Global ETD Search

1	Incipient Bearing Fault Detection for Electric Machines Using Stator Current Noise Cancellation Zhou, Wei 14 November 2007 (has links) The objective of this research is to develop a bearing fault detection scheme for electric machines via stator current. A new method, called the stator current noise cancellation method, is proposed to separate bearing fault-related components in the stator current. This method is based on the concept of viewing all bearing-unrelated components as noise and defining the bearing detection problem as a low signal-to-noise ratio (SNR) problem. In this method, a noise cancellation algorithm based on Wiener filtering is employed to solve the problem. Furthermore, a statistical method is proposed to process the data of noise-cancelled stator current, which enables bearing conditions to be evaluated solely based on stator current measurements. A detailed theoretical analysis of the proposed methods is presented. Several online tests are also performed in this research to validate the proposed methods. It is shown in this work that a bearing fault can be detected by measuring the variation of the RMS of noise-cancelled stator current by using statistical methods such as the Statistical Process Control. In contrast to most existing current monitoring techniques, the detection methods proposed in this research are designed to detect generalized-roughness bearing faults. In addition, the information about machine parameters and bearing dimensions are not required in the implementation. Stator current noise cancellation Bearing fault diagnosis Electric machine condition monitoring Sensorless speed detection Machine vibration Statistical processing control Bearings (Machinery) Nondestructive testing Fault location (Engineering)
2	Sensorless Stator Winding Temperature Estimation for Induction Machines Gao, Zhi 17 October 2006 (has links) The organic materials used for stator winding insulation are subject to deterioration from thermal, electrical, and mechanical stresses. Stator winding insulation breakdown due to excessive thermal stress is one of the major causes of electric machine failures; therefore, prevention of such a failure is crucial for increasing machine reliability and minimizing financial loss due to motor failure. This work focuses on the development of an efficient and reliable stator winding temperature estimation scheme for small to medium size mains-fed induction machines. The motivation for the stator winding temperature estimation is to develop a sensorless temperature monitoring scheme and provide an accurate temperature estimate that is capable of responding to the changes in the motors cooling capability. A discussion on the two major types of temperature estimation techniques, thermal model-based and parameter-based temperature techniques, reveals that neither method can protect motors without sacrificing the estimation accuracy or motor performance. Based on the evaluation of the advantages and disadvantages of these two types of temperature estimation techniques, a new online stator winding temperature estimation scheme for small to medium size mains-fed induction machines is proposed in this work. The new stator winding temperature estimation scheme is based on a hybrid thermal model. By correlating the rotor temperature with the stator temperature, the hybrid thermal model unifies the thermal model-based and the parameter-based temperature estimation techniques. Experimental results validate the proposed scheme for stator winding temperature monitoring. The entire algorithm is fast, efficient and reliable, making it suitable for implementation in real time stator winding temperature monitoring. Rotor slot harmonics Rotor eccentricity harmonics Unbalanced supply Temperature estimation Rotor resistance estimation Rotor speed detection Complex space vector Condition monitoring Goertzel algorithm Hybrid thermal model Impaired cooling Induction machines Motor overload protection Motor parameter estimation Motor thermal protection Proportional integral observer
3	Railway curve squeal: Statistical analysis of train speed impact on squeal noise Asplund, Ruben January 2024 (has links) No description available. ml operation maintenance railway maintenance railway curve machine learning statistics railway train passenger train x60 squeal noise curve squeal squealing noise squeal wheel squeal squealing programming system development computer engineering data visualization data preprocessing industrial computer systems matlab python vibration vibrations vibration signals signals and systems filtering signal processing computer systems communication system infrastructure technology infrastructure track t-test z-test random forest xgboost feature importance speed detection band-pass filter script yaw angle angle of attack decision tree gbdt filter temperature humidity dew point curve radius cloud accelerometers sensors track rail statistical analysis acoustics sound ai artificial intelligence gradient boosting decision trees curve software development drift underhåll järnvägsunderhåll järnvägskurva maskininlärning statistik järnväg tåg x60 skrikljud snäva kurvor kurvradie kurvskrik hjulskrik programmering systemutveckling datateknik data industriella datorsystem matlab python vibration signaler vibrations signaler signaler och system filtrering signalbehandling datorsystem kommunikationssystem infrastrukturteknik infrastruktur spår t-test z-test random forest Södertälje band-pass filter hastighets detektion attackvinkeln anfallsvinkel IMx SKF temperatur fuktighet daggpunkt accelerometer järnvägskurvskrik tåg hastighetens statistisk analys akustik ljud ai kurva kurvor mjukvaruutveckling artificiell intelligens Infrastructure Engineering Infrastrukturteknik Signal Processing Signalbehandling Communication Systems Kommunikationssystem Computer Systems Datorsystem

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