Cédric Peeters

Vrije Universiteit Brussel | VUB · Applied Mechanics (MECH)

Wind energy is considered a sustainable renewable energy source; however, it faces the challenge of significant operating and maintenance costs. The research proposes a hybrid fault detection method to combine the physical domain knowledge with the machine learning models to provide an overview of the health of wind turbine drivetrain components. S...

This study investigates the impact of operating and environmental conditions on the vibration induced on an offshore wind turbine drivetrain. Furthermore, it explores the role of the dynamic response of the global wind turbine structure to vibration on the drivetrain. A prolonged experimental campaign dedicated to condition monitoring the drivetrai...

This study details the development of a fully automated pipeline for the condition monitoring of wind turbine drive trains. Vibration data is collected using hardware designed and manufactured in-house and used directly to monitor the condition of the drive trains. The complex nature of wind turbine vibration signals, due to the large number of com...

The maintenance of wind turbines is essential to reduce wind energy levelized costs. Earlier detection of potential faults in the rotating subcomponents, such as the drivetrain, helps to plan maintenance actions. Several vibration processing methods, e.g., short-time Fourier analysis, are available in the literature to detect faults, however, they...

Research nowadays takes advantage of the cyclostationary properties in the vibration waveforms of rotating machines for fault detection. For example, cyclic spectral coherence maps (CSCM) break down vibration signals into cyclic and carrier frequencies. However, the large size of the CSCMs and the enormous amount of data makes it challenging to ide...

Vibration analysis is a prevalent technique in the predictive maintenance of wind turbines. It is an effective method for early fault detection and enables the creation of cost-effective maintenance strategies. Commonly used vibration analysis methods in the literature rely on signal processing techniques such as time and frequency domain approache...

Drivetrain failures result in the largest downtime per failure among the different turbine components. To minimize O&M costs, it is therefore essential to be able to anticipate failure events sufficiently in advance such that scheduled maintenance can take place. Moreover, a root cause for the failure should be identified, allowing to incorporate t...

The development of a reliable and automated condition monitoring methodology for the detection of mechanical failures in rotating machinery has garnered much interest in recent years. Thanks to the rise in popularity of machine learning techniques, the number of purely data-driven approaches that try to tackle the issue of vibration-based condition...

This study attempts to improve the performance of Generalized Likelihood Ratio Test-based indicators via blind filtering the of vibration signals. The key point is the optimization of the filter coefficients to maximize the indicator of interest. The filter coefficients are optimized through Rayleigh quotient iteration. The proposed method's perfor...

Vibration signals measured on rotating machinery typically exhibit cyclostationarity due to the inherent nature of real-world rotating vibration sources. Hence, the development of signal processing tools devoted to investigating or exploiting this cyclostationarity for condition monitoring purposes of gears and bearings has seen a significant incre...

Studies in condition monitoring literature often aim to detect rolling element bearing faults because they have one of the biggest shares among defects in turbo machinery. Accordingly, several prognosis and diagnosis methods have been devised to identify fault signatures from vibration signals. A recently proposed method to capture the rolling elem...

Deep learning methods have become popular among researchers in the field of fault detection. However, their performance depends on the availability of big datasets. To overcome this problem researchers started applying transfer learning to achieve good performance from small available datasets, by leveraging multiple prediction models over similar...

The short-time Fourier transform (STFT) is a staple analysis tool for vibration signal processing due to it being a robust, non-parametric, and computationally efficient technique to analyze non-stationary signals. However, despite these beneficial properties, the STFT suffers from high variance, high sidelobes, and a low resolution. This paper inv...

Phase demodulation is arguably the most used technique for the estimation of the instantaneous angular speed from vibration signals measured on rotating machinery. Although phase demodulation offers a straightforward approach to determine accurately the rotation speed of a particular shaft in a rotating machine, it does have strict limitations that...

This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the system that converts kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. Offshore development and dig...

