常鹏

职称职务:副教授,硕士生导师

E-mail:changpeng@bjut.edu.cn

通讯地址:科学楼1026

基本情况

常鹏,博士后,副教授,硕士生导师,北京市属高等学校中青年骨干教师。现为北京工业大学信息科学技术学院专任教师,发表SCI期刊论文20余篇,获得发明专利10项,出版专著2部,主持国家级项目1项,省部级项目1项,企业横向项目2项。

研究方向

基于人工智能的城市污水处理过程状态监测及故障诊断、基于人工智能技术的无人车算法研究和基于人工智能技术的城市交枢纽客流预测级车辆调度等。

科研项目

[1]国家自然科学基金面上项目,基于数据驱动的动力电池多模型融合建模与状态估计,2023/01-2026/12,主持

[2]北京市自然科学基金面上项目,面向城市污水处理过程异常工况监测建模方法研究,2023/01-2025/12,主持

代表性研究成果

在城市污水处理过程的异常工况和水质指标的监测建模研究方面,解决了模型精度、实时性和算力成本等若干挑战性问题。在国际高水平权威期刊上发表SCI收录论文20余篇(中科院1区顶刊论文10篇),撰写专著2部,授权美国和中国家发明专利10余项。成果贡献为:①在异常工况监测建模方面:针对过程数据呈现明显的多重特性(非线性、动态性和非高斯性),分别构建出一系列新型的基于数据驱动的高阶统计信息增强的异常工况监测宽度网络模型和深度网络模型,提高了过程监测精度,解决了目前监测模型无法同时提取过程数据的多重特征的困境。②在水质指标的监测建模方面:针对污水处理过程水质指标无法在线精准测量,分别构建了一系列多重特征提取能力的宽度和深度学习网络软测量模型采用新型鸽群优化算法与多重特征增强的宽度学习算法相结合的模型结构更新方法,提高了水质指标监测精度,解决了目前软测量模型的理想性能与实际应用性能存在差距的问题。

主要论文论著

[1]Chang P*, Meng FC. Fault detection of Urban Wastewater Treatment Process Based on Combination of Deep Information and Transformer Network[J]. IEEE Transactions on Neural Networks and Learning Systems.2024,35(6),8124-8133(中科院分区1区,TOP期刊)

[2]Chang P*,Xu Y,Meng FC and Xiong WL.Fault Detection in Wastewater Treatment Process using Broad Slow Feature Neural Network with Incremental Learning Ability[J]. IEEE Transactions on Industrial Informatics. 2024,20(3):4540-4549.(中科院分区1区,TOP期刊)

[3]Chang P*, Zhang SR and Wang ZC. Soft Sensor of the Key Effluent Index in the Municipal Wastewater Treatment Process Based on Transformer[J].IEEE Transactions on Industrial Informatics, 2024,20(3):4021-4028.(中科院分区1区,TOP期刊)

[4]Chang P*, Xu Y, Hu ZQ.Industrial process monitoring based on Dynamic Overcomplete Broad Learning Network[J]. IEEE Transactions on Neural Networks and Learning Systems. 2024,35(2),1761-1772.(中科院分区1区,TOP期刊)

[5]Chang P*, Xu Ying ,Shi SQ and Fang ZY. Application of non-Gaussian feature enhancement extraction in gated recurrent neural network for fault detection in batch production processes [J]. Expert Systems with Applications, 2024,237(3): 121348.(中科院分区1区,TOP期刊)

[6]Chang P*, Xu Ying and Meng FC. Efficient fault monitoring in wastewater treatment processes with time-stacked broad learning network[J]. Expert Systems with Applications, 2023,233(12): 120958.(中科院分区1区,TOP期刊)

[7]Chang P*, Bao X, Meng FC and Lu RW. Multi-objective Pigeon-inspired Optimized feature enhancement soft-sensing model of Wastewater Treatment Process[J]. Expert Systems With Applications, 2023, 215(4):119193.(中科院分区1区,TOP期刊)

[8]Chang P*, Wang K, Zheng K and Meng FC. Monitoring of wastewater treatment process based on multi-stage variational autoencoder[J]. Expert Systems With Applications, 2022, 207(11):17919.(中科院分区1区,TOP期刊)

[9]Chang P*, Zhang RY and Ding CH. Dynamic hidden variable fuzzy broad neural network based batch process anomaly detection with incremental learning capabilities[J]. Expert Systems With Applications, 2022, 202(9):117390.(中科院分区1区,TOP期刊)

[10]Chang P*, Ding C H. Monitoring multi-domain batch process state based on Fuzzy Broad Learning System[J], Expert Systems With Applications, 2022,187(1):11581.(中科院分区1区,TOP期刊)

[11]Chang P*, Zhao L L, Meng F C and Xu Y. Soft measurement of effluent index in sewage treatment process based on overcomplete broad learning system[J], Applied Soft Computing. 2022, 115(1):108235.(中科院分区2区)

