Condition monitoring of machines working under non-stationary operations is one of the most challenging problems in maintenance. A wind turbine is an example of such class of machines. One of effective approaches may be to identify operating conditions and investigate their influence on used diagnostic features. Commonly used methods based on measurement of electric current, rotational speed, power and other process variables require additional equipment (sensors, acquisition cards) and software. It is proposed to use advanced signal processing techniques for instantaneous shaft speed recovery from a vibration signal. It may be used instead of extra channels or in parallel as signal verification.

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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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This paper investigates the circularity of Short Time Fourier Transform (STFT) coefficients noise only, and proposes a modified STFT such that all coefficients coming from white Gaussian noise are circular. In order to use the spectral kurtosis (SK) as a Gaussianity test to check if signal points are present in a set of STFT points, we consider the SK of complex circular random variables, and its link with the kurtosis of the real and imaginary parts. We show that the variance of the SK is smaller than the variance of the kurtosis estimated from both real and imaginary parts. The effect of the noncircularity of Gaussian variables upon the spectral kurtosis of STFT coefficients is studied, as well as the effect of signal presence. Finally, a time-frequency segmentation algorithm based on successive iterations of noise variance estimation and timefrequency coefficients detection is proposed. The iterations are stopped when the spectral kurtosis on non-detected points reaches zero. Examples of segmented time-frequency space are presented on a dolphin whistle and on a simulated signal in non-white and non-stationary Gaussian noise.

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Carrying out information about the microstructure and stress behaviour of ferromagnetic steels, Magnetic Barkhausen Noise (MBN) has been used as a basis for effective Non Destructive Testing methods, opening new areas in industrial applications. One of the factors that determines the quality and reliability of the MBN analysis is the way information is extracted from the signal. Commonly, simple scalar parameters are used to characterize the information content, such as amplitude maxima and signal root mean square. This paper presents a new approach based on the timefrequency analysis. The experimental test case relates the use of MBN signals to characterize hardness gradients in a AISI4140 steel. To that purpose are assessed different time-frequency and time scale representations such as the spectrogram, the Wigner-Ville distribution, the Capongram, the ARgram obtained from an AutoRegressive model, the scalogram, and the Mellingram obtained from a Mellin transform. It is shown that, due to non-stationary characteristics of the MBN, time-2 frequency representations can provide a rich and new panorama of these signals. Extraction techniques of some time-frequency parameters are used to allow a diagnostic process. Comparison with results obtained by the classical method highlights the improvement on the diagnosis provided by the method proposed.