title: Intelligent Approaches for the Improved Condition Monitoring of Rolling Element Bearings
Creators: name: Kannan, Vigneshwar
Publisher: name: Griffith University
Year: 2022
Subjects: subject: rolling element bearings
subject: rotational machinery
subject: condition-based monitoring

description: Rotational machinery is used for various applications and is commonplace in industry and aviation sectors. Rolling Element Bearings (REBs) play a vital part in the optimal functioning of rotational machinery. They can naturally develop faults through standard operation of machinery over time or at an accelerated rate due to external factors. Undetected faults in REBs that have not been addressed with the appropriate maintenance actions can cause severe damage to the machine. Maintenance and repairs once damage reaches this stage will be relatively more expensive. This will lead to unexpected machine downtime which could affect revenue dependant on the operation of these machines. Furthermore, such machine damage could cause machinery to behave erratically making it dangerous for those in the immediate vicinity. Therefore, it is essential to ensure the REB used are in good condition. Traditionally, maintenance was scheduled to occur at a certain time when components were expected to fail. However, this would mean that faults occurring sooner than expected will affect machinery operations. It is also possible for these components to remain in good condition past its expected use making maintenance actions unnecessary. A more cost-effective solution that was since developed was condition-based maintenance. This involved monitoring the health condition of machinery through sensor output using appropriate techniques. Condition monitoring can be achieved using various sensors, each presenting their own advantages. Modern approaches to condition monitoring have also utilised multiple sensors for improved detection and diagnosis of faults. The aim of this research was to develop intelligent approaches for the improved accuracy and reliability of REB condition monitoring. Three gaps in literature were identified and addressed in this thesis. Envelope analysis is a technique that has been widely used for the detection and diagnosis of bearing defects. Defect related frequencies are uncovered through the process by selecting an ideal frequency band for demodulation. The use of this frequency band yields an envelope spectrum with more pronounced defect frequencies. Many studies have been carried out for the selection of an ideal frequency band and is considered a major challenge. To address this issue, a real-coded genetic algorithm (GA) with a novel fitness function and crossover selection method was developed. The proposed method was tested on bearing data from two different rotor-bearing systems and was proven to be capable of fault detection. The method was also benchmarked against the fast kurtogram method, a well-established approach used in the fault detection of bearings. The comparison showed that although a much better demodulation band could be identified using the proposed method, the fast kurtogram method obtained results much faster. Harsh operating conditions and environmental factors can affect information acquired by sensors. This can, in turn, negatively influence the accuracy of fault detection and diagnosis. While signal distortions can be distinguished through visual inspection, this is not always practical especially with the rise of automated condition monitoring systems. Research in the area of signal quality assessment is generally limited in the context of machinery condition monitoring. An anomaly detection method was proposed for the detection of vibration signal quality issues and was intended to be used as a pre-processing step to fault diagnosis. This involved the training of a one-class support vector machine (OCSVM) on features pertaining to good quality signals from various machine health conditions. It was found that the approach worked very well in identifying signal issues for trained health conditions. Though misclassifications occurred for untrained conditions, it could be observed that they fell close to the decision boundary generated by the OCSVM. A countermeasure was also proposed to allow untrained condition signals of good quality to be correctly classified by relaxing the decision boundary. Condition monitoring of machinery using multiple sensors has become quite common due to their increasing availability. Data from different sensors are commonly fused at one of the three information fusion levels to attain a more reliable diagnosis on machine health. It is likely that in the event of sensor issues or sensor failure, the reliability of the sensor output will suffer. Distortions in signals can easily pollute the overall classification and go unnoticed due to a majority of these monitoring systems operating automatically. To address this problem, an approach that was capable of assigning a lower influence to low quality sensor signals in the overall diagnosis was proposed. This involved first computing integrity scores for each sensor in the system using OCSVMs. Individual health classifications for each sensor were derived using support vector machines (SVMs). Finally, fusion was conducted at the decision-level by aggregating the integrity scores of corresponding sensors for each class from which the highest was considered the overall classification. This method was validated on a heterogeneous sensor system that consisted of a triaxial-accelerometer and one Acoustic Emission (AE) sensor. The data used in testing included different machine conditions with signals that were of both good and poor quality. Testing showed that a high reliability was achieved through such an approach. This shows that despite some sensor signals being unreliable, the method would be able to accurately distinguish this and reduce its influence on the final diagnosis. The research gaps identified in condition monitoring of REB were successfully addressed using intelligent algorithms. The presented solutions can be implemented for an increased accuracy and reliability of fault detection and diagnosis of bearings, thereby achieving the aim of the study.