A review of physics-based models in prognostics: application to gears and bearings of rotating machinery

Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability. Covering aspects include sensors, signal processing, health assessment and decision-making. This article focuses on prognostics based on physics-based models. While the majority of the research in health condition monitoring focuses on data-driven techniques, physics-based techniques are particularly important if accuracy is a critical factor and testing is restricted. Moreover, the benefits of both approaches can be combined when data-driven and physics-based techniques are integrated. This article reviews the concept of physics-based models for prognostics. An overview of common failure modes of rotating machinery is provided along with the most relevant degradation mechanisms. The models available to represent these degradation mechanisms and their application for prognostics are discussed. Models that have not been applied to health condition monitoring, for example, wear due to metal–metal contact in hydrodynamic bearings, are also included due to its potential for health condition monitoring. The main contribution of this article is the identification of potential physics-based models for prognostics in rotating machinery

Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning

Components of rotating machines, such as shafts, bearings and gears are subject to performance degradation, which if left unattended could lead to failure or breakdown of the entire system. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes a combination of two supervised machine learning techniques; namely, the regression model and multilayer artificial neural network model, to predict the remaining useful life of rolling element bearings. Root mean square and Kurtosis were analyzed to define the bearing failure stages. The proposed methodology was validated through two case studies involving vibration measurements of an operational wind turbine gearbox and a split cylindrical roller bearing in a test rig

Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning

Deployment of large-scale wind turbines requires sophisticated operation and maintenance strategies to ensure the devices are safe, profitable and cost-effective. Prognostics aims to predict the remaining useful life (RUL) of physical systems based on condition measurements. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes to combine two supervised machine learning techniques, namely, regression model and multilayer artificial neural network model, to predict the RUL of an operational wind turbine gearbox using vibration measurements. Root Mean Square (RMS), Kurtosis (KU) and Energy Index (EI) were analysed to define the bearing failure stages. The proposed methodology was evaluated through a case study involving vibration measurements of a high-speed shaft bearing used in a wind turbine gearbox

Failure Prognosis of Wind Turbine Components

Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms

Remaining useful life (RUL) prediction of bearing by using regression model and principal component analysis (PCA) technique

A wind turbine works under variable load and environmental conditions because of which failure rate has been on the rise. Failure of a gearbox, an integral part of producing wind energy, contributes to 80 % of the total downtime for the wind turbine. For ensuring better utilization of the wind turbines, Fault prognosis and condition monitoring of bearings are of utmost importance as it helps to reduce the downtime by early detection of faults which further increases the power output. In this paper, vibration signals produced and machine learning approach to determine the Remaining Useful Life (RUL) for a degraded bearing is studied. The methodology includes statistical feature extraction analysis with regression models. Further the feature selection is done using Principal Component Analysis (PCA) technique which produces training and testing sets which acts as an input parameter for regression models such as Support Vector Regressor (SVR) and Random Forest (RF). Weibull Hazard Rate Function is used for calculating the RUL of the bearing. Results This study shows the potential application of regression model as an effective tool for degradation performance prediction of bearing

Review of prognostic problem in condition-based maintenance.

International audienceprognostic is nowadays recognized as a key feature in maintenance strategies as it should allow avoiding inopportune maintenance spending. Real prognostic systems are however scarce in industry. That can be explained from different aspects, on of them being the difficulty of choosing an efficient technology ; many approaches to support the prognostic process exist, whose applicability is highly dependent on industrial constraints. Thus, the general purpose of the paper is to explore the way of performing failure prognostics so that manager can act consequently. Diffent aspects of prognostic are discussed. The prognostic process is (re)defined and an overview of prognostic metrics is given. Following that, the "prognostic approaches" are described. The whole aims at giving an overview of the prognostic area, both from the academic and industrial points of views

A Combined Numerical and Experimental Approach for Rolling Bearing Modelling and Prognostics

