Fault Diagnosis of Controllable Pitch Propeller as Few-Shot Classification with Mechanism Simulation Data Augmentation

被引:1
作者
Feng, Yu [1 ]
Chen, Wei [2 ]
Fu, Huanglong [2 ]
Wang, Hua [2 ]
Gao, Chaojian [3 ]
Sun, Xinyu [1 ]
Wang, Jingcheng [1 ]
机构
[1] Shanghai Jiao Tong Univ, Dept Automat, Shanghai, Peoples R China
[2] Shanghai Marine Equipment Res Inst, Shanghai, Peoples R China
[3] Shanghai Jiao Tong Univ, Shanghai, Peoples R China
来源
2023 IEEE 2ND INDUSTRIAL ELECTRONICS SOCIETY ANNUAL ON-LINE CONFERENCE, ONCON | 2023年
基金
中国国家自然科学基金;
关键词
fault diagnosis; time series classification; few-shot learning; mechanical model;
D O I
10.1109/ONCON60463.2023.10430863
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The Controllable pitch propeller(CPP) can control the thrust direction and power through the adjustment of blade pitch using a hydraulic system, significantly improving the maneuverability of the vessel. Given the harsh working environment of the on-ship CPP hydraulic system, it is prone to failure and affects pitch control performance. To address the challenges of limited real-world fault condition data, we utilize a few-shot learning approach and enhance the model's sensitivity to target data distribution by introducing a simulation data augmentation phase during training. A mechanical model of the CPP hydraulic system is developed, and monitoring data under fault conditions of key components is simulated. After training with open-source time-series classification tasks, the augmentation phase is used to obtain prior knowledge of the equipment, and the model is eventually adapted to the recorded data task. The recorded data is derived from an experimental setup comprising the primary components of the CPP, and it is collected from multiple sensors under both normal and fault conditions. The target fault diagnosis task is treated as a time-series classification problem, and a network architecture based on ResNet with Triplet loss is employed.
引用
收藏
页数:5
相关论文
共 10 条
[1]   Deep learning for time series classification: a review [J].
Fawaz, Hassan Ismail ;
Forestier, Germain ;
Weber, Jonathan ;
Idoumghar, Lhassane ;
Muller, Pierre-Alain .
DATA MINING AND KNOWLEDGE DISCOVERY, 2019, 33 (04) :917-963
[2]  
Finn C, 2017, PR MACH LEARN RES, V70
[3]  
Helwig N, 2015, IEEE IMTC P, P210, DOI 10.1109/I2MTC.2015.7151267
[4]   Fault Diagnosis of Hydraulic Systems Based on Deep Learning Model With Multirate Data Samples [J].
Huang, Keke ;
Wu, Shujie ;
Li, Fanbiao ;
Yang, Chunhua ;
Gui, Weihua .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (11) :6789-6801
[5]  
Isermann R, 2011, FAULT-DIAGNOSIS APPLICATIONS: MODEL-BASED CONDITION MONITORING: ACTUATORS, DRIVES, MACHINERY, PLANTS, SENSORS, AND FAULT-TOLERANT SYSTEMS, P1, DOI 10.1007/978-3-642-12767-0
[6]   A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study [J].
Leahy, Kevin ;
Gallagher, Colm ;
O'Donovan, Peter ;
Bruton, Ken ;
O'Sullivan, Dominic T. J. .
ENERGIES, 2018, 11 (07)
[7]  
Leahy K, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (ICPHM)
[8]  
Narwariya J, 2020, ACM INT CONF PR SER, P28, DOI [10.1109/IGARSS39084.2020.9441016, 10.1145/3371158.3371162]
[9]  
Nichol A, 2018, Arxiv, DOI arXiv:1803.02999
[10]  
Schneider T., 2018, Condition Monitoring of Hydraulic Systems Data Set at ZeMA