Multiple Engine Faults Detection Based on Variational Mode Decomposition and Echo State Network Tianjin University

Format:

Book

Conference/Event

Author/Creator:

Li, Xin, author.

Contributor:

Bi, Fengrong

Cheng, Jiangang

Ma, Xiaoqiang

Shen, Pengfei

Conference Name:

WCX SAE World Congress Experience (2020-04-21 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2020

Summary:

As a major power source, diesel engines are being widely used in a variety of fields. However, because of complex structure, some faults which cannot be detected by direct signals would occur on engines and even lead to accidents. Among all kinds of indirect signals, vibration signal is the most common choice for faults detection without disassemble because of its convenience and stability. This paper proposed a novel approach for detecting multiple engine faults based on block vibration signals using variational mode decomposition (VMD) and echo state network (ESN). Since the quadratic penalty has a great influence on adaptable VMD that may make expected component signals cannot be extracted exactly, this paper proposed a dynamic quadratic penalty value, which will change with decomposing level. This paper selected a best dynamic quadratic penalty value by analyzing a large amount of data and results showed that this approach can decompose signals more exactly. Based on that, 8 characteristic parameters were extracted to describe faults features comprehensively, while high-dimensional data raised computational difficulty for classifier. To improve the recognition rate of ESN for high-dimensional data, this paper proposed an approach that using dimensionality reduction method, in which the kernel local Fisher discriminant analysis (KLFDA) based on Gauss kernel function was proved to be the best choice, to reduce the dimension of original data firstly. Then the ESN was used to recognize faults by analyzing the low-dimensional data. Results showed that this approach obtained ideal recognition rate and had ability to detect multiple engine faults exactly

Notes:

Vendor supplied data

Publisher Number:

2020-01-0418

Access Restriction:

Restricted for use by site license