activity
20202024
most citedA Hybrid Method for Condition Monitoring and Fault Diagnosis of Rolling Bearings With Low System Delay

66 citations · 73 across the 7 of their papers we have counts for

collaborators

8 papers

eess.SP2024★ 1 cited

Joint Instantaneous Amplitude-Frequency Analysis of Vibration Signals for Vibration-Based Condition Monitoring of Rolling Bearings

Sulaiman Aburakhia, Ismail Hamieh, Abdallah Shami

Vibrations of damaged bearings are manifested as modulations in the amplitude of the generated vibration signal, making envelope analysis an effective approach for discriminating b…

eess.SP2024★ 2 cited

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Sulaiman Aburakhia, Abdallah Shami, George K. Karagiannidis

Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between sign…

eess.SP2023★ 1 cited

On the Peak-to-Average Power Ratio of Vibration Signals: Analysis and Signal Companding for an Efficient Remote Vibration-Based Condition Monitoring

Sulaiman Aburakhia, Abdallah Shami

Vibration-based condition monitoring (VBCM) is widely utilized in various applications due to its non-destructive nature. Recent advancements in sensor technology, the Internet of…

eess.SP2022★ 1 cited

Similarity-Based Predictive Maintenance Framework for Rotating Machinery

Sulaiman Aburakhia, Tareq Tayeh, Ryan Myers +1

Within smart manufacturing, data driven techniques are commonly adopted for condition monitoring and fault diagnosis of rotating machinery. Classical approaches use supervised lear…

eess.SP2022★ 66 cited

A Hybrid Method for Condition Monitoring and Fault Diagnosis of Rolling Bearings With Low System Delay

Sulaiman Aburakhia, Ryan Myers, Abdallah Shami

Vibration-based condition monitoring techniques are commonly used to detect and diagnose failures of rolling bearings. Accuracy and delay in detecting and diagnosing different type…

cs.LG2022★ 1 cited

An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series

Tareq Tayeh, Sulaiman Aburakhia, Ryan Myers +1

As a substantial amount of multivariate time series data is being produced by the complex systems in Smart Manufacturing, improved anomaly detection frameworks are needed to reduce…