Online Bearing Clearance Monitoring Based on an Accurate Vibration Analysis

Advanced Methods in Rotating Machines

The motions of power sources in industrial applications were always provided by electromechanical systems, which use around 70% of the gross energy consumption of industrialized economies [...]

An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines

The feasibility and usefulness of frequency domain fusion of data from multiple vibration sensors installed on typical industrial rotating machines, based on coherent composite spectrum (CCS) as well as poly-coherent composite spectrum (pCCS) techniques, have been well-iterated by earlier studies. However, all previous endeavours have been limited to rotor faults, thereby raising questions about the proficiency of the approach for classifying faults related to other critical rotating machine components such as gearboxes. Besides the restriction in scope of the founding CCS and pCCS studies on rotor-related faults, their diagnosis approach was manually implemented, which could be unrealistic when faced with routine condition monitoring of multi-component industrial rotating machines, which often entails high-frequency sampling at multiple locations. In order to alleviate these challenges, this paper introduced an automated framework that encompassed feature generation through CCS, data dimensionality reduction through principal component analysis (PCA), and faults classification using artificial neural network (ANN). The outcomes of the automated approach are a set of visualised decision maps representing individually simulated scenarios, which simplifies and illustrates the decision rules of the faults characterisation framework. Additionally, the proposed approach minimises diagnosis-related downtime by allowing asset operators to easily identify anomalies at their incipient stages without necessarily possessing vibration monitoring expertise. Building upon the encouraging results obtained from the preceding part of this approach that was limited to well-known rotor-related faults, the proposed framework was significantly extended to include experimental and open-source gear fault data. The results show that in addition to early established rotor-related faults classification, the approach described here can also effectively and automatically classify gearbox faults, thereby improving the robustness.

Vibration-Based Diagnostics of Radial Clearances and Bolts Loosening in the Bearing Supports of the Heavy-Duty Gearboxes

The problem solved in this research is the diagnosis of the radial clearances in bearing supports and the loosening of fastening bolts due to their plastic elongation (creep) or weak tightening using vibration signals. This is an important issue for the maintenance of the heavy-duty gearboxes of powerful mining machines and rolling mills working in non-stationary regimes. Based on a comprehensive overview of bolted joint diagnostic methods, a solution to this problem based on a developed nonlinear dynamical model of bearing supports is proposed. Diagnostic rules are developed by comparing the changes of natural frequency and its harmonics, the amplitudes and phases of shaft transient oscillations. Then, the vibration signals are measured on real gearboxes while the torque is increasing in the transmission during several series of industrial trials under changing bearings and bolts conditions. In parallel, dynamical torque is measured and its interrelation with vibration is determined. It is concluded that the radial clearances are the most influencing factors among the failure parameters in heavy-duty gearboxes of industrial machines working under impulsive and step-like loading. The developed diagnostics algorithm allows condition monitoring of bearings and fastening bolts, allowing one to undertake timely maintenance actions to prevent failures.

Multi-Dimensional Mathematical Wear Models of Vibration Generated by Rolling Ball Bearings Made of AISI 52100 Bearing Steel

The paper features the development of multi-dimensional mathematical models used for evaluating the impact of selected factors on the vibration generated by 6304ZZ type rolling ball bearings from three manufacturers in the aspect of the wear process. The bearings were manufactured of AISI 52100 bearing steel. The analyzed factors included the inner and outer raceways' roundness and waviness deviations, radial clearance and the total curvature ratio. The models were developed for vibration recorded in three frequency ranges: 50-300 Hz, 300-1800 Hz and 1800-10,000 Hz. The paper includes a specification of the principles of operation of innovative measuring systems intended for testing bearing vibration, raceway geometries and radial clearance. Furthermore, it features a specification of particular stages of the multi-dimensional mathematical models' development and verification. Testing with the purpose of statistical evaluation of the analyzed factors is also presented. The test results and mathematical models indicate that the inner raceway's waviness deviation had a dominant impact on the vibration examined in all frequencies. The roundness and waviness deviation of bearing raceways made of AISI 52100 steel propagates the bearing wear process.

Vibration Characteristics of Rolling Element Bearings with Different Radial Clearances for Condition Monitoring of Wind Turbine

Rolling element bearing is a vital component in rotating machinery, such as a wind turbine (WT) system. By accurately monitoring its health condition, the faults can be detected at an early stage, providing sufficient lead time to perform maintenance and hence reducing accidents and economic losses. Bearing usually suffers from various wears and tears, which result in a gradual increase in clearance through its lifetime. Insufficient understanding of vibration characteristics under different clearances brings difficulties for bearing condition monitoring. Thus, this paper presents a nonlinear bearing vibration model with six degrees of freedom (DOF) to investigate the vibration characteristics under different radial clearances and load conditions. Then, a dedicated bearing test is established to verify the reasonability and effectiveness of the vibration model. Furthermore, a comprehensive simulation analysis is conducted to study the vibration characteristics over an extended range of the internal radial clearance and external load. Results show that the dynamic force on each ball presents an impulse whose magnitudes increases whereas the pulse width reduces with clearance increases. Ball pass frequency of outer race (BPFO) is the dominant modulation component and the frequency is in accordance with the number of dynamic force impulses. Two indicators, i.e., root mean square (RMS) value and spectral centroid, are proposed to indicate clearance changes. In general, they show an uptrend with the increase in clearance, which is in line with the dynamic force increasing with clearance, especially the spectral centroid of the low frequency band. However, it should be noted that the RMS value and spectral centroid exhibit a fluctuating behavior due to nonlinear vibration responses. For the first time, this study shows the details of vibration characteristics with clearance variations and provides a foundation for monitoring the bearing conditions before any obvious local defects on raceways.

Towards Developing an Automated Faults Characterisation Framework for Rotating Machines. Part 1: Rotor-Related Faults

Rotating machines are pivotal to the achievement of core operational objectives within various industries. Recent drives for developing smart systems coupled with the significant advancements in computational technologies have immensely increased the complexity of this group of critical physical industrial assets (PIAs). Vibration-based techniques have contributed significantly towards understanding the failure modes of rotating machines and their associated components. However, the very large data requirements attributable to routine vibration-based fault diagnosis at multiple measurement locations has led to the quest for alternative approaches that possess the capability to reduce faults diagnosis downtime. Initiatives aimed at rationalising vibration-based condition monitoring data in order to just retain information that offer maximum variability includes the combination of coherent composite spectrum (CCS) and principal components analysis (PCA) for rotor-related faults diagnosis. While there is no doubt about the potentials of this approach, especially that it is independent of the number of measurement locations and foundation types, its over-reliance on manual classification made it prone to human subjectivity and lack of repeatability. The current study therefore aims to further enhance existing CCS capability in two facets—(1) exploration of the possibility of automating the process by testing its compatibility with various machine learning techniques (2) incorporating spectrum energy as a novel feature. It was observed that artificial neural networks (ANN) offered the most accurate and consistent classification outcomes under all considered scenarios, which demonstrates immense opportunity for automating the process. The paper describes computational approaches, signal processing parameters and experiments used for generating the analysed vibration data.