自编码
SCADA系统
涡轮机
计算机科学
不可用
人工智能
控制图
风力发电
状态监测
水准点(测量)
深度学习
工程类
实时计算
过程(计算)
可靠性工程
操作系统
电气工程
地理
机械工程
大地测量学
作者
Luoxiao Yang,Zijun Zhang
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号:70: 1-11
被引量:40
标识
DOI:10.1109/tim.2020.3045800
摘要
Gearbox failure is one of top-ranked factors leading to the unavailability of wind turbines (WTs). Existing data-driven studies of gearbox failure detection (GFD) focus on improving detection accuracies while reducing false alarms has not received sufficient discussions. In this article, we propose a deep joint variational autoencoder (JVAE)-based monitoring method using wind farm supervisory control and data acquisition (SCADA) data to more effectively detect WT gearbox failures. The JVAE-based monitoring method includes two parts. First, a novel JVAE that takes a chunk of multivariate time series derived from collected SCADA data as inputs is developed. The JVAE utilizes two types of predefined parameters, behavior parameters (BPs) and conditional parameters (CPs), to produce reconstruction errors (REs) of the BP, which reflects the gearbox abnormality. Next, a statistical process control chart is developed to monitor REs and raise alarms. To validate advantages of the proposed method in GFD, five methods, the joint latent variational autoencoder (JLVAE)-, the variational autoencoder (VAE)-, full-dimensional VAE (FDVAE)-, recurrent autoencoder (RAE)-, and one-class support vector machine (OCSVM)-based monitoring methods, are considered as benchmarks. SCADA data with field reports of gearbox failure events collected from four commercial wind farms are utilized to demonstrate the effectiveness of the JVAE-based monitoring method on GFD and its stronger ability to resist false alarms.
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