Smart contracts offer automation for various decentralized applications but suffer from vulnerabilities that cause financial losses. Detecting vulnerabilities is critical to safeguarding decentralized applications before deployment. Automatic detection is more efficient than manual auditing of large codebases. Machine learning (ML) has emerged as a suitable technique for vulnerability detection. However, a systematic literature review (SLR) of ML models is lacking, making it difficult to identify research gaps. No published systematic review exists for ML approaches to smart contract vulnerability detection. This research focuses on ML-driven detection mechanisms from various databases. 46 studies were selected and reviewed based on keywords. The contributions address three research questions: vulnerability identification, machine learning model approaches, and data sources. In addition to highlighting gaps that require further investigation, the drawbacks of machine learning are discussed. This study lays the groundwork for improving ML solutions by mapping technical challenges and future directions.

This paper provides a method of acoustic emission (AE) technique to detect a train bearing fault of tapered bearing unit (TBU). An approach is to utilize acoustic emission signals which were captured from piezoelectric transducer and processed using Fourier transform. The transformed signals may contain unique characteristic features relating to the various types of bearing faults. The experiments on different operating conditions were investigated and they corresponded to (a) a normal bearing and (b) outer race defect bearing. The result is promising for faulty bearing identification and discrimination between different bearing conditions.

This article provides a method of acoustic emission (AE) technique to detect leakage in pipeline and locate the position of the leakage. The AE sensor is made of piezoelectric effect transducer to pick up the acoustic emission signal which is generated from the turbulent flow at the leak position. The signal conditioning unit is used to enhanced and eliminate the background noise from the leak location sources. The main acoustic emission processing unit is used to acquire and process the extracted AE characteristic parameters from preprocessing waveform. The leak pipeline is simulated by drilling the hole and plugged with M8 screw at different locations. The results show that the proposed AE method can detect and locate simultaneous leak condition in pipeline with promising results.