外文翻译-机械故障检测使用模糊的索引融合.doc

1、Mechanical fault detection using fuzzy index fusion Tony Boutrosa and Ming LiangaDepartment of Mechanical Engineering, University of Ottawa, 770 King Edward Avenue, Ottawa, Ont., Canada K1N 6N5 Received 22 June 2006; revised 26 December 2006; accepted 3 January 2007. Available online 23 January 2007

2、.Abstract This paper reports a simple, effective and robust fusion approach based on fuzzy logic and Sugeno-style inference engine. Using this method, four condition-monitoring indicators, developed for detection of transient and gradual abnormalities, are fused into one single comprehensive fuzzy f

3、used index (FFI) for reliable machinery health assessment. This approach has been successfully tested and validated in two different applications: tool condition monitoring in milling operations and bearing condition assessment. The FFI differentiates clearly between the normal and abnormal conditio

4、ns using the same fuzzy rule base. This certainly shows the versatility and robustness of the FFI. As the FFI value always falls between zero and one, it facilitates threshold setting in monitoring conditions of different tools or machinery components. Our experimental study also indicates that the

5、FFI is sensitive to fault severity, capable of differentiating damages caused by an identical fault at different bearing components, but not susceptible to load changes. Keywords: Condition indicators; Fuzzy fusion; Sugeno inference engine; Tool condition; Bearing condition Article OutlineMachinery

6、fault detection and machining process monitoring have attracted considerable attention. These tasks have become increasingly difficult due to the complexity of machine structure and operation dynamics. Over the last few decades, many different sensors and condition indicators have been developed in

7、an attempt to achieve more reliable results for different monitoring tasks. For machinery fault detection, Collacott 1 used the probability density and kurtosis of vibration signalfor bearing defect identification in an early study. It was found that the probability density of the acceleration of a

8、bearing in good condition has a Gaussian distribution, whereas a damaged bearing resulted in a non-Gaussian distribution with dominant tails. Along this line, Dyer and Stewart 2 also used kurtosis for bearing defect detection based on vibration signal. For an undamaged bearing with Gaussian distribu

9、tion, the kurtosis value was found equal to three. A value greater than three was judged as an indication of impeding failure. However, one disadvantage was noted: the kurtosis value could come down to the level of a normal bearing even when the damage was well advanced. Later, Miyachi and Seki 3 ex

10、tracted the root-mean-square (r.m.s.) and crest factor from vibration signal to monitor the defects in ball bearings. However, the results were not very successful. Liu and Mengel 4 used the peak amplitude in the frequency domain, peak r.m.s. and the power spectrum as indirect indices for monitoring

11、 ball bearing vibration. Heng and Nor 5 reported the application of sound pressure and vibration signals to the detection of bearing faults using a statistical analysis method. The parameters considered in their study included the r.m.s., crest factor and kurtosis. Results obtained through experimen

12、ts revealed that the statistical parameters were subject to the influence of shaft speed. Recently, Baydar and Ball 6 examined the use of acoustic signal along with vibration signals for monitoring various local faults in a gearbox using the wavelet transform. Two commonly encountered local faults w

13、ere simulated: tooth breakage and tooth crack. The results suggested that acoustic signals were very effective for the early detection of faults. However, the influence of load variation on the fault detection capability of the acoustic approach was not considered. For machining process and tool con

14、dition monitoring, the task could be more difficult due to the nonlinear process caused by the interaction of the dynamics of material removal, the dynamics of machine tool and machine tool drive 7. Inasaki 8 developed a monitoring and control system for grinding processes. The system utilized acous

15、tic emission (AE) and power sensors to monitor the grinding process and to construct a control database. Everson and Cheraghi 9 investigated the correlation between the quality of a hole drilled in steel and the AE signal parameters. The AE energy, number of peak amplitudes above a certain threshold

16、 and the r.m.s. were used in this investigation. Experimental work was conducted to validate the method. They observed that the AE energy was a good measure but the peak amplitude count as a condition indicator was inefficient in certain cases where signal was short. There is a rich body of literature on tool condition monitoring. Some of the well-cited studies include the use of AE for tool condition assessment 10, 11 and 12, joint use of A