Operational Modal Analysis allows to assess the modal model of rotating machinery. However, it is needed to pre-process the measured vibration data such that the influence of the harmonic content is suppressed. In this paper, such pre-processing techniques are discussed. The main focus is on evaluating the effects of amplitude modulation, originati...

Detection of bearing faults is a challenging task since the impulsive pattern of bearing faults often fades into the noise. Moreover, tracking the health conditions of rotating machinery generally requires the characteristic frequencies of the components of interest, which can be a cumbersome constraint for large industrial applications because of...

This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the energy conversion systems transferring the kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning, and recycling. Offsh...

Drivetrains play an essential role in guaranteeing the reliability of wind turbines. A challenge in their design is the wide range of loading conditions they are exposed to. Several design load cases are required to be simulated in order to ensure that the ultimate loads are not exceeded, and to validate that the fatigue lifetime matches the design...

This paper investigates the efficacy and reliability of three different state-of-the-art rotation speed estimation techniques on a very large set of experimental vibration data originating from thirty offshore wind turbine gearboxes. The three methods include the multi-order probabilistic approach, the phase demodulation method based on the frequen...

A smartphone is a low-cost pocket wireless multichannel multiphysical data acquisition system: the use of such a device for noise and vibration analysis is a challenging task. To what extent is it possible to carry out relevant analysis from it? The Survishno conference, held in Lyon in July 2019, proposed a contest to participants based on this su...

Due to changes in generator topology, wind turbines are operating in much wider speed ranges and thus at more varying operating conditions. This has a positive influence on the energy production, but results in much wider gear mesh excitation ranges that can lead to tonalities. As such, a high-quality characterization of the modal model of the gear...

Wind energy is one of the largest sources of renewable energy in the world. To further reduce the operations and maintenance (O&M) costs of wind farms, it is essential to be able to accurately pinpoint the root causes of different failure modes of interest. An example of such a failure mode that is not yet fully understood is white etching cracks (...

This paper proposes a signal processing approach for wind turbine gearbox vibration signals based on employing multiple analysis pipelines. These so-called pipelines consist of combinations of various advanced signal processing methods that have been proven to be effective in literature when applied to wind turbine vibration signals. The performanc...

This paper investigates a novel perspective on blind filtering of vibration signals with the purpose of fault detection in rotating machinery. Instead of maximizing a property of the time-domain signal such as kurtosis to find an optimal filter, the sparsity of its envelope spectrum is maximized. The underlying assumption for this approach is that...

One of the advantages of the current industrial digitalization trend, the so-called Industry 4.0, is that machines are becoming increasingly sensorized and connected to the internet. This is similar in the wind industry. Detailed measurements from hundreds of sensors embedded in the wind turbine are being sent continuously to cloud computing data-c...

This paper illustrates an integrated monitoring approach for wind turbines exploiting this Industry 4.0 context. Our combined edge-cloud processing approach is documented. We show edge processing of vibration data captured on a wind turbine gearbox to extract diagnostic features. Focus is on statistical indicators. Real-life signals collected on an...

This Ph.D. dissertation targets innovative methods for vibration-based condition monitoring of rotating machinery. Substantial benefits can be achieved from an economical and a safety point of view using condition monitoring. One of the most popular methods to gather information about the state of machine parts is through the analysis of machine vi...

Noise, vibration and harshness (NVH) problems are critical issues to be tackled for wind turbine drivetrains. Tracking the behavior of modal parameters of the machines’ fundamental modes during operation it is of high interest to validate complex simulation models. A powerful approach for this purpose is represented by operational modal analysis (O...

The central idea behind this paper is to propose a means to filter out vibration signals of interest from a fault detection perspective without actually having knowledge about the kinematics of the machine. In other words, this paper investigates blind filters that do not require a-priori knowledge about the fault frequencies, e.g. of a bearing or...

Instantaneous speed estimation has become a key part of many condition monitoring procedures for rotating machinery. The ability to track the rotational speed of a system is a critical requirement for the majority of vibration-based condition monitoring methods. Information about the speed enables compensating for potential speed variations that wo...

