Abstract
PurposeThe development and application of intelligent models to perform vibration-based condition monitoring in industry seems to be receiving attention in recent years. A number of such research studies using the artificial intelligence, machine learning, pattern recognition, etc., are available in the literature on this topic. These studies essentially used the machine vibration responses with known machine faults to develop smart fault diagnosis models. These models are yet to be tested for all kinds of machine faults and/or different operating conditions. Therefore, the purpose is to develop a generic machine faults diagnosis model that can be applied blindly to any identical machines with high confidence level in accuracy of the predictions.MethodsIn this paper, a supervised smart fault diagnosis model is developed. This model is developed using the available measured vibration responses for the different rotor faults simulated on an experimental rotating rig operating at a constant speed. The developed smart vibration-based machine learning (SVML) model is then blindly tested to identify the healthy and faulty conditions of the rig when operating at different speeds.Results and conclusions Several scenarios are proposed and examined during the development of the SVML model. It is observed that scenario of the vibration measurements simultaneously from all bearings from a machine is capable to fully map the machine dynamics in the VML model. Therefore, this developed when applied blindly to the sets of data at a different machine speed, the results are observed to be encouraging. The results clearly show a possibility for a centralised vibration-based condition monitoring (CVCM) model for identical machines operating at different rotating speeds.
Highlights
Vibration-based condition monitoring (VCM) in rotating machines has been successfully applied in industry for fault detection and diagnosis
Overall performances achieved at all the studied scenarios are presented with the machine conditions in Fig. 5 and diagnoses by each VML model are summarised in Tables 6, 7, 8, 9, 10, 11 and 12
It is important to note that the performance of the VML model, S2_30_30_B3 is showing much better compared to other models at bearings B1, B2 and B4
Summary
Vibration-based condition monitoring (VCM) in rotating machines has been successfully applied in industry for fault detection and diagnosis. Knowledge-based approaches, such as machine learning (ML) models stand out among the developed methods, since their lack of dependency on the expertise or knowledge level of a person to generate the correlations between the identified symptoms and their associated faults. They are characterised by their capability of exploring and learning from empirical data [1]. This ability is a powerful tool for fault identification, for instance through the performance of pattern recognition. These methods can be classified according their learning process, which could be either unsupervised or supervised
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