Refined composite multiscale fuzzy entropy based fault diagnosis of diesel engine

Abstract

Due to complicated transfer paths and strong background noise interference, the fault pattern information deeply hides in common features of the vibration signal at the engine surface. In this study, the refined composite multiscale fuzzy entropy (RCMFE) used to measure the irregularity and self-similarity of time series is proposed to quantify the feature of various fault patterns. Followed by RCMFE, the features dug out are recognized by a parameter-adaptive support vector machine based on the firefly algorithm (FASVM). After putting forward the diagnosing schematics, the RCMFE-FASVM is applied to a fault diagnosis case of a diesel engine on a test rig. A comparative analysis of the four methods to extract features and the four methods to recognize fault patterns are conducted. Results indicate the proposed method has superior recognition performance and can effectively identify the working states of the diesel engine, contrasting the existing methods. Under the small samples and features task of identifying the working states of a diesel engine, the recognition rate of the proposed method with more stability can reach 98.2%, which is larger than other methods. Given the superior performance of the proposed method, the number of input features and training samples should vary from 8 to 20 and from 35 to 50.

A multi-modal joint attention network for vibro-acoustic fusion diagnosis of engines

“…This method takes into account the inadequacy of traditional statistic features in poor representing fault-related information. Instead, refined composite multiscale fuzzy entropy, as a typical entropy-based method, was introduced and applied for intake air system fault diagnosis based on vibration signals [21]. Furthermore, to overcome the limitation of using only single-channel vibration signal-based information, multivariate empirical mode decomposition was employed to integrate the multichannel vibration signals of the diesel engine cylinder head to eliminate the noise component hidden in the original signal, and the dispersion entropy of the reconstructed signal with a large correlation coefficient was calculated as the features for misfire fault recognition [22].…”

High-Dimensional Mapping Entropy Method and Its Application in the Fault Diagnosis of Reciprocating Compressors

“…These methodologies include analysis methods based on signal processing in the time domain, frequency domain, and time-frequency domain [5]. Feature extraction methods are informed by information theory, including information entropy [6] and mutual information [7]. Additionally, techniques based on sparsity measurement [8] and deep learning have gained prominence [9,10].…”