Dong Sik Gu

Rolling element bearing fault detection using acoustic emission signal analyzed by envelope analysis with discrete wavelet transform

Acoustic Emission (AE) what is a non-destructive testing technique is widely used for the early detection of faults in rotating machine in these days, because its sensitivity is higher than normal accelerometers and it can detect low energy vibration signals. The faults in rotating machines are generally occurred at bearing and/or gearboxes which are one of the principal parts of the machines. To detect the bearing fault, envelope analysis was studied and presented for several decade years. And the researches spoke that AE has a possibility of the application in condition monitoring system using envelope analysis for the rolling element bearing. And the peak ratio (PR) was developed for expression of the bearing condition in condition monitoring system using AE. The noise level is needed to reduce to get exact PR value because PR is calculated from total root mean square (RMS) and the harmonics of the defect frequencies. Therefore, in this paper, the discrete wavelet transform (DWT) was added in envelope analysis to reduce the noise level in AE signals. And then, the PR was calculated and compared with general envelope analysis result and the result of envelope analysis added DWT.

Machinery Faults Detection Using Acoustic Emission Signal

Application of the high-frequency acoustic emission (AE) technique in condition monitoring of rotating machinery has been growing over recent years. This is particularly true for bearing defect diagnosis and seal rubbing (Mba et al., 1999, 2003, 2005; Kim et al., 2007; Siores & Negro, 1997). The main drawback with the application of the AE technique is the attenuation of the signal and as such the AE sensor has to be close to its source. However, it is often practical to place the AE sensor on the non-rotating member of the machine, such as the bearing or gear casing. Therefore, the AE signal originating from the defective component will suffer severe attenuation before reaching the sensor. Typical frequencies associated with AE activity range from 20 kHz to 1 MHz. While vibration analysis on gear fault diagnosis is well established, the application of AE to this field is still in its infancy. In addition, there are limited publications on application of AE to gear fault diagnosis. Siores explored several AE analysis techniques in an attempt to correlate all possible failure modes of a gearbox during its useful life. Failures such as excessive backlash, shaft misalignment, tooth breakage, scuffing, and a worn tooth were seeded during tests. Siores correlated the various seeded failure modes of the gearbox with the AE amplitude, root mean square, standard deviation and duration. It was concluded that the AE results could be correlated to various defect conditions (Siores et al., 1997). Sentoku correlated tooth surface damage such as pitting to AE activity. An AE sensor was mounted on the gear wheel and the AE signature was transmitted from the sensor to data acquisition card across a mercury slip ring. It was concluded that AE amplitude and energy increased with increased pitting (Sentoku, 1998). In a separated study, Singh studied the feasibility of AE for gear fault diagnosis. In one test, a simulated pit was introduced on the pitch line of a gear tooth using an electrical discharge machining (EDM) process. An AE sensor and an accelerometer for comparative purposes were employed in both test cases. It was important to note that both the accelerometer and AE sensor were placed on the gearbox casing, it was observed that the AE amplitude increased with increased rotational speed and increased AE activity was observed with increased pitting. In a second test, periodically occurring peaks were observed when natural pitting started to appear after half an hour of operation. These AE activities increased as the pitting spread over more teeth. Singh concluded that AE could provide earlier detection over vibration monitoring for pitting of gears, but noted it could not be applicable to extremely high speeds or for

Development of Rotordynamics Program Based on the 2D Finite Element Method for Flywheel Energy Storage System

Flywheel energy storage system (FESS) is defined as a high speed rotating flywheel system that can save surplus electric power. The FESS is proposed as an efficient energy storage system because it can accumulate a large amount of energy when it is operated at a high rotating speed and no mechanical problems are encountered. The FESS consists of a shaft, flywheel, motor/generator, bearings, and case. It is difficult to simulate rotor dynamics using common structure simulation programs because these programs are based on the 3D model and complex input rotating conditions. Therefore, in this paper, a program for the FESS based on the 2D FEM was developed. The 2D FEM can model easier than 3D, and it can present the multi-layer rotor with different material each other. Stiffness changing of the shaft caused by shrink fitting of the hub can be inputted to get clear solving results. The results obtained using the program were compared with those obtained using the common programs to determine any errors.