Detailed knowledge about the modal model is essential to enhance the NVH behavior of (rotating) machines. To have more realistic insight in the modal behavior of the machines, observation of modal parameters must be extended to a significant amount of time, in which all the significant operating conditions of the turbine can be investigated, togeth...

This work describes an automated condition monitoring framework to process and analyze vibration data measured on wind turbine gearboxes. The current state-of-the-art in signal processing often leads to a large quantity in health indicators thanks to the multiple potential pre-processing steps. Such large quantities of indicators become unfeasible...

This work describes an autonomous condition monitoring framework to process and analyze data measured on wind turbine gearboxes. Industry 4.0 and the Industrial Internet of Things open the door for much more elaborate measurement and data analysis campaigns thanks to the reduction in cost of sensors and of processing power. This increase in data ac...

Perhaps the most used approach for vibration-based instantaneous angular speed estimation is based on phase demodulation of a shaft-speed related harmonic. While this approach can deliver very accurate IAS estimations in cases where such a single, constantly present, and dominant harmonic is existing, there are ample cases where this approach has i...

Today, we are at the beginning of Industry 4.0. Machines are becoming increasingly sensorized and connected to the internet. Streaming data will thus be sent continuously to cloud computing data-centers. Condition monitoring techniques can leverage these huge volumes of available data to increase detection potential and insights in system behavior...

Effectively monitoring the health of a wind turbine gearbox is a complex and often multidisciplinary endeavor. Recently, condition monitoring practices increasingly combine knowledge from fields like signal processing, machine learning, and mechanics. Such a diverse approach becomes necessary when dealing with the vast amount of data that is genera...

Accurate speed estimation is essential for dynamic analysis of rotating machinery; particularly for condition monitoring and operational modal analysis of applications with significant load and speed variation. The wind turbine is such an application. However, a sufficiently accurate tachometer, to keep track of the instantaneous speed, is not alwa...

The detection and diagnosis of incipient rolling element bearing faults is not an undemanding task and signal analysis of vibration measurements therefore often incorporates the use of various complex processing techniques. One of the key steps in the processing procedure is the proper separation of the bearing signal from other influencing sources...

To solve the problem of non-stationary speed conditions in rotating machinery, order tracking has become a popular practice to synchronize the Fourier analysis to the shaft rotation. Being able to estimate the instantaneous angular speed (IAS) accurately, enables analysts to identify machine events linked to the shaft rotation much easier. The IAS...

Most processing tools based on frequency analysis of vibration signals are only applicable for stationary speed regimes. Speed variation causes the spectral content to smear, which encumbers most conventional fault detection techniques. To solve the problem of non-stationary speed conditions, the instantaneous angular speed (IAS) is estimated. Wind...

Particularly offshore there is a trend to cluster wind turbines in large wind farms, and in the near future to operate such a farm as an integrated power production plant. Predictability of individual turbine behavior across the entire fleet is key in such a strategy. Failure of turbine subcomponents should be detected well in advance to allow earl...

Critical mechanical faults in wind turbine systems lead to considerable downtime and repair costs. Improving the detection and diagnosis of such faults thus brings about significant cost reductions for operations and maintenance (O&M) and electricity production. One of the most common defects in drivetrains are rolling element bearing faults. Detec...

A key step towards proper detection and diagnosis of bearing faults through vibration analysis is the separation of bearing fault signal components from other masking deterministic components. Bearing fault signals can generally be classified as (quasi-)cyclostationary which makes the separation possible from deterministic content, e.g. from gears...

Real world vibration measurements often contain a lot of signal components originating from different machine elements. Detecting and recognizing the signal of a single element is thus a complicated endeavor because of the masking presence of other element's signals in the measured data. Of particular interest to the industry is the early detection...

Rolling element bearing faults are one of the most common defects in rotating machinery. The detection of these faults has lately attracted an increasing amount of attention in the industry. Detecting the faults in their incipient phase can prevent a more catastrophic breakdown of a machine and can save a company time and money. An often occurring...