| Citation: |
DENG Lijun，YUAN Jinbo，LIU Jian，et al. Detection method of wind speed anomaly fluctuation based on SSA−LSTM[J]. Coal Science and Technology，2024，52（3）：139−147. DOI: 10.12438/cst.2023-0463
|
Aiming at the problem of high leakage rate and false alarm rate of traditional statistical methods for abnormal fluctuation in sensor monitoring data caused by dampers opening and closing, a SSA-LSTM wind speed abnormal fluctuation detection method based on the combination of Singular Spectrum Analysis (SSA) and Long and Short-Term Memory Neural Network (LSTM) was proposed by mining the data features in the time-series data in the wind speed sensors. Firstly, SSA was used to pre-process the wind speed sensor monitoring data, and the wind speed data was decomposed into trend component, periodic component and noise component. The data noise generated by turbulent pulsation was removed via reorganizing the trend component and noise component. The LSTM parameters was then optimized, and the optimized LSTM model was used to predict the pre-processed data and obtain the reconstructed wind speed. Finally, the anomaly fraction of the monitored wind speed and reconstructed wind speed was calculated by using the logarithmic probability density function. Anomaly detection for monitoring wind speed was performed by calculating the threshold set value of training set data samples. The experimental results shown that, the removing effect for the data noise generated by turbulence pulsation via SSA was better. Removing the noise component without affecting the data fluctuation was helpful in improving the wind speed reconstruction effect and the anomaly detection accuracy. LSTM can correctly reconstruct the small amplitude wave due to turbulence pulsation without anomalous fluctuation and fits well with the actual data. The reconstruction of abnormal fluctuation segment based on historical fluctuation trend when there was abnormal fluctuation can effectively improve the accuracy of anomaly detection. Through comparative analysis, the reconstruction effect of proposed method in this paper was better than ARIMA, BP and CNN models, with an anomaly detection accuracy of 99.2% and an F1-Score of 0.97, which verified the reliability of the proposed method. The method proposed in the paper has important application value in detecting the abnormal fluctuation of wind speed caused by the opening and closing of dampers.
| [1] |
刘 剑,郭 欣,邓立军,等. 基于风量特征的矿井通风系统阻变型单故障源诊断[J]. 煤炭学报,2018,43(1):143−149.
LIU Jian,GUO Xin,DENG Lijun,et al. Resistance variant single fault source diagnosis of mine ventilation system based on air volume characteristic[J]. Journal of China Coal Society,2018,43(1):143−149.
|
| [2] |
魏 林,白天亮,付 华,等. 基于EMD-LSSVM的瓦斯浓度动态预测模型[J]. 安全与环境学报,2016,16(2):119−123.
WEI Lin,BAI Tianliang,FU Hua,et al. New gas concentration dynamic prediction model based on the EMD-LSSVM[J]. Journal of Safety and Environment,2016,16(2):119−123.
|
| [3] |
郭 超,陈 蓥. 矿山震动信号预处理及其对时频的影响[J]. 煤炭学报,2016,41(7):1859−1865.
GUO Chao,CHEN Ying. Filtering technique for mining vibration signals and its affection on the signals’ time-frequency characteristics[J]. Journal of China Coal Society,2016,41(7):1859−1865.
|
| [4] |
戴前伟,丁 浩,张 华,等. 基于变分模态分解和奇异谱分析的GPR信号去噪[J]. 吉林大学学报(地球科学版),2022,52(3):701−712.
DAI Qianwei,DING Hao,ZHANG Hua,et al. Noise reduction method of GPR signal based on VMD-SSA[J]. Journal of Jilin University(Earth Science Edition),2022,52(3):701−712.
|
| [5] |
张杏莉,卢新明,贾瑞生,等. 基于变分模态分解及能量熵的微震信号降噪方法[J]. 煤炭学报,2018,43(2):356−363.
ZHANG Xingli,LU Xinming,JIA Ruisheng,et al. Micro-seismic signal denoising method based on variational mode decomposition and energy entropy[J]. Journal of China Coal Society,2018,43(2):356−363
|
| [6] |
周兰庭,邓思源,柳志坤,等. 基于SSA−LSTM-GF的混凝土坝变形分析预测方法[J]. 河海大学学报(自然科学版),2023,51(2):73−80,149.
ZHOU Lanting,DENG Siyuan,LIU Zhikun,et al. Deformation analysis and prediction method of concrete dam based on SSA−LSTM-GF[J]. Journal of Hohai University(Natural Sciences),2023,51(2):73−80,149.
|
| [7] |
陈 越,刘雄英,任子良,等. 基于相空间重构和奇异谱分析的混沌信号降噪[J]. 华南理工大学学报(自然科学版),2018,46(3):58−64,91.
CHEN Yue,LIU Xiongying,REN Ziliang,et al. Noise reduction of chaotic signals based on phasespace reconstruction and singular spectrum analysis[J]. Journal of South China University of Technology( Natural Science Edition),2018,46(3):58−64,91.
|
| [8] |
董维强,孟召平,沈 振,等. 基于循环神经网络的煤层气井产气量预测方法研究[J]. 煤炭科学技术,2021,49(9):176−183.
DONG Weiqiang,MENG Zhaoping,SHEN Zhen,et al. Research on coalbed methane well gas production forecast method based on cyclic neural network[J]. Coal Science and Technology,2021,49(9):176−183.
|
| [9] |
邓华伟,李喜旺. 基于深度学习的网络流量异常识别与检测[J]. 计算机系统应用,2023,32(2):274−280.
DENG Huawei,LI Xiwang. Abnormal network flow identification and detection based on deep learning[J]. Computer Systems & Applications,2023,32(2):274−280.
|
| [10] |
龚向阳,杨跃平,张明达,等. 基于深度残差LSTM的视频异常行为识别算法[J]. 电子设计工程,2022,30(19):164−168.
