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Demodulation methods are one of the most widely used tools for bearing diagnostics, and in particular, are methods based on the Squared Envelope Spectrum (SES) with band pass filtering. One of the main challenges of these methods is the detection of a suitable band for the demodulation of the signal under varying speed conditions. Angular resampling methods may

Rolling element bearing fault diagnostics has been a topic of intensive research.As the rolling element bearings are a critical component of rotating machinery, failure of these components results in the breakdown of the mechanical structure in which they operate. The correct detection of incipient faults on the bearings can reduce the production cost by allowing maintenance engineers to schedule a replacement at the most convenient time. Envelope analysis is a widespread method in bearing diagnostics, where the frequency band that carries the fault information is detected and amplitude demodulated to obtain the envelope, whose spectrum reveals the repetition frequencies of the particular fault. It is often used along with Fast Kurtogram, which selects this band based on the kurtosis level. However, this methodology shows difficulty in selecting the correct band on complex signals. Signals contaminated with Electro-Magnetic Interference (EMI) are one such example where Envelope Analysis based on the Fast Kurtogram (FK) often fails to diagnose bearing faults. EMI is often present in mechanisms whose motor is controlled by a Variable-Frequency Drive (VFD), and shows a behaviour similar to that of bearing faults, and thus increases the

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Wind industry experiences a tremendous growth during the last few decades. As of the end of 2016, the worldwide total installed electricity generation capacity from wind power amounted to 486,790 MW, presenting an increase of 12.5% compared to the previous year. Nowadays wind turbine manufacturers tend to adopt new business models proposing total health monitoring services and solutions, using regular inspections or even embedding sensors and health monitoring systems within each unit. Regularly planned or permanent monitoring ensures a continuous power generation and reduces maintenance costs, prompting specific actions when necessary. The core of wind turbine drivetrain is usually a complicated planetary gearbox. One of the main gearbox components which are commonly responsible for the machinery breakdowns are rolling element bearings. The failure signs of an early bearing damage are usually weak compared to other sources of excitation (e.g., gears). Focusing toward the accurate and early bearing fault detection, a plethora of signal processing methods have been proposed including spectral analysis, synchronous averaging and enveloping. Envelope analysis is based on the extraction of the envelope of the signal, after filtering around a frequency band excited by impacts due to the bearing faults. Kurtogram has been proposed and widely used as an automatic methodology for the selection of the filtering band, being on the other hand sensible in outliers. Recently, an emerging interest has been focused on modeling rotating machinery signals as cyclostationary, which is a particular class of nonstationary stochastic processes. Cyclic spectral correlation and cyclic spectral coherence (CSC) have been presented as powerful tools for condition monitoring of rolling element bearings, exploiting their cyclostationary behavior. In this work, a new diagnostic tool is introduced based on the integration of the cyclic spectral coherence (CSC) along a frequency band that contains the diagnostic information. A special procedure is proposed in order to automatically select the filtering band, maximizing the corresponding fault indicators. The effectiveness of the methodology is validated using the National Renewable Energy Laboratory (NREL) wind turbine gearbox vibration condition monitoring benchmarking dataset which includes various faults with different levels of diagnostic complexity.

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Digitally enhanced services for wind power could reduce operations and maintenance costs as well as the levelized cost of energy. Therefore, there is a continuous need for advanced monitoring techniques, which can exploit the opportunities of internet of things and big data analytics, revolutionizing the future of the energy sector. The heart of wind turbines is a rather complex epicyclic gearbox. Among others, extremely critical gearbox components, which are often responsible for machinery stops, are the rolling element bearings. The vibration signatures of bearings are rather weak compared to other components, such as gears, and as a result, an extended number of signal processing techniques and tools have been proposed during the last decades, focusing toward accurate, early, and on time bearing fault detection with limited false alarms and missed detections. Envelope analysis is one of the most important methodologies, where an envelope of the vibration signal is estimated usually after filtering around a frequency band excited by impacts due to the bearing faults. Different tools, such as Kurtogram, have been proposed in order to accurately select the optimum filter parameters (center frequency and bandwidth). Cyclic spectral correlation (CSC) and cyclic spectral coherence (CSCoh), based on cyclostationary analysis, have been proved as very powerful tools for condition monitoring. The monitoring techniques seem to have reached a mature level in case a machinery operates under steady speed and load. On the other hand, in case the operating conditions change, it is still unclear whether the change of the monitoring indicators is due to the change of the health of the machinery or due to the change of the operating parameters. Recently, the authors have proposed a new tool called improved envelope spectrum via feature optimization-gram (IESFOgram), which is based on CSCoh and can automatically select the filtering band. Furthermore, the CSCoh is integrated along the selected frequency band leading to an improved envelope spectrum (IES). In this paper, the performance of the tool is evaluated and further extended on cases operating under different speeds and different loads. The effectiveness of the methodology is tested and validated on the National Renewable Energy Laboratory (NREL) wind turbine gearbox vibration condition monitoring benchmarking dataset, which includes various faults with different levels of diagnostic complexity as well as various speed and load operating conditions.