A Multivariate Statistics-Based Approach for Detecting Diesel Engine Faults with Weak Signatures

Jinxin Wang, Chi Zhang, Xiuzhen Ma, Zhongwei Wang, Yuandong Xu and Robert Cattley
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Jinxin Wang: College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
Chi Zhang: College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
Xiuzhen Ma: College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
Zhongwei Wang: College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
Yuandong Xu: Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Robert Cattley: Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK

Energies, 2020, vol. 13, issue 4, 1-14

Abstract: The problem of timely detecting the engine faults that make engine operating parameters exceed their control limits has been well-solved. However, in practice, a fault of a diesel engine can be present with weak signatures, with the parameters fluctuating within their control limits when the fault occurs. The weak signatures of engine faults bring considerable difficulties to the effective condition monitoring of diesel engines. In this paper, a multivariate statistics-based fault detection approach is proposed to monitor engine faults with weak signatures by taking the correlation of various parameters into consideration. This approach firstly uses principal component analysis (PCA) to project the engine observations into a principal component subspace (PCS) and a residual subspace (RS). Two statistics, i.e., Hotelling’s T 2 and Q statistics, are then introduced to detect deviations in the PCS and the RS, respectively. The Hotelling’s T 2 and Q statistics are constructed by taking the correlation of various parameters into consideration, so that faults with weak signatures can be effectively detected via these two statistics. In order to reasonably determine the control limits of the statistics, adaptive kernel density estimation (KDE) is utilized to estimate the probability density functions (PDFs) of Hotelling’s T 2 and Q statistics. The control limits are accordingly derived from the PDFs by giving a desired confidence level. The proposed approach is demonstrated by using a marine diesel engine. Experimental results show that the proposed approach can effectively detect engine faults with weak signatures.

Keywords: diesel engine; condition monitoring; fault detection; multivariate statistics; principal component analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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