Research Progress and Development Trend of Prognostics and Health Management Key Technologies for Equipment Diesel Engine

Liu, Zichang; Zhang, Cuixuan; Dong, Enlai; Wang, Rongcai; Li, Siyu; Han, Yangming

doi:10.3390/pr11071972

Cited by 5 publications

(4 citation statements)

References 85 publications

(87 reference statements)

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“…PHM technology has come a long way in the last few decades, from the early sensors and data loggers to today's cloud computing and artificial intelligence, which continue to improve the reliability and availability of devices [11]. Li et al studied the application of PHM technology in the field of diesel engines, described the characteristics of traditional diesel engine fault diagnosis, constructed the system architecture of diesel engine PHM, summarized its key technologies, and provided an outlook on data analysis, fault diagnosis, fault EW, and health management [12]. The PHM system is used extensively on the International Space Station (ISS) [13].…”

Section: Introductionmentioning

confidence: 99%

Research on Multi-Parameter Fault Early Warning for Marine Diesel Engine Based on PCA-CNN-BiLSTM

Su,

Gan,

2024

JMSE

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The safe operation of marine diesel engines (MDEs) is an important safeguard for ships and engine crews at sea. In this paper, a combined neural network prediction model (PCA-CNN-BiLSTM) is proposed for the problem of multi-parameter prediction and fault warning for MDEs. PCA is able to reduce the data dimensions and diminish the redundant information in the data, which helps to improve the training efficiency and generalization ability of the model. CNN can effectively extract spatial features from data, assisting in capturing local patterns and regularities in signals. BiLSTM works to process time series data and capture the temporal dependence in the data, enabling prediction of the failure conditions of MDE, condition monitoring, and prediction of a wide range of thermal parameters with more accuracy. We propose a standardized Euclidean distance-based diesel engine fault warning threshold setting method for ships combined with the standard deviation index threshold to set the diesel engine fault warning threshold. Combined with experimental verification, the method can achieve real-time monitoring of diesel engine operating condition and abnormal condition warning and realize diesel engine health condition assessment and rapid fault detection function.

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Section: Introductionmentioning

confidence: 99%

Research on Multi-Parameter Fault Early Warning for Marine Diesel Engine Based on PCA-CNN-BiLSTM

Su,

Gan,

2024

JMSE

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“…In the event of blade fracture, rotor imbalance, rubbing, surges and other faults [1], minor faults may cause equipment failure and production interruption, and serious faults may cause machine damage and fatal accidents, leading to huge economic losses or social impact to enterprises [2,3]. Timely and automatic identification of equipment failure types, to take control and to take preventive measures, is of great significance for reducing or avoiding economic losses in enterprises and preventing catastrophic failures of rotating machinery [4].…”

Section: Introductionmentioning

confidence: 99%

Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating Equipment

Zhou,

Wang,

Xiao

et al. 2023

Lubricants

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The research on fault diagnosis methods based on generative adversarial networks has achieved fruitful results, but most of the research objects are rolling bearings or gears, and the model test data are almost all derived from laboratory bench test data. In the industrial Internet environment, equipment-fault diagnosis is faced with the characteristics of large amounts of data, unbalanced data samples, and inconsistent data file lengths. Moreover, there are few research results on the fault diagnosis of rotor systems composed of shafts, impellers or blades, couplings, and tilting pad bearings. There are still shortcomings in the operational risk evaluation of rotor systems. In order to ensure the reliability and safety of rotor systems, an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty (IACWGAN-GP) model is constructed, a fault diagnosis method based on IACWGAN-GP for tilting pad bearings is proposed, and an intelligent fault diagnosis system platform for equipment in an industrial Internet environment is built. The verification results of engineering case data show that the fault diagnosis model based on IACWGAN-GP can adapt to any length of sequential data files, and the automatic identification accuracy of early faults in tilting pad bearings reaches 98.7%.

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Section: Introductionmentioning

confidence: 99%

“…Because the collection of vibration signals is simple and fast, there is no need to disassemble the diesel engine body and change the diesel engine structure. Usually, the basic idea of fault status identification for diesel engines is firstly, collecting one-dimensional vibration data during the engine working condition, secondly, completing noise reduction and feature extraction of the original signal, and finally, completing fault status identification through a pattern recognition method [4]. Compared with traditional machine learning methods, deep learning has a powerful adaptive feature-learning capability to independently build the desired network model based on the sample data during the learning process, and has received much attention in the field of prognostics and health management [5].…”

Section: Introductionmentioning

confidence: 99%

Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer

Li¹,

Liu²,

Yan³

et al. 2023

Sensors

Self Cite

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The reliability and safety of diesel engines gradually decrease with the increase in running time, leading to frequent failures. To address the problem that it is difficult for the traditional fault status identification methods to identify diesel engine faults accurately, a diesel engine fault status identification method based on synchro squeezing S-transform (SSST) and vision transformer (ViT) is proposed. This method can effectively combine the advantages of the SSST method in processing non-linear and non-smooth signals with the powerful image classification capability of ViT. The vibration signals reflecting the diesel engine status are collected by sensors. To solve the problems of low time-frequency resolution and weak energy aggregation in traditional signal time-frequency analysis methods, the SSST method is used to convert the vibration signals into two-dimensional time-frequency maps; the ViT model is used to extract time-frequency image features for training to achieve diesel engine status assessment. Pre-set fault experiments are carried out using the diesel engine condition monitoring experimental bench, and the proposed method is compared with three traditional methods, namely, ST-ViT, SSST-2DCNN and FFT spectrum-1DCNN. The experimental results show that the overall fault status identification accuracy in the public dataset and the actual laboratory data reaches 98.31% and 95.67%, respectively, providing a new idea for diesel engine fault status identification.

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Research Progress and Development Trend of Prognostics and Health Management Key Technologies for Equipment Diesel Engine

Cited by 5 publications

References 85 publications

Research on Multi-Parameter Fault Early Warning for Marine Diesel Engine Based on PCA-CNN-BiLSTM

Research on Multi-Parameter Fault Early Warning for Marine Diesel Engine Based on PCA-CNN-BiLSTM

Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating Equipment

Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer

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