Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships. (July 2018) - Silva, Andre A.; Gupta, Shalabh; Bazzi, Ali M.; Ulatowski, Arthur
Abstract: Electric machines and drives have enjoyed extensive applications in the field of electric vehicles (e.g., electric ships, boats, cars, and underwater vessels) due to their ease of scalability and wide range of operating conditions. This stems from their ability to generate the desired torque and power levels for propulsion under various external load conditions. However, as with the most electrical systems, the electric drives are prone to component failures that can degrade their performance, reduce the efficiency, and require expensive maintenance. Therefore, for safe and reliable operation of electric vehicles, there is a need for automated early diagnostics of critical failures such as broken rotor bars and electrical phase failures. In this regard, this paper presents a fault diagnosis methodology for electric drives in electric ships. This methodology utilizes the two-dimensional, i.e. scale-shift, wavelet transform of the sensor data to filter optimal information-rich regions which can enhance the diagnosis accuracy as well as reduce the computational complexity of the classifier. The methodology was tested on sensor data generated from an experimentally validated simulation model of electric drives under various cruising speed conditions. The results in comparison with other existing techniques show a high correct classification rate with low false alarm and miss detection rates. Abstract : Highlights: Electric machines and drives have enjoyed extensive applications in the field of electric vehicles (e.g., electric ships, boats, cars, and underwater vessels) due to their ease of scalability and wide range of operating conditions. However, as with the most electrical systems, the electric drives are prone to component failures that can degrade their performance, reduce the efficiency, and require expensive maintenance. Despite the additional benefits of the scale-shift information present in the wavelet domain for fault diagnosis, there exists added computational complexity for machine learning for fault classification. Thus, there is still a gap in understanding whether the entire two-dimensional domain of the wavelet transform is necessary for motor diagnosis. This paper developed a wavelet-based filtering methodology for fault diagnosis of electric drives in electric ships, which uses the two-dimensional, i.e. scale-shift, wavelet transform of the sensor data to extract optimal information-rich regions as features which can enhance the diagnosis accuracy as well as reduce the computational complexity of the classifier. The fault diagnosis methodology is built upon the following four main processes: 1) Wavelet transformation of the motor current time-series data, 2) Filtering of optimal regions from the wavelet domain based on their available information content to separate different fault classes, 3) Feature extraction via further reduction of the filtered data using the Principal Component Analysis (PCA), and 4) Pattern classification using a diagnostic tree classifier for sequential diagnosis of different faults in the system. Validation of the methodology was done using an experimentally-validated simulation model of the motor drive system for electric ships using a cross-validation process which yielded high correct classification rate.