Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review

Applications of machine learning to machine fault diagnosis: A review and roadmap

(2020) Yaguo Lei et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Assessment of characteristics of acoustic emission parameters for valve damage detection under varying compressor speeds

(2020) Hoi Yin Sim et al.   PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE

Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor

(2019) Diego Cabrera et al.   NEUROCOMPUTING

Detection and estimation of valve leakage losses in reciprocating compressor using acoustic emission technique

(2019) Hoi Yin Sim et al.   MEASUREMENT

Artificial intelligence for fault diagnosis of rotating machinery: A review

(2018) Ruonan Liu et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Fault-diagnosis for reciprocating compressors using big data and machine learning

(2018) Guanqiu Qi et al.   SIMULATION MODELLING PRACTICE AND THEORY

A survey on Deep Learning based bearing fault diagnosis

(2018) Duy-Tang Hoang et al.   NEUROCOMPUTING

Machine learning methods for wind turbine condition monitoring: A review

(2018) Adrian Stetco et al.   RENEWABLE ENERGY

An image-based pattern recognition approach to condition monitoring of reciprocating compressor valves

(2017) Jason R Kolodziej et al.   JOURNAL OF VIBRATION AND CONTROL

An enhancement deep feature fusion method for rotating machinery fault diagnosis

(2017) Haidong Shao et al.   KNOWLEDGE-BASED SYSTEMS

Semi-supervised vibration-based classification and condition monitoring of compressors

(2017) Primož Potočnik et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network

(2017) Van Tung Tran et al.   PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE

Fault detection in reciprocating compressor valves under varying load conditions

(2016) Kurt Pichler et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

(2015) Zhiwei Gao et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Deep learning

(2015) Yann LeCun et al.   NATURE

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

(2014) Van Tung Tran et al.   EXPERT SYSTEMS WITH APPLICATIONS

Detecting cracks in reciprocating compressor valves using pattern recognition in the pV diagram

(2014) Kurt Pichler et al.   PATTERN ANALYSIS AND APPLICATIONS

A Data-Driven Approach for Condition Monitoring of Reciprocating Compressor Valves

(2013) Christopher J. Guerra et al.   JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME

A novel scheme for fault detection of reciprocating compressor valves based on basis pursuit, wave matching and support vector machine

(2012) Qiang Qin et al.   MEASUREMENT

A recognition and novelty detection approach based on Curvelet transform, nonlinear PCA and SVM with application to indicator diagram diagnosis

(2011) Kun Feng et al.   EXPERT SYSTEMS WITH APPLICATIONS

Natural computing for mechanical systems research: A tutorial overview

(2010) Keith Worden et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Support Vector Machines for classification and regression

(2009) Richard G. Brereton et al.   ANALYST

Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method

(2009) Houxi Cui et al.   JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES

Automated valve condition classification of a reciprocating compressor with seeded faults: experimentation and validation of classification strategy

(2009) Yih-Hwang Lin et al.   Smart Materials and Structures

Fault diagnosis for diesel valve trains based on time–frequency images

(2008) Chengdong Wang et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING