个人简介
宋狄，博士，讲师，硕士研究生导师。2022年11月至2023年11月加拿大CSC联合培养博士，2024年博士毕业于东南大学机械工程专业，2024年6月入职江苏师范大学机电工程学院。
研究方向
▪主要从事于轴承、齿轮、叶片等旋转机械设备故障诊断与智能运维
主讲课程
1.本科生课程：《Python程序设计》《单片机原理及应用》《数据结构与数据库》
2.研究生课程：《现代信号处理及应用》《研究生前沿讲座》
论文专著及专利
论文：
1．Song D.*, Meng L., Cao J., et al. Incomplete acoustic data-driven pipelines leakage detection using wavelet denoising and time-frequency images[J]. Measurement Science and Technology, 2026, 37: 086103.
2．Song D.*, Wang K., Xiao Y., et al. Piezoelectric bearing transducer and deep fusion framework for crack detection[J]. Smart Materials and Structures, 2026, 35: 035002.
3．Meng L., Song D.*, Cao J., et al. Natural gas pipeline leakage detection based on new wavelet basis transform and singular value decomposition in 2D-CNN[J]. Nondestructive Testing and Evaluation, 2026.
4．Zhong J., Liu W., Song D., et al. A novel rolling bearing fault diagnosis method based on E-RTH multi-parameter optimization of LSSVM[J]. Journal of Vibration Engineering & Technologies, 2025, 14(1): 23.
5．Wang K., Han Y.*, Song D.*, et al. Data and decision-level hybrid fusion method for pressure pipeline pattern recognition[J]. Structural Health Monitoring, 2025.
6．Xiao Y., Song D., Wu N. Development of compact smart bearing and novel hybrid feature assessment for weak defect identification[J]. Nondestructive Testing and Evaluation, 2025, 40(10): 4669-4695.
7．Shen J., Ma T., Song D.*, et al. An embedded physical information network for blade crack detection considering dynamic multi-level credibility[J]. Mechanical Systems and Signal Processing, 2025, 224: 111948.
8．马天池, 沈君贤, 宋狄, 等. 基于多域特征与信息融合的叶片裂纹故障诊断[J]. 东南大学学报(自然科学版), 2024, 54(6): 1567-1573.
9．Shen J., Ma T., Song D., et al. Quantitative blade damage detection based on multisource domain and multistage joint transfer[J]. Structural Health Monitoring, 2024, 23(6): 3581-3598.
10．Ma T., Shen J., Song D., et al. A two-level fusion model of vibro-acoustic signals for centrifugal fan blade crack detection[J]. Structural Health Monitoring, 2024, 23(6): 3800-3813.
11．Ding P.*, Song D., Shen J., et al. A novel graph structure data-driven crack damage identification for compressor blade based on vibro-acoustic signal[J]. Structural Health Monitoring, 2024, 23(5): 3046 - 3062.
12．Song D., Ma T., Shen J., et al. Incremental learning-based quantitative crack detection using prioritized experience replaying and layered importance sampling[J]. IEEE Sensors Journal, 2024, 24(15): 25132-25140.
13．沈君贤,马天池, 宋狄, 等. 基于可解释选择性集成框架的离心风机叶片裂纹损伤检测[J]. 东南大学学报(自然科学版), 2024, 60(12): 183-193.
14．Shen J., Ma T., Song D., et al. Incremental learning BiLSTM based on dynamic proportional adjustment mechanism and experience replay for quantitative detection of blade crack propagation[J]. Structural Health Monitoring, 2024, 23(2): 733-749.
15．Ma T., Shen J., Song D., et al. A vibro-acoustic signals hybrid fusion model for blade crack detection[J]. Mechanical Systems and Signal Processing, 2023, 204: 110815.
16．Ma T., Shen J., Song D., et al. Multi-sensor and multi-level information fusion model for compressor blade crack detection[J]. Measurement, 2023, 222: 113622.
17．Shen J., Song D., Ma T., et al. Blade crack detection based on domain adaptation and autoencoder of multidimensional vibro-acoustic feature fusion[J]. Structural Health Monitoring, 2023, 22(5): 3498-3513.
18．Song D., Shen J., Ma T., et al. Multi-objective acoustic sensor placement optimization for crack detection of compressor blade based on reinforcement learning[J]. Mechanical Systems and Signal Processing, 2023, 197: 110350.
19．Song D., Ma T., Shen J., et al.. Multiobjective-Based Acoustic Sensor Configuration for Structural Health Monitoring of Compressor Blade[J]. IEEE Sensors Journal, 2023, 23(13): 14737-14745.
20．Song D., Shen J., Ma T., et al. Acoustic sensor placement optimization for compressor based on adversarial transfer learning and vibro-acoustic simulation[J]. IEEE Sensors Journal, 2023, 23(12): 13539-13547.
21．Song D., Shen J., Ma T., et al. Two-level fusion of multi-sensor information for compressor blade crack detection based on self-attention mechanism[J]. Structural Health Monitoring, 2023, 22(3): 1911-1926.
22．Ma T., Song D., Shen J., et al. Blade crack detection using variational model decomposition and time-delayed feedback nonlinear tri-stable stochastic resonance[J]. Structural Health Monitoring, 2023, 22(2): 1478-1493.
23．Song D., Xu F.*, Hu J., et al. Fault feature recognition of centrifugal compressor with cracked blade based on SNR estimation and adaptive stochastic resonance[J]. Structural Health Monitoring, 2023, 22(1): 131-150.
24．Song D., Xu F.*, Ma T. Crack damage monitoring for compressor blades based on acoustic emission with novel feature and hybridized feature selection[J]. Structural Health Monitoring, 2022, 21(6): 2641-2656.
25．Ma T., Shen J., Song D., et al. Unsaturated piecewise bistable stochastic resonance with three kinds of asymmetries driven by multiplicative and additive noise[J]. Chaos, Solitons & Fractals, 2022, 162: 112457.
26．Ma T., Song D., Shen J., et al. Unsaturated piecewise bistable stochastic resonance with three kinds of asymmetries and time-delayed feedback[J]. Chaos, Solitons & Fractals, 2022, 161: 112352.
27．Song D., Ma T., Li Y., et al. Data and decision level fusion-based crack detection for compressor blade using acoustic and vibration signal[J]. IEEE Sensors Journal, 2022, 22(12): 12209-12218.
专利：
1．宋狄, 许飞云, 胡建中等. 一种压缩机叶片裂纹损伤加速装置及方法. 发明专利, 2025, CN115127881B.
2．宋狄, 许飞云, 胡建中等. 一种基于自适应随机共振的叶片裂纹故障识别方法及应用. 发明专利, 2025, CN114518412A.
3．宋狄, 许飞云, 胡建中等. 一种压缩机叶片裂纹故障检测方法. 发明专利, 2024, CN114528868B.
4．宋狄, 许飞云, 胡建中等. 一种风机叶片裂纹的检测装置及方法. 发明专利, 2024, CN115166032B.
5．宋狄, 许飞云, 胡建中等. 一种声振融合的叶片裂纹故障检测方法及应用. 发明专利, 2023, CN114509158A.
6．徐桂云, 宋狄, 张晓光等. 一种自调节式矿井滚轮充电装置. 发明专利, 2023, CN110228739B.
荣誉获奖
1．2025年江苏省振动工程学会科学技术优秀博士奖
2．2025年江苏省仪器仪表学会科学技术二等奖（排名3）
3．2025年江苏师范大学本科毕业论文（设计）优秀指导老师
