A novel approach to feature extraction from gear condition monitoring signals

Extracting features from condition monitoring signals of rotating machines is challenging, primarily due to the many potential sources of noise and interference that can corrupt the signals. Additionally, these signals can vary significantly depending on the operating conditions of the machine, making it difficult to develop consistent diagnostic methods. However, an effective feature extraction remains crucial for proper maintenance scheduling and for preventing unexpected machine breakdowns, which can result in costly repairs and operational disruptions. Gear faults, if left unchecked, can be extremely dangerous, particularly in critical applications such as wind turbines, where a failure can lead to significant operational losses. Vibration signals from machinery are often complex, consisting of many different components, making it challenging to isolate the fault-related features from the noise. In this paper, an Envelope-Derivative Operator (EDO) is proposed to overcome these challenges by achieving a balance between the signal amplitude and frequency. The EDO measures the rate of change of the envelope, which is useful in detecting shifts in both the amplitude and the frequency of the signal. To identify the impulsive-like behaviour often associated with gear faults, the EDO analyses the energy content of the signal in both the time and frequency domains. By isolating the fault-related components and filtering out irrelevant noise, the proposed operator shows high efficiency in diagnosing gear faults under various experimental conditions, with no data fitting required and minimal computational resources. In addition, the non-destructive nature of this method can significantly reduce downtime and associated maintenance costs, making it an ideal tool for real-time fault detection in rotating machinery.