An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks

Tran, Van Tung; Althobiani, Faisal; Ball, Andrew

doi:10.1016/j.eswa.2013.12.026

Cited by 355 publications

(168 citation statements)

References 26 publications

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“…Tran et al [9] applied a new type of machine learning architecture namely the deep belief networks (DBN) to diagnose faults in reciprocating compressor valves. By stacking a number of the restricted Boltzmann machines (RBM) layer by layer, DBN is formed as shown in Fig.…”

Section: Latest Development In Artificial Intelligence Techniques Formentioning

confidence: 99%

Vibration Condition Monitoring: Latest Trend and Review

Hui

Hee

Leong

et al. 2015

AMM

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Abstract. Vibration analysis has proven to be the most effective method for machine condition monitoring to date. Various effective signal analysis methods to analyze and extract fault signature that embedded in the raw vibration signals have been introduced in the past few decades such as fast Fourier transform (FFT), short time Fourier transform (STFT), wavelets analysis, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), etc. however, these is still a need for human to interpret vibration signature of faults and it is regarded as one of the major challenge in vibration condition monitoring. Thus, most recent researches in vibration condition monitoring revolved around using Artificial Intelligence (AI) techniques to automate machinery faults detection and diagnosis. The most recent literatures in this area show that researches are mainly focus on using machine learning techniques for data fusion, features fusion, and also decisions fusion in order to achieve a higher accuracy of decision making in vibration condition monitoring. This paper provides a review on the most recent development in vibration signal analysis methods as well as the AI techniques used for automated decision making in vibration condition monitoring in the past two years.

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Section: Latest Development In Artificial Intelligence Techniques Formentioning

confidence: 99%

Vibration Condition Monitoring: Latest Trend and Review

Hui

Hee

Leong

et al. 2015

AMM

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“…It is widely used in many different areas in the recent years, such as graphics processing and language recognition [12][13][14]. DBN is advanced model which can fit the complex nonlinear relationship between attributes in many issues [15,16].…”

Section: Related Workmentioning

confidence: 99%

“…Unlike normal multilayer network models such as NN, the training phrase of DBN is divided into two parts, the pretraining part and the fine-tune part. In the pretraining part, the network is trained layer by layer with a greedy unsupervised method, when the network will find the hidden structure of the input data and initialize the parameters [15][16][17][18][19]. In the fine-tune part, the network is trained by a back propagation supervised method with initialized parameters obtained from the previous part.…”

Section: Introductionmentioning

confidence: 99%

Deep Belief Networks for Fingerprinting Indoor Localization Using Ultrawideband Technology

Luo

Gao

2016

International Journal of Distributed Sensor Networks

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With the increasing requirement of localization services in indoor environment, indoor localization techniques have drawn a lot of attention. In recent years, fingerprinting localization techniques have been proved to be effective in indoor localization tasks. Due to the complexity and variability of indoor environment, some traditional geometric localization techniques based on time of arrival (TOA), received signal strength (RSS), or direction of arrival (DOA) may cause big position errors. Unlike common geometric localization methods, fingerprinting localization techniques estimate the position of target by creating a pattern matching model or regression model for the measurement. Therefore, a suitable learning model is the key of a fingerprinting location system. This paper presents a fingerprinting based localization technique using deep belief network (DBN) and ultrawideband (UWB) signals in an office environment. Some location-dependent parameters extracted from channel impulse response (CIR) are used as signatures to build the fingerprinting database. The construction of DBN which is based on the fingerprinting database is also discussed in this paper. Experiment results show that, with appropriate fingerprinting database and model structure, the location system can get desired accuracy.

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“…As a new kind of machine learning model, DBN primarily simulate the human brain and extract the input data features layer by layer [4,5,6]. Now the DBN has been successfully applied to mechanical fault diagnosis, text, voice, image recognition and other fields [7][8][9][10]. However, it has not been reported that using the DBN to reconstruct the vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearing using Deep Belief Networks

Tao¹,

Liu²,

Yang³

et al. 2015

Proceedings of the 2015 International Symposium on Material, Energy and Environment Engineering

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Abstract. This paper presents an approach to implement vibration signals for fault diagnosis of the rolling bearing. Due to the noise and transient impacts, it is difficulty to accurately diagnosis the faults with traditional methods. So a new type of learning architecture for deep generative model called deep belief networks (DBN) is applied. Since the unsupervised learning ability in DBN, it can extract the features from the raw data layer by layer. This article mainly research how to construct the encoder using DBN which can minimize the energy between the output and input vibration signals. Compared with existing diagnosis techniques, the proposed method can learn a good representation of features with higher accuracy. The results show that DBN can more comprehensively retain the data features in pattern recognition.

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An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks

Cited by 355 publications

References 26 publications

Vibration Condition Monitoring: Latest Trend and Review

Vibration Condition Monitoring: Latest Trend and Review

Deep Belief Networks for Fingerprinting Indoor Localization Using Ultrawideband Technology

Fault Diagnosis of Rolling Bearing using Deep Belief Networks

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