Machine fault detection methods based on machine learning algorithms: A review.

Area of Science:

• Mechanical Engineering
• Data Science
• Artificial Intelligence

Background:

• Industrial machinery degrades over time, leading to reduced efficiency and potential breakdowns.
• Production stoppages due to mechanical failures result in significant financial losses for companies.
• Proactive identification of wear and tear is essential for efficient machine maintenance.

Purpose of the Study:

• To examine methodologies for identifying common mechanical failures in industrial machinery.
• To analyze widely applied machine learning algorithms for preventive fault detection.
• To review current research and identify future challenges in machine learning-based fault prediction.

Main Methods:

• Review of various machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN).
• Analysis of systems combining low-cost sensors with machine learning for fault detection.
• Detailed examination of scientific literature on mechanical failure identification.

Main Results:

• Machine learning algorithms show significant promise in the preventive identification of mechanical failures.
• Different algorithms like SVM, ANN, CNN, and RNN offer distinct strengths in fault detection.
• The study highlights the effectiveness of integrating sensor data with advanced computational models.

Conclusions:

• Machine learning offers powerful tools for predicting mechanical part failures, minimizing production downtime.
• Continued research into algorithms like Deep Generative Systems is crucial for advancing fault prediction capabilities.
• The review provides a foundation for future work in developing more robust and accurate predictive maintenance systems.