[12]Chang P*, Lu R W. Fault monitoring of batch process based on over complete broad learning network[J]. Engineering Applications of Artificial Intelligence, 2021,99 (3):104139.(中科院分区2区)

[13]Chang P*, Li Z Y. Over-complete deep recurrent neutral network based on wastewater treatment process soft sensor application[J]. Applied Soft Computing.2021,105 (3):107227.(中科院分区2区)

[14]Chang P*, Lu R W and Olivia K. Batch Process Fault Detection for Multi-Stage Broad Learning System [J]. Neural Networks, 2020,129 (9) : 298-312.(中科院分区1区)

[15]Chang P*, Le Z Y and Wang G M,. An effective deep recurrent network with high-order statistic information for fault monitoring in wastewater treatment process[J]. Expert Systems With Applications. 2021,27(10), 114141.(中科院分区1区,TOP期刊)

[16]Chang P*, Wang K. Quality relevant Over-complete Independent Component Analysis Based monitoring for Nonlinear and Non-Gaussian Batch Process[J]. Chemometrics and Intelligent Laboratory Systems, 2020, 205(10),104140.(中科院分区2区)

[17]Chang P*, Olivia K, Ding C H and Lu R W. Application of fault monitoring and diagnosis in process industry based on fourth order moment and singular value decomposition[J]. The Canadian Journal of Chemical Engineering, 2020, 98(3): 717-727.(中科院分区4区)

[18]Chang P*, Qiao J, Lu R W and Zhang X Y. Multiphase batch process monitoring based on higher order cumulant analysis[J]. The Canadian Journal of Chemical Engineering, 2020, 98(2): 513-524.(中科院分区4区)

[19]Chang P*,Ding C H and Zhao Q K. Fault diagnosis of microbial pharmaceutical fermentation process with non-Gaussian and nonlinear coexistence[J]. Chemometrics and Intelligent Laboratory Systems, 2020, 199(4),103931.(中科院分区2区)

[20]Ding C H, Chang P* and Olivia K. Enhanced high order information extraction for multiphase batch process fault monitoring [J]. The Canadian Journal of Chemical Engineering[J], 2020,98(10):2187-2204.(中科院分区4区)

专著

[1]常鹏,王普.间歇过程统计建模及故障诊断研究:基于数据驱动角度,知识产权出版社,170千字,2018.

[2]常鹏.基于数据驱动的间歇过程建模及故障监测:质量控制角度,知识产权出版社,180千字,2018.

专利(已授权)

(1)常鹏,丁春豪,王普.一种基于模糊宽度自适应学习模型的污水处理过程故障监测方法,2022-08-02,美国专利授权, US011403546B2

(2)常鹏,王凯,王普.一种基于过完备宽度学习模型的污水处理过程故障监测方法, 2023-06-02中国, ZL201911402093.X (专利)

(3)常鹏,卢瑞炜,张祥宇;王普.一种基于子阶段内高阶统计量构建的微生物发酵过程故障监测方法,2023-05-26,中国, ZL201911388480.2 (专利)

(4)常鹏,李泽宇,王普.一种特征自增强的循环神经网络的污水关键水质指标软测量方法,2023-05-02,中国专利授权,ZL201911298640.4 (专利)

(5)常鹏,张祥宇,卢瑞炜,王普.一种基于OICA的复杂工业过程故障监测方法, 2023-05-02,中国专利授权,ZL201910410875.1 (专利)

(6)常鹏,丁春豪,王普.一种基于多阶段OICA的间歇过程故障监测方法,2023-04-25,中国专利授权,ZL201910582671.6 (专利)

(7)王凯,常鹏,李泽宇,丁春豪.一种基于变分自编码器模型的污水处理过程故障监测方法, 2022-10-28,中国专利授权,ZL202011585643.9(专利)

(8)常鹏,卢瑞炜,张祥宇,王普.一种基于改进的多种群全局最优的自适应鸽群优化方法,2022-02-15,中国专利授权,ZL201811381008.1(专利)

(9)常鹏,卢瑞炜,张翔宇,王普.一种基于四阶矩阵奇异值分解的间歇过程故障监测方法,2021-07-02,中国专利授权,ZL201910664867.X(专利)

(10)常鹏,丁春豪,王普.一种基于模糊自适应学习模型的污水处理过程故障监测方法, 2021-02-26,中国专利授权ZL201911225929.3(专利)

(11)王普,卢瑞炜,常鹏,张祥宇.一种基于宽度学习系统的间歇过程故障监测与诊断方法, 2020-08-28,中国, ZL201910136910.5 (专利)

联系方式

北京工业大学科学楼1026,E-mail: changpeng@bjut.edu.cn

学校地址:北京市朝阳区平乐园100号
邮政编码:100124

  • 北京工业大学
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