Abstract
The development of technologies for the maintenance industry has taken an important role to meet the demanding challenges. One of the important challenges is to predict the defects, if any, in machines as early as possible to manage the machines downtime. The vibration-based condition monitoring (VCM) is well-known for this purpose but requires the human experience and expertise. The machine learning models using the intelligent systems and pattern recognition seem to be the future avenue for machine fault detection without the human expertise. Several such studies are published in the literature. This paper is also on the machine learning model for the different machine faults classification and detection. Here the time domain and frequency domain features derived from the measured machine vibration data are used separated in the development of the machine learning models using the artificial neutral network method. The effectiveness of both the time and frequency domain features based models are compared when they are applied to an experimental rig. The paper presents the proposed machine learning models and their performance in terms of the observations and results.
Highlights
The increased application of vibration-based condition monitoring (VCM) in industry has been reflected in the continued development of technologies and instrumentation, as well data processing and analysis techniques
There, while the best result for time domain features is at B1, for frequency features it is achieved at B2, and the poorest at B4
The highest accuracy achieved is 100% for the time domain features compared to the frequency domain features (99.7% accuracy only)
Summary
The increased application of vibration-based condition monitoring (VCM) in industry has been reflected in the continued development of technologies and instrumentation, as well data processing and analysis techniques. Artificial neural networks (ANN) have been beneficially applied in mechanical systems due their suitability for complex sensor data processing problems [1]. The time domain and frequency domain features derived from the measured machine vibration data are used separated in the development of the models using the artificial neutral network method. The effectiveness of both the time and frequency domain features based models are compared when they are applied to an experimental rig. The paper presents the proposed machine learning models and their performance in terms of the observations and results
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