杨华芬,陈斌.大数据框架下基于改进自适应滤波算法的机械故障信号处理[J].机床与液压,2021,49(2):175-180.
YANG Huafen,CHEN Bin.Mechanical Fault Signal Processing Based on Improved Adaptive Filtering Algorithm in Big Data Frame[J].Machine Tool & Hydraulics,2021,49(2):175-180 |
| 大数据框架下基于改进自适应滤波算法的机械故障信号处理 |
| Mechanical Fault Signal Processing Based on Improved Adaptive Filtering Algorithm in Big Data Frame |
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| DOI：10.3969/j.issn.1001-3881.2021.02.35 |
| 中文关键词: 大数据 Hadoop平台 自适应滤波 步长因子 故障信号 |
| 英文关键词: Big data Hadoop platform Adaptive filtering Step factor Fault signal |
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| 摘要点击次数: 74 |
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| 中文摘要: |
| 针对经典自适应滤波算法处理机械故障信号时收敛过慢的问题，在大数据框架下提出一种改进的自适应滤波算法。以Hadoop平台为基础架构，构建一种三层次结构的机械故障大数据处理框架，用于采集和预处理原始故障大数据集；在信号滤波方面引入步长变化因子函数和均方误差函数，提高算法的收敛性能；基于离散粒子群算法对故障信号滤波处理过程进行优化，提高迭代速度和全局寻优的能力。实验结果表明：改进后的滤波算法降噪效果明显，尤其在低信噪比条件下其收敛性能相对于经典滤波算法更具优势 |
| 英文摘要: |
| In view of the slow convergence of the classical adaptive filtering algorithm in processing mechanical fault signals, an improved adaptive filtering algorithm was proposed in the framework of big data. Based on Hadoop platform, a mechanical fault big data processing framework with three level structure was constructed, which was used to collect and preprocess the original fault big data set. In the aspect of signal filtering, step change factor function and mean square error function were introduced to improve the convergence performance of the algorithm. Based on discrete particle swarm optimization algorithm, the filtering process of fault signal was optimized to improve the iteration speed and global optimization ability. The experimental results show that the improved filtering algorithm has obvious noise reduction effect, especially in the condition of low SNR, the convergence performance is better than the classical filtering algorithm |
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