FAULT DETECTION – CLASSIFICATION THROUGH VIBRATION MONITORING USING ARTIFICIAL NEURAL NETWORKS | ||||
| ERJ. Engineering Research Journal | ||||
| Article 1, Volume 27, Issue 1, January 2004, Page 1-8 PDF (1.9 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/erjm.2004.82599 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Yehia El-Mashad; Atef Hassan | ||||
| Associate Professor, Shoubra Faculty of Engineering | ||||
| Abstract | ||||
| Vibration analysis can give an indication of the condition of rotating shaft highlighting potential fault such as unbalance and rubbing. Faults may however occur intermittently and consequently to detect these requires continuous monitoring with real time analysis. In this research, we describe how to use Artificial Neural Networks (ANN's) for classification of machine conditions by using two sensor techniques. In this technique, calculated moments from times series are used as input features as they can be quickly computed from measured data. Orthogonal vibrations are considered as two -dimension victor, the magnitude of which can be expressed as time series. Some signal processing operations are applied to the data to enhance the differences between signals. A fault signature data base is built which includes vibration signature of common failure modes of critical components in rotating equipment. The database is used to train the neural network to classify the different fault classes. Such expert system has some limitations because it is tailored to a specific machine and specific faults under certain operating conditions. Comparison is made with frequency domain analysis methods, which has some ambiguities when components may, more or less overlap and certain faults may exhibit themselves in different ways in spectrum. The results show that the success of the network is highly dependent on the deduced feature signal which contain the symptoms of faults and healthy operation. | ||||
| Keywords | ||||
| Condition Monitoring; Signal Analysis; Vibration Measurements; Artificial Neural Networks | ||||
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