Improving the sensitivity of early rub detection in rotating machines with an adaptive orthogonal filter. (15th May 2022) - Silva, Alejandro; González-Guillén, Carlos; Ruiz-García, Miguel; Domínguez-Jiménez, María Elena
Abstract: The early detection of rotor-casing rub helps to prevent permanent damage or catastrophic failure, saves maintenance costs and decreases the downtime of rotating machines. In aeroderivative gas turbines, rub detection is possible only with the use of casing sensors such as accelerometers. In previous works, the Wavelet Synchrosqueezed Transform (WSST) was proposed for very early detection of rub in aeroderivative gas turbines with accelerometer signals. In a quest for even better detection performances, this manuscript aims to design adaptive orthogonal wavelet filter banks for optimal rub feature separation from accelerometer signals. The outputs of the adaptive filter bank show improved feature separation between the no-rub and the rub regimes within a validation set from an experimental machine. Ultimately, it is demonstrated that the combination of the WSST and the adaptive orthogonal filter outperforms other methodologies for the early detection of rotor-casing rub with accelerometer signals, also when white noise is present in the data. Highlights: Adaptive orthogonal wavelet filters improve separation between fault/no fault states. With those we achieved a more accurate early rub detection within acceleration data. The filters synthesize the fundamental signal features in a set of coefficients. Samples from the machine in normal operation and under rub are enough to optimize the filters.