Fault diagnosis method for underground coal mining equipment audio signals based on improved transfer learning
-
摘要:
煤矿井下生产运行环境恶劣，其关键设备如瓦斯泵、通风机、采煤机等长期处于启动状态，易产生缺陷性故障。目前端到端音频数据故障诊断方法的模型训练与更新高度依赖于数据标注，尽管可以获取海量原始数据，但这些数据通常未经标注，难以直接用于模型训练，设备运行工况的突变和设备重组等因素可能导致数据分布发生变化，从而引起模型性能下降。针对上述问题，提出了一种基于改进迁移学习的煤矿井下设备音频信号故障诊断方法。首先，对煤矿设备音频信号进行梅尔频率倒谱系数（MFCC）特征提取，捕捉设备运行状态中的关键信息，得到故障特征二维系数图。然后，构建基于改进迁移学习的故障诊断网络模型，以改进最大均值差异，即多核联合最大均值差异作为度量标准，借助伪标签计算联合分布距离，将标签信息通过多重线性映射进行特征匹配，以减少数据分布差异，实现边缘分布和条件分布同时对齐。实验结果表明：所提方法在无标签条件下能够实现高精度的故障诊断，准确率达到96.99%，标准差为0.014；在模型抗噪性能实验中，基于改进迁移学习的故障诊断模型在低信噪比（如10 dB）条件下仍能保持80%的故障诊断准确率，展现出较强的抗噪鲁棒性。
Abstract:The underground production environment in coal mines is harsh, and key equipment such as gas pumps, ventilators, and coal shearers often operate continuously, making them susceptible to faults. Currently, end-to-end audio data fault diagnosis methods heavily depend on data labeling for model training and update. Although large amounts of raw data can be collected, these data are typically unlabeled and cannot be directly used for model training. Factors such as sudden changes in equipment operating conditions or equipment reconfiguration may cause data distribution changes, leading to decreased model performance. To address these issues, an underground coal mining equipment audio signal fault diagnosis method based on improved transfer learning is proposed. First, Mel-Frequency Cepstral Coefficients (MFCC) features were extracted from the audio signals of coal mining equipment to capture key information about the equipment's operational status, generating a 2D fault feature coefficient map. Then, a fault diagnosis network model based on improved transfer learning was established, using the improved Maximum Mean Discrepancy (MMD) and multi-kernel joint MMD as metrics. The joint distribution distance was calculated using pseudo-labels, and label information was mapped through multiple linear transformations to match features and reduce data distribution differences, achieving simultaneous alignment of both marginal and conditional distributions. Experimental results showed that the proposed method achieved high-accuracy fault diagnosis under unlabeled conditions, with an accuracy rate of 96.99% and a standard deviation of 0.014. In model noise resistance experiments, the fault diagnosis model based on improved transfer learning maintained 80% diagnostic accuracy under low signal-to-noise ratio conditions (e.g., 10 dB), demonstrating strong noise robustness.
-
-
![]()
图 2 基于改进迁移学习的煤矿井下设备音频信号故障诊断模型
Figure 2. Underground coal mining equipment audio signal fault diagnosis model based on improved transfer learning
![]()
图 3 基于改进迁移学习的煤矿井下设备音频故障诊断流程
Figure 3. Underground coal mining equipment audio signal fault diagnosis process based on improved transfer learning
![]()
图 4 胶带运输设备在500 r/min负载下不同状态截取信号的一维时域波形
Figure 4. One-dimensional time-domain waveforms of signals extracted from belt conveyor equipment at 500 r/min load under different states
![]()
图 5 可变参数设置方式下模型损失的变化趋势
Figure 5. Variation trends of model loss under variable parameter settings
![]()
图 6 固定参数设置方式下模型损失的变化趋势
Figure 6. Variation trends of model loss under fixed parameter settings
表 1 模型参数
Table 1 Model parameters
