Spektral trend ve durağan dalgacık dönüşümü yardımıyla durum izleme ve arıza tanısı

In this study, a spectral trending method is developed as a simple and feasible condition monitoring technique for electric motors. Considering the situation of the motor, a fault detection approach benefiting from the redundancy property of Stationary Wavelet Transform (SWT) is proposed. Through this, the study presents an integrated condition monitoring and fault detection approach using vibration signals of an induction motor. Today's world industry constitutes of electric motors and their control applications. Induction motors are the most preferred type in industry due to several advantages. In the industrial application the most important concept is reliability. This is due to the fact that, any interruption or deceleration in the industrial process may result in huge financial losses. Therefore, condition monitoring of induction motors is a very popular research area. There are important surveys related to the failures of induction motors. As the result of these surveys, it can be concluded that the most common failure mode is bearing failures with a rate of approximately 40%. Winding faults are also very frequent with 30%, whereas rotor related faults are at around 9%. In order to detect these faults, several condition monitoring techniques are used, according to the needs of the system. This way, the machine performance can be tracked and required precautions can be considered. These monitoring techniques are listed as: electrical current, flux, power, mechanical vibration, temperature, wearing and electrical discharge. In order to interpret and assess the monitoring information, a diagnostic system is designed and established. Diagnostic systems are classified in three categories: model based, statistical data based and signal based. In this study, vibration signals are employed due to their capability to reflect either electrical or mechanical fault signatures. SWT is employed as the signal based diagnostic method. Conventional condition monitoring and fault detection systems calculate specific fault frequencies for each motor and track them regularly. However, the faults do not suddenly pop up, they progress in time and raise the critical aging condition in the motor. Considering this gradual and progressive nature, the aging is trended in this study. This way, the motor situation can be classified as healthy or not. If the motor is not healthy, there are plenty of techniques to identify the fault. However, if it is healthy, there might still be some minor indications which may amplify in the future. For that reason, redundancy of SWT is employed to detect the potential faults of the motor. The study has two parts as classification and analysis. In the classification part, Power Spectral Densities (PSD) of vibration signals for different aging cycles are calculated logarithmically. In order to express each aging cycle individually, linear models are fitted to logarithmic PSDs. Hence, linear models become an alternative tool for the spectral domain and these linear variations can define a closed region in sense of convexity. The geometric interpretation of trends in the convex region is achieved, thus a new and efficient vibration monitoring strategy is proposed. In addition, a classification approach is introduced to determine the situation of the motor. In the second part, the situations named as healthy are taken into consideration. An early and sensitive detection method is aimed using SWT. SWT is a redundant transform due to its nature. This redundancy is preserved on purpose to amplify the existing small fault signatures. The method is developed on an artificial data and then verified on an experimental data. The experimental data has been taken from The University of Tennessee Knoxville (UTK). The experimental setup had been built in a research and development project supported by The University of Tennessee Maintenance and Reliability Centre. The project was an extensive research on fault detection in induction motors. For this purpose, an accelerated aging experiment, which contains two phases as electrical discharge machining and thermal-chemical aging operations, is realized. This study has six sections; they are listed and described as below; In the first section, the reason for preferring induction motors is given. Their failure modes, monitoring techniques and fault detection algorithms are introduced with several references. In order to emphasize the need for SWT, signal based detection methods are given in detail. In the second section, the convex region approach, spectral trending and types of wavelet transforms are presented as the mathematical background of the study. Redundancy concept is explained through reconstruction operations. In the third section, the aging procedure is presented. Then the accelerated aging experiment is given in detail with standards and aging actions. The aging cycles are introduced through time and frequency domain representations. In the fourth section, the classification part of the study is performed. Logarithmic PSDs are calculated and linearly trended to deteremine the situation of the motor. Thus, the progress of aging becomes trackable. These trends define an Euclidian plane and they create a convex region together with the measurement boundary. The cycles within this region can be rated and graded in terms of health. In the fifth section, a modification is proposed for the reconstruction algorithm of SWT to conserve the redundancy. The modified approach is named as the algebraic summation method. The development phase of the method is improved on artificial data, then it is applied to healthy cycle data. It is seen that it amplifies very small fault indications as a reflection of redundancy. Through this, an early and sensitive detection method is introduced. In addition, the catastrophy limits for a motor is identified originating from algebraic summation. In the conclusion section, the findings and comments are given, the contributions are highlighted. Spectral trending is a very simple method to estimate the situation of the motor and it is very applicable for industry. The study defines the health of a motor as a rateable variable. The definition of the convex region approach is a new and original concept for motor monitoring. The study increases the effectiveness of vibration monitoring by new monitoring strategy. Algebraic summation in the signal reconstruction step of SWT brings an early and sensitive detection possibility for the healthy case data. In addition, the degradation limits are determined inspiring algebraic summation and convexity concept. These contributions are very significant in terms of effective condition monitoring and detection of incipient faults. This study presents an integrated condition monitoring and fault detection approach which may be used by maintenance engineering widely, because it is practical, simple and applicable. The study can be employed to reschedule and reinterpret the maintenance. Furthermore, rating of aging is a complicated process and it is required for critical applications. This study meets this need successfully, as well. From a wider perspective, geometric interpretations of trends and redundancy based fault detection are suitable to be employed in different fields of engineering for monitoring and diagnostic aims.