Machine Learning Algorithms for Fault Detection in Rotating Machinery
Abstrak
Rotating machinery is widely used in industrial applications, where its failure can lead to significant operational downtime and costly repairs. Traditional fault detection techniques, such as vibration analysis and statistical methods, are often insufficient for handling complex fault patterns and large datasets. This paper presents an exploration of machine learning (ML) algorithms for fault detection in rotating machinery. Specifically, it investigates how ML models can enhance predictive maintenance systems, detect anomalies, and classify faults in real-time. The paper reviews the application of various ML algorithms, including support vector machines (SVM), decision trees, neural networks, and ensemble methods. The benefits of using vibration signals, acoustic signals, and other sensor data for training ML models are discussed. Finally, case studies and future trends in the use of deep learning for fault detection in rotating machinery are also explored.
Penulis (1)
Dr. John Doe
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Sumber Database
- CrossRef
- DOI
- 10.71465/ajmet2134
- Akses
- Open Access ✓