Blind Application of Developed Smart Vibration-Based Machine Learning (SVML) Model for Machine Faults Diagnosis to Diffe
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ORIGINAL PAPER
Blind Application of Developed Smart Vibration‑Based Machine Learning (SVML) Model for Machine Faults Diagnosis to Different Machine Conditions Natalia F. Espinoza Sepúlveda1 · Jyoti K. Sinha1 Received: 11 February 2020 / Revised: 21 August 2020 / Accepted: 16 September 2020 © The Author(s) 2020
Abstract Purpose The 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. Methods In 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 vibrationbased condition monitoring (CVCM) model for identical machines operating at different rotating speeds. Keywords Machine fault diagnosis · Vibration analysis · Machine learning · Artificial neural network · Pattern recognition
Introduction Vibration-based condition monitoring (VCM) in rotating machines has been successfully applied in industry for fault detection and diagnosis. However, the current and future approaches in the VCM are the research topic due to rapid changes in the technologies and instrumentation, including techniques for data processing and analysis. * Jyoti K. Sinha [email protected] Natalia F. Espinoza Sepúlveda [email protected] 1
Dynamics Laboratory, Department of Mechanical, Aerospace and Civil Engineering (MACE), School of Engineering, The University of Manchester, Manchester M13 9PL, UK
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 ge
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