Symbolic Important Point Perceptually and Hidden Markov Model Based Hydraulic Pump Fault Diagnosis Method

Jia, Yunzhao; Xu, Min; Wang, Rixin

doi:10.3390/s18124460

Cited by 22 publications

(17 citation statements)

References 25 publications

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“…Recently, most of the researches on intelligent diagnosis for pump have focused on traditional intelligent methods [112], [113]. There are few researches on application of DNN-based methods in pump fault diagnosis, especially on CNN-based approaches.…”

Section: Cnn-based Fault Diagnosis For Pumpmentioning

confidence: 99%

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

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Rotating machinery is of vital importance in the field of engineering, including aviation and navigation. Its failure will lead to severe loss to personnel safety and the stability of the equipment system. It is a long way to investigate the relevant fault diagnosis method, especially the intelligent fault diagnosis method on the basis of deep learning. In consideration of the limitations of traditional fault diagnosis approaches based on shallow layer network structure, the methods based on deep neural network (DNN) are worthy of thorough exploration. As a common DNN with special structure, deep convolutional neural network is of great concern in intelligent fault diagnosis due to its advantages in processing nonlinear problems. This review will play an emphasis on convolutional neural network (CNN). The basic structure and principle are introduced. The applications of CNN-based fault diagnosis method in rotating machinery are summarized and analyzed. Furthermore, the diagnosis performance and potential mechanism from different CNN methods are discussed. In the end, this review is highlighted on the challenges and the potential key points in research on novel intelligent fault diagnosis strategies. The corresponding analysis and discussion will provide some references and lay the foundation for the investigation in related fields. INDEX TERMS Deep learning, convolutional neural network, intelligent fault diagnosis, rotating machinery.

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Section: Cnn-based Fault Diagnosis For Pumpmentioning

confidence: 99%

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

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“…We consider an HMM with the hidden state number S = 2. Where b i,t = (b i,t,1 , b i,t,2 ) , in which b i,t,1 and b i,t,2 are independently generated from Bernoulli(0.5) [17];…”

Section: Empirical Simulationmentioning

confidence: 99%

Detection of Factors Affecting State Transition Based on Non-Homogeneous Markov Chain Model

Xiujuan

Liu

et al. 2021

IEEE Access

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The dependent and independent variables in traditional linear regression models are continuous numerical variables. When the dependent variable or independent variable is a discrete variable, the traditional linear regression model can no longer be used to analyze. To solve this problem, this article introduces the non-homogeneous Markov chain model. It introduces the mathematical definition of the non-homogeneous Markov chain model. And then this article uses Bayesian estimation method to derive posterior distribution of model parameters. Through the MCMC algorithm, we simulate an experiment, posterior means value of the parameters is estimated, and the estimation effect is found to be better. Finally, we analyze the impact of learning state transition about college students on the non-homogeneous Markov chain model. Influencing factors include whether to receive a scholarship and whether to serve as a class leader. In this paper, non-homogeneous Markov chain model is used to analyze and detect the impact of discrete variables on dependent variables. This is the major innovation in this article. INDEX TERMSNon-homogeneous Markov chain model, Bayesian estimation, MCMC algorithm, state transition.

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“…In order to diagnose faults in hydraulic pumps while working with inaccurate information, Jia et al [21] propose a data-driven fault diagnosis method based on SPIP and HMM. Their experimental platform adopts the NI pxi-8880 sensor system to collect vibration signals from a hydraulic pump with a sampling rating is 10000 Hz and a rotation speed is 2900 RPM.…”

Section: B Vibration Monitoringmentioning

confidence: 99%

Overview of Applications of the Sensor Technologies for Construction Machinery

Jiang

2020

IEEE Access

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In recent years, great development in sensor technologies have resulted in substantial progress in the construction efficiency and the degree of automation of construction machinery have made great progress. Applications of the sensor technologies, such as condition monitoring, autonomous construction and the Internet of Things (IoT), have been extensively studied for construction machinery. Firstly, this paper discusses the condition monitoring technology in construction machinery, which mainly involves the monitoring of oil pollution and leakage, vibration monitoring, emission monitoring and wholesystem monitoring. Second, regarding autonomous construction technology, this paper conducts analysis and discussion on linear motion, rotary motion, navigation/positioning systems, and visual systems. Third, research on IoT technology in construction machinery is comprehensively discussed, especially in term of life cycle maintenance, construction management and safety management. Finally, higher reliability, the development of smart sensors and big data management of sensor information are described as prospective issues that must be addressed for future construction machinery. INDEX TERMS Construction equipment, condition based maintenance (CBM), global navigation satellite system (GNSS), building information modelling (BIM), Internet of Things (IoT) This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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Symbolic Important Point Perceptually and Hidden Markov Model Based Hydraulic Pump Fault Diagnosis Method

Cited by 22 publications

References 25 publications

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

Detection of Factors Affecting State Transition Based on Non-Homogeneous Markov Chain Model

Overview of Applications of the Sensor Technologies for Construction Machinery

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