Eigenvalue Analysis of Roots Type Vacuum Pump Using FE-Method

The demand and importance of vacuum pumps are increasing, because the necessity of dry vacuum pump is proven in semiconductor industry. Various types of vacuum pumps have been developed, and in this research a roots type vacuum pump has been analyzed with a variety of mathematical analysis. Analysis of eigenvalue by FEM analysis and Modal Test were performed through the Roots type vacuum pump and using the results of irregular vibration and instability an assessment has been performed.

Signal Processing Method (HT and HHT) of AE for Complex Fault Detection

In the signal processing method, envelope analysis is a useful method to evaluate the rolling element bearing problems and Wavelet transform is a powerful method to detect faults occurred on gearboxes. However, exact method for AE signal is not developed yet. Therefore, in this paper, two methods, which are Hilbert Transform (HT) and Hilbert-Huang transforms (HHT), will be compared for development a signal processing method for early fault detection system by using AE. HHT has better advantages than HT because HHT can show the time-frequency domain result. But, HHT needs long time to process a signal, which has a lot of data, and has a disadvantage in de-noising filter.

Vibration Analysis of Ship Propulsion Shaft with the Stern Bearing Characteristics

Efficiency improvements in ships lead often to increased size and speed. However, due to the increase in the agencys output, the weight of the propeller, propulsion shaft, vane pitch and the number is increased. Simultaneously, various. vibrations occur in the shaft. Thus, the design of shaft, and vibration analysis has become a challenging matter. In this study the vibration analysis of ship propulsion shaft has been performed, considering the stern bearing characteristics, and established the measures for shaft vibration with suggestion for shaft design method.

Analytical Evaluation of Rotor Dynamic Characteristic of Roots Type Vacuum Pump

The goal of this study is the stability evaluation of a vacuum pump through modal test and rotor dynamics. Roots type vacuum pump, which is a dry vacuum pump, is necessary for the manufacturing process of the semiconductor and the display. Eigenvalue was solved by the finite-element method(FEM) using 2D and 3D models, then the modal test result was compared with the FEM result. According to the comparison, the analysis result using the 2D was more accurate than the 3D model. Therefore, rotor dynamics was performed by the 2D model. Campbell diagram and root-locus maps, which were calculated by complex-eigenvalue analysis, were used to evaluate the stability of the rotors of the vacuum pump. And displacement solved by unbalance response analysis was compared with the minimum clearance between two rotors of the vacuum pump. Thus, the vacuum pump is assumed operated under steady state through the evaluation of the rotor dynamics.

Comparison of vibration analysis with different modeling method of a rotating shaft system

The numerical analysis is the basic step of machine designing and the result from it is the fundamental data for the assess management of machine/plant. Recently, many kinds of softwares based on different method, like Finite Element Method (FEM) and Transfer Matrix Method (TMM) were developed for the machine design. Especially the development of simulating a rotating component, gas/steam/wind turbine shaft, high speed shaft, the shaft of large vessels and the shaft with blades, flywheels and gears. In order to reduce the error, it always needs to increase the number of element, some times over 100,000. But it will affect the inputing matrix size and calculating time. In this paper, a 2D modelling method was proposed to reduce the element size and solving time. This paper compared the simulation results from MSC Partan/Nastran and ANSYS Workbench using a 3D model and the results by solving MATLAB based on FEM using a 2D model.

Application of Envelop Analysis and Wavelet Transform for Fault Detection in Local Gearboxes Failure

Vibration analysis is widely used in machinery diagnosis and the wavelet transform has also been implemented in many applications in the condition monitoring of machinery. In contrast to previous applications, this paper examines whether acoustic signal can be used effectively along vibration signal to detect the various local fault, in local fault of gearboxes using the wavelet transform. Moreover, envelop analysis is well known as useful tool for the detection of rolling element bearing fault. In this paper, acoustic emission (AE) sensor is employed to detect gearbox damage by installing them around bearing housing at driven-end side. Signal processing is conducted by wavelet transform and enveloping to detect the fault all at once gearbox and bearing using AE signal. Result of fault detection is presented using some general statistical features and a proposed new feature (RGF: Ratio of Gear Frequency) for gear fault calculated from AE signal with different condition.© 2008 ASME