Haobin Wen
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View article: Standardisation of Vibration-based Parameters for Rotor and Bearing for Machine Faults Detection Using Machine Learning Model
Standardisation of Vibration-based Parameters for Rotor and Bearing for Machine Faults Detection Using Machine Learning Model Open
Purpose In vibration-based condition monitoring of rotating machinery, machine learning (ML) models have demonstrated significant diagnostic capabilities; however, their efficacy is fundamentally constrained by the selection and quality of…
View article: The Effect of Defect Size and Location in Roller Bearing Fault Detection: Experimental Insights for Vibration-Based Diagnosis
The Effect of Defect Size and Location in Roller Bearing Fault Detection: Experimental Insights for Vibration-Based Diagnosis Open
In rotating machines, any faults in anti-friction bearings occurring during operation can lead to failures that are unacceptable due to considerable downtime losses and maintenance costs. Hence, early fault detection is essential, and diff…
View article: From Envelope Spectra to Bearing Remaining Useful Life: An Intelligent Vibration-Based Prediction Model with Quantified Uncertainty
From Envelope Spectra to Bearing Remaining Useful Life: An Intelligent Vibration-Based Prediction Model with Quantified Uncertainty Open
Bearings are pivotal components of rotating machines where any defects could propagate and trigger systematic failures. Once faults are detected, accurately predicting remaining useful life (RUL) is essential for optimizing predictive main…
View article: Fault Detection of Rotating Machines Using poly-Coherent Composite Spectrum of Measured Vibration Responses with Machine Learning
Fault Detection of Rotating Machines Using poly-Coherent Composite Spectrum of Measured Vibration Responses with Machine Learning Open
This study presents an efficient vibration-based fault detection method for rotating machines utilising the poly-coherent composite spectrum (pCCS) and machine learning techniques. pCCS combines vibration measurements from multiple bearing…
View article: Sparse Tensor Decomposition of Multi-Sensory Data for Fault Localization in Rotating Machinery Health Monitoring
Sparse Tensor Decomposition of Multi-Sensory Data for Fault Localization in Rotating Machinery Health Monitoring Open
Multi-sensor monitoring is prevalent in modern structural health monitoring (SHM) practice. As the number of sensors and sampling requirements increase, a monitoring sensor network can generate substantial data which are high-volume and hi…
View article: Early Prediction of Remaining Useful Life for Rolling Bearings Based on Envelope Spectral Indicator and Bayesian Filter
Early Prediction of Remaining Useful Life for Rolling Bearings Based on Envelope Spectral Indicator and Bayesian Filter Open
On top of the condition-based maintenance (CBM) practice for rotating machinery, the robust estimation of remaining useful life (RUL) for rolling-element bearings (REB) is of particular interest. The failure of a single bearing often resul…
View article: Adaptive Band Extraction Based on Low Rank Approximated Nonnegative Tucker Decomposition for Anti-Friction Bearing Faults Diagnosis Using Measured Vibration Data
Adaptive Band Extraction Based on Low Rank Approximated Nonnegative Tucker Decomposition for Anti-Friction Bearing Faults Diagnosis Using Measured Vibration Data Open
Condition monitoring and fault diagnosis are topics of growing interest for improving the reliability of modern industrial systems. As critical structural components, anti-friction bearings often operate under harsh conditions and are cont…
View article: Feature Extraction Using Sparse Kernel Non-Negative Matrix Factorization for Rolling Element Bearing Diagnosis
Feature Extraction Using Sparse Kernel Non-Negative Matrix Factorization for Rolling Element Bearing Diagnosis Open
For early fault detection of a bearing, the localized defect generally brings a complex vibration signal, so it is difficult to detect the periodic transient characteristics from the signal spectrum using conventional bearing fault diagnos…
View article: Feature Extraction of Impulse Faults for Vibration Signals Based on Sparse Non-Negative Tensor Factorization
Feature Extraction of Impulse Faults for Vibration Signals Based on Sparse Non-Negative Tensor Factorization Open
The incipient damages of mechanical equipment excite weak impulse vibration, which is hidden, almost unobservable, in the collected signal, making fault detection and failure prevention at the inchoate stage rather challenging. Traditional…