Health monitoring techniques for rotating machinery.

The present research is concerned with health monitoring techniques for rotating machinery, for example Turbogenerator (TG) sets in the power industry. Vibration based condition monitoring is widely accepted for rotating machinery and hence the vibration response of a machine is again utilized in the present research study. Experience shows that faults develop in rotating machines during normal operation and hence their quick identification and remedy are important from safety and plant productivity considerations. The vibration based fault identification procedures are well developed for rotating machinery. However the quantification part of the identified faults has still not matured, and is an ongoing research topic. Hence the remedial action is usually time consuming, even though the machine is known to have some known faults, due to lack of knowledge of their locations and the extent of the faults. In general such a quantification of the identified fault relies on the mathematical model of the complete system along with the measured vibration response of the system. Rotating machinery consists of three major parts - a rotor, fluid journal bearings and a foundation which is often flexible. Often a good model of the rotor (usually a finite element model) and an adequate model of the fluid bearings may be constructed. However, a reliable model for the foundation is difficult to construct due to a number of practical difficulties. Hence the present study has concentrated on two objectives - reliable modelling for the foundation and the quantification of faults using the measured vibration response at the bearing pedestals and the mathematical model of the rotor and the fluid bearings. For the foundation model, the theory which was developed to estimate the models for flexible foundation has been described in the thesis. The method uses measured vibration response at bearing pedestals during machine run-downs, a priori rotor and journal bearing models, and a knowledge of the rotor unbalance, to estimate the stiffness, damping and mass matrices of the foundation. The method was tested on both simulated and experimental examples. The prediction capability of the estimated foundation model was also demonstrated. For the fault estimation a different approach has been used. It has been assumed that the foundation mathematical model is not known, and it is demonstrated that the two faults - the state of rotor unbalance and the misalignment in the rotor can be estimated reliably. The theory of the proposed methods is discussed in the thesis. The method uses measured vibration response at bearing pedestals during a single machine run-down, and a priori rotor and journal bearing models, to estimate the rotor unbalance and the misalignment along with the foundation parameters, so that the dynamics of the foundation is also accounted for during the estimation. The methods were tested on simulated and experimental examples and the estimation accuracy was found to be excellent and generally robust to errors in the rotor and bearing models

Blind Application of Developed Smart Vibration-Based Machine Learning (SVML) Model for Machine Faults Diagnosis to Different Machine Conditions

From Springer Nature via Jisc Publications RouterHistory: received 2020-02-11, rev-recd 2020-08-21, accepted 2020-09-16, registration 2020-09-16, pub-electronic 2020-10-07, online 2020-10-07, pub-print 2021-06Publication status: PublishedFunder: University of ManchesterAbstract: 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 vibration-based condition monitoring (CVCM) model for identical machines operating at different rotating speeds