层 核尺寸 核数量 步长 输出尺寸 输入层 — — — 64×64×1 Conv 5×5 16 1 60×60×16 Pooling 5×5 16 2 30×30×16 Conv 5×5 32 1 26×26×32 Pooling 5×5 32 2 13×13×32 Flatten — — — 1 024 FC — — — 256 FC — — — 256 FC — — — 4 
下载: 导出CSV
表 2 煤矿井下设备音频跨域学习任务数据集
Table 2 Dataset of underground coal mining equipment audio signals for cross-domain learning tasks
任务 转速/（r·min−1） 0 500 1 700 2 900
下载: 导出CSV
表 3 不同学习率下故障诊断准确率
Table 3 Fault diagnosis accuracy rates at different learning rates
学习率 迁移任务 平均
准确率/%标准差 0—1 0—2 1—0 1—2 2—0 2—1 0.001～0.000 01 0.9816 0.9633 0.9589 0.9735 0.9525 0.9896 96.99 0.014 0.001 0.9423 0.9601 0.9452 0.9612 0.8922 0.9534 94.24 0.025 0.000 1 0.9354 0.8755 0.9025 0.9145 0.8633 0.9612 90.87 0.036 0.000 01 0.9522 0.8647 0.9254 0.9412 0.8511 0.9587 91.55 0.046
下载: 导出CSV
表 4 不同平衡超参数下故障诊断准确率
Table 4 Fault diagnosis accuracy rates under different balance hyperparameters
平衡
超参数迁移任务 平均
准确率/%标准差 0—1 0—2 1—0 1—2 2—0 2—1 0～1 0.9816 0.9633 0.9589 0.9735 0.9525 0.9896 96.99 0.014 0.5 0.9391 0.9093 0.9281 0.89534 0.9382 0.9515 92.69 0.020 1.0 0.9799 0.9302 0.9233 0.9586 0.8956 0.9413 93.81 0.029
下载: 导出CSV
表 5 不同模型的诊断精度
Table 5 Diagnosis accuracy of different models
模型 迁移任务 平均准确率/% 标准差 0—1 0—2 1—0 1—2 2—0 2—1 Base 0.5115 0.6072 0.5781 0.6992 0.6685 0.8251 64.79 0.103 AdaBN 0.5263 0.5537 0.6731 0.6594 0.6454 0.9033 66.43 0.111 MKMMD 0.9699 0.9247 0.9365 0.9816 0.9164 0.9365 94.03 0.023 CORAL 0.6184 0.5067 0.5017 0.6134 0.7408 0.5418 58.27 0.101 DANN 0.9649 0.9365 0.8712 0.9515 0.801 0.9482 88.93 0.079 MKJMMD 0.9816 0.9633 0.9589 0.9735 0.9525 0.9896 96.99 0.014
下载: 导出CSV
-
[1] 王国法,庞义辉,任怀伟,等. 智慧矿山系统工程及关键技术研究与实践[J]. 煤炭学报,2024,49(1):181-202. WANG Guofa,PANG Yihui,REN Huaiwei,et al. System engineering and key technologies research and practice of smart mine[J]. Journal of China Coal Society,2024,49(1):181-202.
[2] ZHANG Wei,LI Xiang,DING Qian. Deep residual learning-based fault diagnosis method for rotating machinery[J]. ISA Transactions,2019,95:295-305. DOI: 10.1016/j.isatra.2018.12.025
[3] 曲星宇,曾鹏,李俊鹏. 基于RNN−LSTM的磨矿系统故障诊断技术[J]. 信息与控制,2019,48(2):179-1866. DOI: 10.5755/j01.itc.48.2.21390 QU Xingyu,ZENG Peng,LI Junpeng. Fault diagnosis technology of grinding system based on RNN-LSTM[J]. Information and Control,2019,48(2):179-186. DOI: 10.5755/j01.itc.48.2.21390
[4] TAO Hongfeng,WANG Peng,CHEN Yiyang,et al. An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks[J]. Journal of the Franklin Institute,2020,357(11):7286-7307. DOI: 10.1016/j.jfranklin.2020.04.024
[5] WANG Dong. K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads:revisited[J]. Mechanical Systems and Signal Processing,2016,70/71:201-208. DOI: 10.1016/j.ymssp.2015.10.007
[6] WANG Huaqing,CHEN Peng. Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network[J]. Computers & Industrial Engineering,2011,60(4):511-518.
[7] 徐可,陈宗海,张陈斌,等. 基于经验模态分解和支持向量机的滚动轴承故障诊断[J]. 控制理论与应用,2019,36(6):915-922. DOI: 10.7641/CTA.2018.80257 XU Ke,CHEN Zonghai,ZHANG Chenbin,et al. Rolling bearing fault diagnosis based on empirical mode decomposition and support vector machine[J]. Control Theory & Applications,2019,36(6):915-922. DOI: 10.7641/CTA.2018.80257
[8] WU Dazhong,JENNINGS C,TERPENNY J,et al. A comparative study on machine learning algorithms for smart manufacturing:tool wear prediction using random forests[J]. Journal of Manufacturing Science and Engineering,2017,139(7). DOI: 10.1115/1.4036350.
