Observer-biased bearing condition monitoring: from fault detection to multi-fault classification

Bearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems. (C) 2016 Elsevier Ltd. All rights reserved.Grant number: 145602

Fuzzy determination of informative frequency band for bearing fault detection

Detecting early faults in rolling element bearings is a crucial measure for the health maintenance of rotating machinery. As faulty features of bearings are usually demodulated into a high-frequency band, determining the informative frequency band (IFB) from the vibratory signal is a challenging task for weak fault detection. Existing approaches for IFB determination often divide the frequency spectrum of the signal into even partitions, one of which is regarded as the IFB by an individual selector. This work proposes a fuzzy technique to select the IFB with improvements in two aspects. On the one hand, an IFB-specific fuzzy clustering method is developed to segment the frequency spectrum into meaningful sub-bands. Considering the shortcomings of the individual selectors, on the other hand, three commonly-used selectors are combined using a fuzzy comprehensive evaluation method to guide the clustering. Among all the meaningful sub-bands, the one with the minimum comprehensive cost is determined as the IFB. The bearing faults, if any, can be detected from the demodulated envelope spectrum of the IFB. The proposed fuzzy technique was evaluated using both simulated and experimental data, and then compared with the state-of-the-art peer method. The results indicate that the proposed fuzzy technique is capable of generating a better IFB, and is suitable for detecting bearing faults

Componential coding in the condition monitoring of electrical machines Part 2: application to a conventional machine and a novel machine

This paper (Part 2) presents the practical application of componential coding, the principles of which were described in the accompanying Part 1 paper. Four major issues are addressed, including optimization of the neural network, assessment of the anomaly detection results, development of diagnostic approaches (based on the reconstruction error) and also benchmarking of componential coding with other techniques (including waveform measures, Fourier-based signal reconstruction and principal component analysis). This is achieved by applying componential coding to the data monitored from both a conventional induction motor and from a novel transverse flux motor. The results reveal that machine condition monitoring using componential coding is not only capable of detecting and then diagnosing anomalies but it also outperforms other conventional techniques in that it is able to separate very small and localized anomalies

Vibration-based methods for structural and machinery fault diagnosis based on nonlinear dynamics tools

This study explains and demonstrates the utilisation of different nonlinear-dynamics-based procedures for the purposes of structural health monitoring as well as for monitoring of robot joints

PHM survey: implementation of signal processing methods for monitoring bearings and gearboxes

The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes

Trends in condition monitoring of pitch bearings

The value of wind power generation for energy sustainability in the future is undeniable. Since operation and maintenance activities take a sizeable portion of the cost associated with offshore wind turbines operation, strategies are needed to decrease this cost. One strategy, condition monitoring (CM) of wind turbines, allows the extension of useful life for several parts, which has generated great interest in the industry. One critical part are the pitch bearings, by virtue of the time and logistics involved in their maintenance tasks. As the complex working conditions of pitch bearings entail the need for diverse and innovative monitoring techniques, the classical bearing analysis techniques are notsuitable. This paper provides a literature review of several condition monitoring techniques, organized as follows: first, arranged according to the nature of the signal such as vibration, acoustic emission and others; second, arranged by relevant authors in compliance with signal nature. While little research has been found, an outline is significant for further contributions to the literature.Postprint (published version

Incipient defect identification in rolling bearings using adaptive lifting scheme packet

Defects on the surface of rolling bearing elements are some of the most frequent causes of malfunctions and breakages of rotating machines. Defect detection in rolling bearings via techniques that examine changes in measured signal is a very important topic of research due to increasing demands for quality and reliability. In this paper, incipient defect identification method based on adaptive lifting scheme packet is proposed. Adaptive lifting scheme packet operators which adapt to the signal characteristic are constructed. The shock pulse value in defect sensitive frequency band is used as the defect indicator to identify the defect location and severity of rolling bearing. The proposed defect identification method is applied to analyze the experimental signal from rolling bearing with incipient inner raceway defect. The result confirms that the proposed method is accurate and robust in rolling bearing incipient defect identification

Diagnostic monitoring of drivetrain in a 5 MW spar-type floating wind turbine using Hilbert spectral analysis

The objective of this paper is to investigate the frequency-based fault detection of a 5MW spar-type floating wind turbine (WT) gearbox using measurements of the global responses. It is extremely costly to seed managed defects in a real WT gearbox to investigate different fault detection and condition monitoring approaches; using analytical tools, therefore, is one of the promising approaches in this regard. In this study, forces and moments on the main shaft are obtained from the global response analysis using an aero-hydro-servo-elastic code, SIMO-RIFLEX-AeroDyn. Then, they are utilized as inputs to a high-fidelity gearbox model developed using a multi-body simulation software (SIMPACK). The main shaft bearing is one of the critical components since it protects gearbox from axial and radial loads. Six different fault cases with different severity in this bearing are investigated using power spectral density (PSD) of relative axial acceleration of the bearing and nacelle. It is shown that in severe degradation of this bearing the first stage dynamic of the gearbox is dominant in the main shaft vibration signal. Inside the gearbox, the bearings on the high speed side are those often with high probability of failure, thus, one fault case in IMS-B bearing was also considered. Based on the earlier studies, the angular velocity error function is considered as residual for this fault. The Hilbert transform is used to determine the envelope of this residual. Information on the amplitude of this residual properly indicates damage in this bearing