A fault diagnosis model of marine diesel engine cylinder based on modified genetic algorithm and multilayer perceptron

Hou, Liangsheng; Zou, Jiaqi; Du, Changjiang; Zhang, Jundong

doi:10.1007/s00500-019-04388-3

Cited by 25 publications

(11 citation statements)

References 33 publications

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“…In order to verify the superiority of BLS itself, the multi-domain features of the balanced dataset A in section 4.3.2 are extracted and optimized to obtain the high fault sensitivity feature set, which is input into BLS. Likewise, the work evaluates and compares also the relatively mature deep learning methods at present, including CNN [42], DBN [15], SAE [17], and multilayer perceptron (MLP) [43], which are also trained with dataset A. The deep learning methods are set to train 500 times, and the training process is carried out on the MATLAB software platform and the computer with corei7-10750h CPU @ 2.60 GHz configuration.…”

Section: Comparison With Deep Learning Methodsmentioning

confidence: 99%

An imbalanced sample intelligent fault diagnosis method using data enhancement and improved broad learning system

Cui

2023

Meas. Sci. Technol.

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Broad learning system (BLS) has been widely applied in the field of fault diagnosis because of its high computational efficiency, simple structure, and strong interpretability. However, traditional BLS cannot extract deep level fault features. Meanwhile, some fault samples are difficult to obtain, which leads to the imbalance of samples and further affects the diagnostic results of BLS. To solve these problems, an improved BLS fault diagnosis method based on data enhancement and multi-domain feature fusion is proposed. First, to solve the problem of sample imbalance, some false samples are generated through Deep Convolutional Generative Adversarial Networks (DCGAN). Second, to solve the problem of poor feature extraction ability of BLS, the multi-domain feature extraction and feature optimization based on ReliefF algorithm are carried out for the enhanced samples. Compared with traditional BLS, the improved BLS effectively solves the problem of sample imbalance and greatly improves the diagnostic accuracy. The proposed method is then testified on the rolling bearing fault simulation test bench. The results show that, samples generated by the proposed method are highly similar to the real samples. In addition, the diagnostic accuracy of the BLS after multi-domain feature extraction and optimization is improved by about 19.67%, which proves the effectiveness of the method. This method provides a new perspective in fault diagnosis and could further expand the application of BLS in fault diagnosis.

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Section: Comparison With Deep Learning Methodsmentioning

confidence: 99%

An imbalanced sample intelligent fault diagnosis method using data enhancement and improved broad learning system

Cui

2023

Meas. Sci. Technol.

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“…Hou et al [10] propose a simple model to diagnose cylinder faults, which uses some primary monitoring parameters, such as temperature and pressure, to train a MLP using BP and Levenberg-Marquadt. Ramteke et al [11] use FFT in vibration and acoustic signals to extract statistical features to detect liner scuffing faults.…”

Section: Literature Reviewmentioning

confidence: 99%

Aplicação de Machine Learning para Diagnóstico de Falha em Motores a Diesel utilizando Sinais de Áudio

JUNQUEIRA¹,

VIOLATO²,

SIMÕES³

et al. 2022

Blucher Engineering Proceedings

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The present work aims to analyze machine learning techniques applied to pattern recognition of diesel engines audio files captured with a smartphone. Audio samples from 3 different engines were recorded, under 3 different operating conditions: normal operation, leaking hose and injector failure. Several combinations of data processing, audio feature extraction and classifiers were evaluated. The results show that this approach is very promising for fault diagnosis of diesel engines. RESUMOO presente trabalho tem como objetivo a análise de técnicas de machine learning aplicadas ao reconhecimento de padrões em arquivos de áudio contendo sons de motores a diesel capturados a partir de um smartphone. Foram gravadas amostras de áudio de 3 motores distintos, em 3 condições de operação diferentes: motor com funcionamento normal, com a mangueira furada e com falha no injetor. Várias combinações de tratamento dos dados, de atributos extraídos do áudio e de classificadores foram testadas. Os resultados mostram que essa abordagem é bastante promissora para diagnosticar falhas em motores a diesel.

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“…However, the acoustic signal was susceptible to environmental noise, and the diagnosis between strong noise and different noise levels was not addressed. Hou and co‐workers [32] suggested an integrated identification algorithm based on multilayer perceptron and genetic algorithm. However, the algorithm was time‐consuming to obtain the best individual and did not consider complex scenes with strong noise.…”

Section: Introductionmentioning

confidence: 99%

Anti‐noise diesel engine misfire diagnosis using a multi‐scale CNN‐LSTM neural network with denoising module

Qin

Jin

Zhang

et al. 2023

CAAI Trans on Intel Tech

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Currently, accuracy of existing diesel engine fault diagnosis methods under strong noise and generalisation performance between different noise levels are still limited. A novel multi‐scale CNN‐LSTM neural network (MSCNN‐LSTMNet) is proposed with a residual‐CNN denoising module for anti‐noise diesel engine misfire diagnosis. First, a residual‐CNN module is designed for denoising the original vibration signal measured from the diesel engine cylinder and residual loss for constructing a new loss function is utilised. Considering the essential characteristics of measured vibration signals at different scales, a multi‐scale convolutional NN (CNN) block is designed to realize multi‐scale feature extraction. Specifically, multiple convolution layers with different branches and different convolution kernel sizes are utilised to extract different time scales features, enhancing the robustness of the model. On this basis, the LSTM is utilised to further extract sequential features for improving anti‐noise and generalisation performances. The effectiveness of MSCNN‐LSTMNet is validated by experimental results of both one‐ and hybrid‐cylinder diesel engine misfires diagnosis under various noise levels and working conditions. The results demonstrate that MSCNN‐LSTMNet achieved much better anti‐noise and generalisation performances than the existing methods. Under strong noise conditions (−10 dB signal‐to‐noise ratio) for four datasets, MSCNN‐LSTMNet obtained 97.561% average accuracy, while average accuracy for random forest, deep neural network, CNN and MSCNNNet were 73.828%, 79.544%, 82.247%, and 89.741%, respectively. Moreover, for 11 noise generalisation tasks between different noise levels, MSCNN‐LSTMNet obtained at least 96.679%, 97.849%, 98.892%, and 94.010% accuracy on the four datasets, which are much higher than those of the existing methods.

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A fault diagnosis model of marine diesel engine cylinder based on modified genetic algorithm and multilayer perceptron

Cited by 25 publications

References 33 publications

An imbalanced sample intelligent fault diagnosis method using data enhancement and improved broad learning system

An imbalanced sample intelligent fault diagnosis method using data enhancement and improved broad learning system

Aplicação de Machine Learning para Diagnóstico de Falha em Motores a Diesel utilizando Sinais de Áudio

Anti‐noise diesel engine misfire diagnosis using a multi‐scale CNN‐LSTM neural network with denoising module

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