Rolling-element bearings are widely employed components which cover a major role in the NVH behaviour of the mechanical systems in which they are inserted. Therefore, it is crucial to thoroughly understand their fundamental properties and accurately quantify their most relevant parameters. Moreover, their inevitable failure due to contact fatigue leads to the necessity of correctly describing their dynamic behaviour. In fact, they permit to develop diagnostic and prognostic schemes, which are heavily requested in the nowadays industrial scenario due to their increasingly important role in the development of efficient maintenance strategies. As a result, throughout the years several techniques have been developed by researchers to address different challenges related to the modelling of these components. Within this context, this thesis aims at improving the available methods and at proposing novel approaches to tackle the modelling of rolling-element bearings both in case of static and dynamic simulations. In particular, the dissertation is divided in three major topics related to this field, i.e. the estimation of bearing radial stiffness trough the finite-element method, the lumped-parameter modelling of defective bearings and the development of physics-based prognostic models. The first part of the thesis deals with the finite-element simulations of rolling-element bearings. In particular, the investigation aims at providing an efficient procedure for the generation of load-dependent meshes. The method is developed with the primary objective of determining the radial stiffness of the examined components. In this regard, the main contribution to the subject is the definition of mesh element dimensions on the basis of analytical formulae and in the proposed methodology for the estimation of bearing stiffness. Then, the second part describes a multi-objective optimization technique for the estimation of unknown parameters in lumped parameter models of defective bearings. In fact, it was observed that several parameters which are commonly inserted in these models are hardly measurable or rather denoted by a high degree of uncertainty. On this basis, an optimization procedure aimed at minimizing the difference between experimental and numerical results is proposed. The novelty of the technique lies in the approach developed to tackle the problem and its peculiar implementation in the context of bearing lumped-parameter models. Lastly, the final part of the dissertation is devoted to the development of physics-based prognostic models. Specifically, two models are detailed, both based on a novel degradation-related parameter, i.e. the Equivalent Damaged Volume (EDV). An algorithm capable of extracting this quantity from experimental data is detailed. Then, EDV values are used as input parameters for two prognostic models. The first one aims at predicting the bearing vibration under different operative conditions with respect to a given reference deterioration history. On the other hand, the objective of the second model is to predict the time until a certain threshold on the equivalent damaged volume is crossed, regardless of the applied load and the shaft rotation speed. Therefore, the original aspect of this latter part is the development of prognostic models based on a novel indicator specifically introduced in this work. Results obtained from all proposed models are validated through analytical methods retrieved from the literature and by comparison with data acquired on a dedicated test bench. To this end, a test rig which was set-up at the Engineering Department of the University of Ferrara was employed to perform two type of tests, i.e. stationary tests on bearings with artificial defects and run-to-failure tests on initially healthy bearings. The characteristics of acceleration signals acquired during both tests are extensively discussed.I cuscinetti a rotolamento sono componenti meccanici che influenzano in maniera considerevole il comportamento dinamico dei sistemi all’interno dei quali sono montati. Pertanto, è di fondamentale importanza possedere strumenti atti alla stima dei loro parametri più rilevanti e avere a disposizione modelli dedicati allo studio delle loro caratteristiche dinamiche. Questo aspetto è di estrema importanza soprattutto nell’ottica dello sviluppo di schemi di diagnostica e prognostica, i quali sono sempre più richiesti all’interno dello scenario industriale odierno. In questo contesto, questa tesi si propone di migliorare le tecniche numeriche già esistenti e di fornire nuovi approcci per la modellazione dei cuscinetti a rotolamento sia nel caso di problemi statici che dinamici. Nello specifico, il lavoro tratta in maniera dettagliata tre diversi argomenti relativi a questo tema, ossia la stima della rigidezza radiale tramite il metodo agli elementi finiti, la modellazione a parametri concentrati di cuscinetti con difetti e lo sviluppo di modelli di prognostica physics-based. La prima parte della tesi concerne la simulazione di cuscinetti a rotolamento tramite il metodo ad elementi finiti. In particolare, lo studio si propone di fornire una procedura per la generazione di griglie le cui dimensioni dipendano dal carico applicato. Il metodo è sviluppato con l’obbiettivo di stimare in maniera computazionalmente efficace la rigidezza radiale del componente in esame. Pertanto, il contributo principale sul tema dato da questa prima parte riguarda il metodo analitico che permette di definire a priori le dimensioni degli elementi che costituiscono la mesh e la metodologia utilizzata per la stima della rigidezza. La seconda parte descrive una procedura di ottimizzazione multi obbiettivo per la stima dei parametri incogniti all’interno dei modelli a parametri concentrati di cuscinetti con difetti. Questa esigenza nasce dall’osservazione che numerosi parametri tipicamente inseriti in questa tipologia di modelli sono difficilmente misurabili oppure caratterizzati da un alto grado di incertezza. Perciò, nella seconda parte viene introdotta una tecnica innovativa che consente di stimare i parametri di modello che minimizzano la differenza fra risultati numerici e sperimentali in diverse condizioni di funzionamento. Infine, l’ultima parte è dedicata allo sviluppo di modelli di prognostica physics-based. A tal riguardo, vengono dettagliati due modelli basati su un nuovo indicatore di degrado del cuscinetto, denominato Equivalent Damaged Volume (EDV). Questo indicatore viene calcolato durante il funzionamento del cuscinetto tramite un algoritmo dedicato. I valori così ottenuti sono poi utilizzati come dati di input per i due modelli prognostici. Il primo mira a predire la vibrazione del cuscinetto in condizioni operative diverse rispetto ad una storia di degrado di riferimento. Diversamente, il secondo modello permette di prevedere il tempo rimanente prima del superamento di una soglia critica di volume equivalente danneggiato, indipendentemente da carico applicato e velocità di rotazione. Dunque, l’aspetto originale di quest’ultima parte ricade nello sviluppo di tecniche prognostiche basate su un nuovo indicatore introdotto ad-hoc in questo lavoro. I risultati ottenuti da tutti i modelli proposti sono validati grazie a metodi analitici di letteratura e a dati acquisiti in laboratorio per mezzo di un banco prova installato presso il Dipartimento di Ingegneria dell’Università di Ferrara. Il banco prova è stato utilizzato per realizzare due tipologie di prove, ossia test stazionari su cuscinetti che presentano difetti artificiali e prove di tipo run-to-failure su cuscinetti inizialmente sani. Le caratteristiche dei segnali di accelerazione acquisiti in entrambe le prove sono discussi in maniera esaustiva

Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review

There is an ever-increasing need to optimise bearing lifetime and maintenance cost through detecting faults at earlier stages. This can be achieved through improving diagnosis and prognosis of bearing faults to better determine bearing remaining useful life (RUL). Until now there has been limited research into the prognosis of bearing life in rotating machines. Towards the development of improved approaches to prognosis of bearing faults a review of fault diagnosis and health management systems research is presented. Traditional time and frequency domain extraction techniques together with machine learning algorithms, both traditional and deep learning, are considered as novel approaches for the development of new prognosis techniques. Different approaches make use of the advantages of each technique while overcoming the disadvantages towards the development of intelligent systems to determine the RUL of bearings. The review shows that while there are numerous approaches to diagnosis and prognosis, they are suitable for certain cases or are domain specific and cannot be generalised