A summary of fault modelling and predictive health monitoring of rolling element bearings
I El-Thalji, E Jantunen - Mechanical systems and signal processing, 2015 - Elsevier
The rolling element bearing is one of the most critical components that determine the
machinery health and its remaining lifetime in modern production machinery. Robust …
machinery health and its remaining lifetime in modern production machinery. Robust …
Artificial intelligence application in fault diagnostics of rotating industrial machines: A state-of-the-art review
The fault monitoring and diagnosis of industrial machineries are very significant in Industry
4.0 revolution but are often complicated and labour intensive. The application of artificial …
4.0 revolution but are often complicated and labour intensive. The application of artificial …
Multi-sensor data fusion using support vector machine for motor fault detection
TP Banerjee, S Das - Information Sciences, 2012 - Elsevier
Motor fault diagnosis in dynamic condition is a typical multi-sensor data fusion problem. It
involves the use of information collected from multiple sensors, such as vibration, sound …
involves the use of information collected from multiple sensors, such as vibration, sound …
Application of relevance vector machine and logistic regression for machine degradation assessment
W Caesarendra, A Widodo, BS Yang - Mechanical Systems and Signal …, 2010 - Elsevier
Degradation parameter or deviation parameter from normal to failure condition of machine
part or system is needed as an object of prediction in prognostics method. This study …
part or system is needed as an object of prediction in prognostics method. This study …
Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance
This paper presents the application of a state space model (SSM) for prognostics of an
engineering system subject to degradation. A health index (HI) is inferred from a set of …
engineering system subject to degradation. A health index (HI) is inferred from a set of …
Machine health prognostics using survival probability and support vector machine
A Widodo, BS Yang - Expert Systems with Applications, 2011 - Elsevier
Prognostic of machine health estimates the remaining useful life of machine components. It
deals with prediction of machine health condition based on past measured data from …
deals with prediction of machine health condition based on past measured data from …
Fault diagnosis in industrial chemical processes using interpretable patterns based on Logical Analysis of Data
This paper applies the Logical Analysis of Data (LAD) to detect and diagnose faults in
industrial chemical processes. This machine learning classification technique discovers …
industrial chemical processes. This machine learning classification technique discovers …
Application of relevance vector machine and survival probability to machine degradation assessment
A Widodo, BS Yang - Expert Systems with Applications, 2011 - Elsevier
Condition monitoring (CM) of machines health or industrial components and systems that
can detect, classify and predict the impending faults is critical in reducing operating and …
can detect, classify and predict the impending faults is critical in reducing operating and …
PCA-SVM method with sliding window for online fault diagnosis of a small pressurized water reactor
H Cao, P Sun, L Zhao - Annals of Nuclear Energy, 2022 - Elsevier
Traditional fault diagnosis methods based on signals and operation procedures in small
pressurized water reactors (SPWRs) exhibit low efficiency. Complex alarm signals cannot be …
pressurized water reactors (SPWRs) exhibit low efficiency. Complex alarm signals cannot be …
Recent advances in the theory and practice of logical analysis of data
Abstract Logical Analysis of Data (LAD) is a data analysis methodology introduced by Peter
L. Hammer in 1986. LAD distinguishes itself from other classification and machine learning …
L. Hammer in 1986. LAD distinguishes itself from other classification and machine learning …