Informative frequency band selection for performing envelope analysis under fluctuating operating conditions in the presence of strong noise and deterministic components

Abstract

Condition-based maintenance is an important aspect in various industries to ensure reliable operation of machinery. To successfully execute maintenance responsibilities, it is required to know which components are healthy and which are in a damaged state. Thus, the need for effective incipient fault detection requires a method that can separate fault signatures from operating condition information. Conventional gearbox monitoring techniques assume that a change in the vibration signal is caused by the presence of a fault. Under constant operating conditions this assumption may be valid, but under fluctuating conditions the assumption does not hold.

Fluctuating operating conditions are inevitable for gearboxes in mining and wind turbine industries due to fluctuating ground and wind properties. The fluctuating conditions cause smearing of the signal frequency spectrum and valuable diagnostic information is lost when using classical condition monitoring techniques. More sophisticated signal processing techniques are therefore needed to effectively diagnose incipient faults to make informed asset management decisions.

In this dissertation, envelope analysis, which has long been recognized as one of the best methods for bearing fault diagnosis, is used as the primary diagnostic tool. A common precursor to envelope analysis is bandpass filtering which is aimed at emphasising bearing faults and removing background noise and deterministic components. Identification and optimal selection of the informative frequency band which contains damage related information is the focus area for research in this dissertation. Many automatic band selection techniques exist and have proven effective under constant speed conditions. However, it has been shown that these techniques occasionally identify frequency bands that contain non-damage related information, especially under fluctuating speeds and low damage levels.

With this research, a new methodology is proposed which makes use of popular informative frequency band selection techniques, such as the Fast Kurtogram amongst others, to effectively identify damage under constant and fluctuating speed conditions. The proposed methodology uses both healthy and damaged vibration signals to identify novelty information. In doing so, the method can also identify damage earlier than existing methods. The technique is designed to ignore potentially dominant deterministic components which would lead to incorrect band selection for envelope analysis.

Furthermore, pre-whitening of vibration signals is a common technique to enhance the bearing signal-to-noise ratio. Without pre-whitening, random noise and deterministic components often dominate the bearing fault signatures and render existing diagnostic techniques ineffective. The proposed methodology is shown to be more robust than existing automatic band selection methods because it requires no pre-whitening. By using both healthy and damaged signals, the proposed methodology favours frequency bands that contain damage related information.

The findings in this dissertation are validated on a range of synthetic signals as well as on actual experimental data. The synthetic signals are constructed from a phenomenological gearbox model where the exact operating and bearing condition can be controlled. The experimental results are statistically compared for a wide range of signals and damage levels such that the robustness of the proposed method can be critically evaluated. It was found that the new method is capable of outperforming existing methods in terms of percentage classification of bearing signals with outer race damage and can detect damage with smaller fault severity.