附录Ⅱ
Proceedings of the 7th ICFDM2006
International Conference on Frontiers of Design and Manufacturing June 19-22, 2006, Guangzhou, China Pages 255-260
A STUDY ON VIBRATION SIGNAL-BASED FEATURE EXTRACTION FOR GRINDING SPINDLE-TYPED ROTOR-BEARING SYSTEM
DURING ACCELERATION
Jong-Kweon Park , Bong-Suk Kim , Soo-Hun Lee and Jun-Yeob Song Intelligence and Precision Machinery Research Division, KIMM, 305 343, Rep. of Korea
School of Mechanical Engineering, Ajou University, 443 749, Rep. of Korea
Abstract: The goal of system monitoring is to minimize economic loss, to increase reliability, to maximize productivity, and to maintain product quality in manufacturing. Since vibration signals sufficiently contain the abundant
running information of the real system and the hidden fault symptoms, the feature extraction through those signals is widely applied for performance evaluation fault diagnostics of rotating machineries.
This paper shows feature extraction from vibration signals gathered
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in the grinding spindle-typed rotor-bearing system during acceleration in order to monitor an abnormal condition of current system like shaft crack by using various kinds of signal processing methods such as the Fast Fourier Transform, Short-Time Fourier Transform, Wigner-Ville Distribution, and Discrete Wavelet Transform. As well, the result of feature extraction in shaft crack condition was compared with that in normal condition.
Keywords: Feature extraction, Grinding spindle-typed rotor-bearing system, Non-stationary signal processing method, Accelerating process, Wavelet transform
1. Introduction
The condition monitoring or fault diagnosis in rotating machineries and machining process is a crucial requirement in order to maintain reliability, safety, and product quality and to prevent failures or damages .Compared with other machining methods, high- performance grinding process is one of the most complicated and important cutting processes as final machining stage; consequently, the monitoring of grinding process and machine is much more necessary in order to supervise the process and machine and also detect abnormalities . Among various kinds of approaches, vibration signal analysis method for feature extraction and nondestructive damage identification has been widely utilized due to capability to carry the abundant dynamic information and to indicate detailed motion of mechanical systems and to describe simultaneously when a fault occurs or what is its frequency . However, since most of the vibration signals sampled on mechanical systems are non-stationary or transient signals which sufficiently contain additional information or abnormal symptom, which can not be revealed from stationary signal, it is the key how to accurately
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draw dominant feature components from vibration signals because non-stationary signal is more complex than stationary signal. Up to date, for feature extraction of rotating machinery, many kinds of research results have mainly been focused on the stationary signal process; on the other hand, little research has been accomplished for the non-stationary signal process such as speed-up process; especially, there is almost no feature extraction using vibration signal of speed-up condition in the field of grinding process.
.This paper was about a study to extract the dominant features from vibration signals acquired in a laboratory grinding spindle-typed rotor-bearing system during acceleration by using several signal processing methods such as Time Domain Analysis (TDA), Frequency
Domain Analysis (FDA), and the Time-Frequency Analysis Method (TFAM). Modal testing, which detects dynamic characteristics of the system like natural frequency, was performed for the purpose of determining operating range for acceleration in test setup. Vibration data from the bearing housing passing through the distinctive resonance frequencies and frequency band in speed-up process were gathered through the experiments with normal and crack shaft condition. To get prominent signals of abnormality from acquired time data as a fundamental stage for diagnosis or monitoring technology, the Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Wigner-Ville distribution (WVD), and Wavelet Transform (WT) using commercial software were carried out and compared with each result
2. Theoretical Background
2.1. Review of Signal Analysis Methods
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There are two major types of signal in the first natural division category: the stationary signal and non-stationary signal. Stationary signals are constant in their statistical parameters over time. Moreover, stationary signals are further divided into deterministic and random signals.
Random signals are unpredictable in their frequency content and their amplitude level, but they still have relatively uniform statistical characteristics over time.
On the other hand, non-stationary signals are divided into continuous and transient types. Transient signals are defined as signals which start and end at zero level and last a finite amount of time. In the stationary signal analyses, there are the RMS, Peak Value, Average/Distribution, and dynamic time models such as AR model and ARMA model in time
domain analysis as well as the Fourier Transform (FT) in frequency domain analysis. However, most signals of mechanical system like vibrations, noises, and sounds, generate non-stationary signals that data are intricate and irregular, so it is certainly necessary to use non-stationary signal processing methods to extract valid information from those signals. The FT is mainly used in frequency analysis of stationary signals, while frequency analysis
tends to accompany errors with regard to non-stationary signals. Since the frequency analysis simply shows only frequency components when non-stationary transient signals occur, it is difficult to find the time information. However, the TFAMs such as the STFT, WVD, and WT,
which perform frequency analysis at the time when failure occurs, compensate defects of time and frequency domain analysis, so that, it is
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considerably useful for applications to a number of fields.
2.2. Fast Fourier Transform
An infinite-range FT of a real-valued or a complex-valued record x(t) is defined by the complex-valued quantity.
（2.1）
Theoretically, as noted previously, this transform X(f) will not exist for an x(t) that is a representative member of a stationary random process when the infinite limits are used. However, by restricting the limits to a finite time interval of x(t), say in the range (0,T), then the finite-range the FT will exist, as defined by
（2.2）
Assume now that this x(t) is sampled at N equally t apart, where t has been spaced points a distance selected to produce a sufficiently high Nyquist frequency. As before, the sampling times are t=nt. However, it is convenient here to start with n=0 like Eq. (3). Let
（2.3）
Then, for arbitrary f, the discrete version of Eq. (2) is
（2.4）
The usual selection of discrete frequency values for the computation
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提取磨削主轴型转子轴承系统在加速度期间振动信号的特征研究外文文献翻译、中英文翻译
