Monitoring Based on Time-Frequency Tracking of Estimated Harmonic Series and Modulation Sidebands

Gerber, Timothée; Martin, Nadine; Mailhes, Corinne

doi:10.1007/978-3-319-20463-5_7

Cited by 7 publications

(11 citation statements)

References 7 publications

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“…One records the signals over a wind turbine simulation test-bench [3] . The other, named Arfons, is composed of real-world wind turbine signals.…”

Section: Results Of Characterisation Of Real-world Signalsmentioning

confidence: 99%

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AStrion data validation of non-stationary wind turbine signals

Song¹,

Zy²,

Bellemain³

et al. 2015

Insight

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AStrion is an automatic spectrum analyser software, which proposes a new generic and data-driven method without any a priori information on the measured signals. In order to compute some general characteristics and derive the properties of the signal, the first step in this approach is to give some insight into the nature of the signal. This pre-analysis, the so-called data validation, contains a number of tests to reveal some of the properties and characteristics of the data, such as the acquisition validity (the absence of saturation and a posteriori in respect of the sampling theorem), the stationarity (or non-stationarity), the periodicity and the signal-to-noise ratio. Based on these characteristics, the proposed method defines indicators and alarm trigger thresholds and also categorises the signal into three classes, which helps to guide the following spectral analysis. The present paper introduces the four tests of this pre-analysis and the signal categorisation rules. Finally, the proposed approach is validated on a set of wind turbine vibration measurements to demonstrate its applicability for a long-term and continuous monitoring of real-world signals.

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“…One records the signals over a wind turbine simulation test-bench [3] . The other, named Arfons, is composed of real-world wind turbine signals.…”

Section: Results Of Characterisation Of Real-world Signalsmentioning

confidence: 99%

“…It is capable of deducing reliable mechanical fault indicators thanks to many spectrum analysis modules, which are described in previous publications [3,4] . In this paper, we focus on the angular resampling and data validation modules in AStrion.…”

Section: Introductionmentioning

confidence: 99%

AStrion data validation of non-stationary wind turbine signals

Song¹,

Zy²,

Bellemain³

et al. 2015

Insight

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“…This section starts with a brief introduction of the wind turbine test rig in VI-A, and ends up with the obtained results, already presented in [40]. The first main bearing has been dismantled after the first signs of the fault, while the second main bearing has been fully damaged up to totally stop the normal operation of the rig.…”

Section: Methodsmentioning

confidence: 99%

Time-Frequency Tracking of Spectral Structures Estimated by a Data-Driven Method

Gerber

Martin

Mailhes

2015

IEEE Trans. Ind. Electron.

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Abstract-The installation of a condition monitoring system aims to reduce the operating costs of the monitored system by applying a predictive maintenance strategy. However, a systemdriven configuration of the condition monitoring system requires the knowledge of the system kinematics and could induce lots a false alarms because of predefined thresholds. The purpose of this paper is to propose a complete data-driven method to automatically generate system health indicators without any a priori on the monitored system or the acquired signals. This method is composed of two steps. First, every acquired signal is analysed: the spectral peaks are detected and then grouped in more complex structure as harmonic series or modulation sidebands. Then, a time-frequency tracking operation is applied on all available signals: the spectral peaks and the spectral structures are tracked over time and grouped in trajectories, which will be used to generate the system health indicators. The proposed method is tested on real-world signals coming from a wind turbine test rig. The detection of a harmonic series and a modulation sideband reports the birth of a fault on the main bearing inner ring. The evolution of the fault severity is characterised by three automatically generated health indicators and is confirmed by experts.

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“…In the AStrion methods, some prior works (5,6,8) mainly focused on the analysis of a single signal, while some others focus on the time-tracking of the features (7) . In this paper, we are going to summarize the methodologies of the entire AStrion architecture, including both types of methods.…”

Section: Introductionmentioning

confidence: 99%

AStrion strategy: from acquisition to diagnosis. Application to wind turbine monitoring

Zy¹,

Gerber²,

Firla³

et al. 2016

Int J Cond Monitor

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This paper proposes an automatic procedure for condition monitoring. It represents a valuable tool for maintenance of expensive and spread systems such as wind turbine farms. Thanks to data-driven signal processing algorithms, the proposed solution is fully automatic for the user. The paper briefly describes all the steps of the processing, from pre-processing of acquired signal to interpretation of generated results. It starts with an angular resampling method with speed measurement correction. Then comes a data validation step, in both time/angular and frequency/order domains. After these preprocessings, the spectral components of the analyzed signal are identified and classified in several classes from sine wave to narrow band components. This spectral peak detection and classification allows extracting the harmonic and side-band series which may be part of the signal spectral content. Moreover, the detected spectral patterns are associated with the characteristic frequencies of the investigated system. Based on the detected side-band series, the full-band demodulation is performed. At each step, the diagnosis features are computed and dynamically tracked signal by signal. Finally, system health indicators are proposed to conclude about the condition of the investigated system. All mentioned steps create a self-sufficient tool for robust diagnosis of mechanical faults. The paper presents the performance of the proposed method on real-world signals from a wind turbine drive train.

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Monitoring Based on Time-Frequency Tracking of Estimated Harmonic Series and Modulation Sidebands

Cited by 7 publications

References 7 publications

AStrion data validation of non-stationary wind turbine signals

AStrion data validation of non-stationary wind turbine signals

Time-Frequency Tracking of Spectral Structures Estimated by a Data-Driven Method

AStrion strategy: from acquisition to diagnosis. Application to wind turbine monitoring

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