380 9]	0.186 5	37	1.635 9

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表 4 补偿后传感器的输出值

Table 4. The output value of the sensor after compensation

标定压力/kPa	不同标定温度下的输出值/kPa
标定压力/kPa	20 ℃	30 ℃	40 ℃	50 ℃	60 ℃	70 ℃	80 ℃
100	100.38	99.75	100.26	100.20	100.25	100.37	100.03
150	150.34	149.80	149.99	150.08	149.84	149.94	150.11
200	200.07	199.82	200.30	200.10	199.80	199.84	199.87
250	250.01	249.75	250.19	250.00	249.86	250.08	249.86
300	299.99	299.99	300.06	299.85	299.83	299.85	300.19
350	350.11	349.84	349.90	350.01	349.80	349.76	350.06
400	400.04	400.09	399.79	399.91	399.90	399.87	400.14
450	449.69	449.73	449.70	449.80	449.97	449.88	449.95
500	499.94	499.71	499.65	499.78	499.87	499.76	499.88
550	549.64	550.10	549.71	549.86	550.17	549.76	550.20
600	599.83	599.79	599.69	599.85	600.35	599.97	599.73

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表 5 补偿后各标定温度下传感器输出误差比较

Table 5. Comparison of output errors of the sensor at each calibration temperature after compensation

标定温度/℃	最大误差/kPa	标准差/kPa	标定温度/℃	最大误差/kPa	标准差/kPa
20	0.38	0.23	60	0.35	0.20
30	−0.29	0.14	70	0.37	0.18
40	−0.35	0.24	80	−0.27	0.15
50	−0.22	0.14

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表 6 起爆后各时段温度压力值

Table 6. Temperature and pressure values at various times after explosion

相对时间/s	介质温度/℃	环境温度/℃	实测压力/kPa	补偿后压力/kPa	误差/kPa
0	21.2	21.2	89.4	90.3	0.9
20	54.9	26.1	458.8	460.5	1.7
25	63.2	29.4	494.6	496.8	2.2
40	112.8	38.5	446.0	448.9	2.9
60	126.1	53.8	393.2	398.5	5.3
80	125.9	73.4	362.8	375.1	12.3
100	120.5	74.6	342.8	355.6	12.8
120	116.2	69.7	330.4	338.9	8.5

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