[9] 雷亚国,杨彬,杜兆钧,等. 大数据下机械装备故障的深度迁移诊断方法[J]. 机械工程学报,2019,55(7):1-8. DOI: 10.3901/JME.2019.07.001 LEI Yaguo,YANG Bin,DU Zhaojun,et al. Deep transfer diagnosis method for machinery in big data era[J]. Journal of Mechanical Engineering,2019,55(7):1-8. DOI: 10.3901/JME.2019.07.001
[10] 陈湘源,秦伟,刘晏驰,等. 融合卷积神经网络与线性回归的带式输送机托辊故障音频识别方法[J/OL]. 煤炭科学技术:1-9[2024-12-14]. http://kns.cnki.net/kcms/detail/11.2402.TD.20240701.1327.001.html.html. CHEN Xiangyuan,QIN Wei,LIU Yanchi,et al. Audio recognition method of belt conveyor roller fault based on convolutional neural network and linear regression[J/OL]. Coal Science and Technology:1-9[2024-12-14]. http://kns.cnki.net/kcms/detail/11.2402.TD.20240701.1327.001.
[11] 李羽蒙,樊红. 基于MFCC特征与卷积神经网络的托辊故障诊断方法[J]. 武汉大学学报(工学版),2024,57(5):691-698. LI Yumeng,FAN Hong. A fault diagnosis method of roller based on MFCC features and convolutional neural network[J]. Engineering Journal of Wuhan University,2024,57(5):691-698.
[12] SCHWENDEMANN S,AMJAD Z,SIKORA A. Bearing fault diagnosis with intermediate domain based layered maximum mean discrepancy:a new transfer learning approach[J]. Engineering Applications of Artificial Intelligence,2021,105. DOI: 10.1016/J.ENGAPPAI.2021.104415.
[13] HABIC V,SEMENOV A,PASILIAO E L. Multitask deep learning for native language identification[J]. Knowledge-based Systems,2020,209. DOI: 10.1016/j.knosys.2020.106440.
[14] LI Xuhong,GRANDVALET Y,DAVOINE F,et al. Transfer learning in computer vision tasks:remember where you come from[J]. Image and Vision Computing,2020,93. DOI: 10.1016/j.imavis.2019.103853.
[15] 吴静然,刘建华,崔冉. 子域适应无监督轴承故障诊断[J]. 振动与冲击,2021,40(15):34-40. WU Jingran,LIU Jianhua,CUI Ran. Sub-domain adaptive unsupervised bearing fault diagnosis[J]. Journal of Vibration and Shock,2021,40(15):34-40.
[16] WANG Xu,SHEN Changqing,XIA Min,et al. Multi-scale deep intra-class transfer learning for bearing fault diagnosis[J]. Reliability Engineering & System Safety,2020,202. DOI: 10.1016/j.ress.2020.107050.
[17] ZHU Yongchun,ZHUANG Fuzhen,WANG Jindong,et al. Deep subdomain adaptation network for image classification[J]. IEEE Transactions on Neural Networks and Learning Systems,2020,32(4):1713-1722.
[18] FAN Cheng,SUN Yongjun,XIAO Fu,et al. Statistical investigations of transfer learning-based methodology for short-term building energy predictions[J]. Applied Energy,2020,262. DOI: 10.1016/j.apenergy.2020.114499.
[19] 于功也,蔡伟东,胡明辉,等. 故障机理与领域自适应混合驱动的机械故障智能迁移诊断[J]. 航空学报,2023,44(2):321-332. YU Gongye,CAI Weidong,HU Minghui,et al. Intelligent migration diagnosis of mechanical faults driven by hybrid fault mechanism and domain adaptation[J]. Acta Aeronautica et Astronautica Sinica,2023,44(2):321-332.
[20] 张家安,田家辉,王铁成,等. 基于改进MFCC算法的风力机叶片故障诊断方法[J]. 太阳能学报,2024,45(1):285-290. ZHANG Jia'an,TIAN Jiahui,WANG Tiecheng,et al. Wind turbine blade fault diagnosis method based on improved MFCC algorithm[J]. Acta Energiae Solaris Sinica,2024,45(1):285-290.
[21] LU Weining,LIANG Bin,CHENG Yu,et al. Deep model based domain adaptation for fault diagnosis[J]. IEEE Transactions on Industrial Electronics,2017,64(3):2296-2305. DOI: 10.1109/TIE.2016.2627020
计量
- 文章访问数: 69
- HTML全文浏览量: 20
- PDF下载量: 14