GONG Xiangyang,YANG Yueping,ZHANG Mingda,et al. Video abnormal behavior recognition algorithm based on depth residual and Long Short-Term Memory network[J]. Electronic Design Engineering,2022,30(19):164−168.
|
| [11] |
蔡兴旭,刘以安,肖 颖. 基于改进LSTM的桥梁传感器异常数据的检测方法[J]. 计算技术与自动化,2021,40(2):8−11,20.
CAI Xinxu,LIU Yian,XIAO Ying. Detection method of bridge sensor abnormal data based on improved LSTM[J]. Computing Technology and Automation,2021,40(2):8−11,20.
|
| [12] |
田 亮,袁存波. 基于LSTM和证据理论的引风机轴承故障诊断[J]. 动力工程学报,2023,43(5):614−621.
TIAN Liang,YUAN Cunbo. Fault diagnosis of induced draft fan bearing based on LSTM and evidence theory[J]. Journal of Chinese Society of Power Engineering,2023,43(5):614−621.
|
| [13] |
吴慧军,郭超雨,苏承国,等. 基于EEMD-GRU-MC的短期风功率组合预测方法[J]. 南方电网技术,2023,17(2):66−73.
WU Huijun,GUO Chaoyu,SU Chengguo,et al. Combined prediction method for short-term wind power based on EEMD-GRU-MC[J]. Southern Power System Technology,2023,17(2):66−73.
|
| [14] |
邹文进,郝少飞,马 刚,等. 基于CEEMD-GA-BP神经网络的风光发电功率预测[J]. 电网与清洁能源,2022,38(3):111−118.
ZOU Wenjin,HAO Shaofei,MA Gang,et al. Forecast of wind and solar power generation based on CEEMD-GA-BP neural network[J]. Power System and Clean Energy,2022,38(3):111−118.
|
| [15] |
缪新颖,褚金奎,杜小文. LM-BP神经网络在大坝变形预测中的应用[J]. 计算机工程与应用,2011,47(1):220−222.
MIAO Xinying,CHU Jinkui,DU Xiaowen. Application of LM-BP neural network in predicting dam deformation[J]. Computer Engineering and Applications,2011,47(1):220−222.
|
| [16] |
王 月,江逸茗,兰巨龙. 基于改进三元组网络和K近邻算法的入侵检测[J]. 计算机应用,2021,41(7):1996−2002.
WANG Yue,JIANG Yiming,LAN Julong. Intrusion detection based on improved triplet network and K-nearest neighbor algorithm[J]. Journal of Computer Applications,2021,41(7):1996−2002.
|
| [17] |
朱墨然,王麒翔,张庆华. 多源数据驱动的防突预警指标自适应技术研究[J]. 煤炭科学技术,2022,50(8):75−81.
ZHU Moran,WANG Qixiang,ZHANG Qinghua. Research on adaptive technology of outburst prevention early warning index driven by multi-source data[J]. Coal Science and Technology,2022,50(8):75−81.
|
| [18] |
戴豪民,许爱强,孙伟超. 基于改进奇异谱分析的信号去噪方法[J]. 北京理工大学学报,2016,36(7):727−732,759.
DAI Haomin,XU Aiqiang,SUN Weichao. Signal denoising method based on improvesingular spectrum analysis[J]. Transactions of Beijing Institute of Technology,2016,36(7):727−732,759.
|
| [19] |
董维强,孟召平,沈 振,等. 基于循环神经网络的煤层气井产气量预测方法研究[J]. 煤炭科学技术,2021,49(9):176−183
DONG Weiqiang,MENG Zhaoping,SHEN Zhen,et al. Research on coalbed methane well gas production forecast method based on cyclic neural network[J]. Coal Science and Technology,2021,49(9):176−183.
|
| [20] |
邓立军,尚文天,刘 剑,等. 风门开闭对矿井风流影响规律模拟试验研究[J]. 安全与环境学报,2023,23(8):2759−2769.
DENG Lijun,SHANG Wentian,LIU Jian,et al. Simulation experimental study on the effect of air door opening and closing on the wind flow law of mine[J]. Journal of Safety and Environment,2023,23(8):2759−2769.
|
| [21] |
SINGH A. Anomaly detection for temporal data using long short-term memory (LSTM)[D]. Sweden:KTH Royal Institute of Technology,2017.
|
| 1. |
刘志强，宋朝阳，荆国业，程守业，王强，吕政辉. 井筒建设行业格局与智能建井新局发展策略. 建井技术. 2025(01): 1-14 .
| |
| 2. |
刘志强，宋朝阳，程守业，荆国业，高伟，李生生. 煤矿矿井建设技术与装备70余年创新发展及推广实践. 煤炭科学技术. 2024(01): 65-83 .
本站查看
| |
| 3. |
宋朝阳，崔泽升，王子雷，王强，荆国业，李英全. 不同钻进参数下反井钻机扩孔钻进速率与岩渣特征试验研究. 建井技术. 2024(03): 68-73+67 .
| |
| 4. |
刘志强，宋朝阳，王强，吕政辉，张广宇. 金属矿开采天井机械破岩钻进工艺及关键技术分析. 有色金属(矿山部分). 2024(06): 42-50 .
| |
| 5. |
李术才，李利平，孙子正，刘知辉，李梦天，潘东东，屠文锋. 超长定向钻注装备关键技术分析及发展趋势. 岩土力学. 2023(01): 1-30 .
| |
| 6. |
刘志强，宋朝阳. 我国大直径井筒机械破岩钻井技术与装备新进展. 建井技术. 2022(01): 1-9 .
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: