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8 Письменно ответьте на следующие вопросы используя текст
1.What is the aim of machine fault diagnosis?
2.What are the stages of machine fault diagnosis?
3.What methods of machine fault diagnosis do you know?
4.Why is machine fault diagnosis widely used in industry?
Machine field diagnosis.
Machine fault diagnosis is a field of mechanical engineering concerned with finding faults arising in machines. A particularly well developed part of it applies specifically to rotating machinery, one of the most common types encountered. To identify the most probable faults leading to failure, many methods are used for data collection, including vibration monitoring, thermal imaging, oil particle analysis, etc. Then these data are processed utilizing methods like spectral analysis, wavelet analysis, waveform analysis (in the time domain, because spectral analysis usually concerns only frequency distribution and not phase information) and others. The results of this analysis are used in a root cause failure analysis in order to determine the original cause of the fault. For example, if a bearing fault is diagnosed, then it is likely that the bearing was not itself damaged at installation, but rather as the consequence of another installation error (e.g., misalignment) which then led to bearing damage. Diagnosing the bearing's damaged state is not enough for precision maintenance purposes. The root cause needs to be identified end remedied. If this is done, the replacement bearing will soon wear out for the same reason and the machine will suffer more damage, remaining dangerous. Of course, the cause may also be visible as a result of the spectral analysis undertaken at the data-collection stage, but this may not always be the case.
A neural network can be applied to the fault diagnosis of the machine. The neural network has learning and memory capability. The proposed fault diagnosis system is based on the spectrum of vibration or sounds obtained from the operating machine. The difference between normal and abnormal data becomes clearer when comparing time series data. It is suitable for the detection of the fault to utilize changes of spectral data. Using this method, it is shown that it can detect unknown fault patterns.
Fault diagnostics in usual industrial practice need to be applied according to guidelines. This need arises from the fact that diagnostics on their own may be capable of saving a single machine if monitoring is adequate, but it is impossible to apply them to all the equipment. The investment needed to either install continuous condition monitoring sensors on all the machinery in a factory or to check enough samples from all machinery on a regular basis would be forbidding.
As a result, using fault diagnostics to meet industrial needs in a cost effective way, and to reduce maintenance costs without requiring more investments than the cost of what is to be avoided in the first place, requires an effective scheme of applying them.