In machinery and equipment, sliding bearings are used more frequently, but they are prone to wear. In the actual application process, the composition of the oil sample can be monitored and analyzed using iron spectrum analysis, so that abnormalities can be found in time to facilitate timely troubleshooting by machinery maintenance personnel.
Although vibration analysis can also effectively detect the situation of mechanical operation failure, but wear failure is more difficult to troubleshoot, and the sliding bearing wear at the beginning, its working condition is still in the normal state, and wear will not affect the normal operation of other parts, so that the overall mechanical vibration parameters may be in the normal parameter range, and thus can not effectively predict the obstacles.
Different from the vibration analysis method, the iron spectrum analysis method can effectively detect a large number of abrasive particles, so as to provide a scientific basis for early troubleshooting. However, in practical application, since ferro-spectroscopy is mainly sensitive to ferromagnetic substances, but is slow to respond to non-magnetic substances, it may fail if the amount of non-magnetic nature substances is not large. This shows that the application of iron spectrum analysis to predict the wear failure of sliding bearings is difficult.
In this regard, enterprises should actively strengthen the research on failure prediction technology, carefully study the causes of main exhaust sliding bearing wear, accumulate experience, and propose effective treatment measures to prevent the occurrence of failure, so as to reduce the event of sliding bearing failure, reduce the economic loss due to failure, and improve the economic efficiency of enterprises.
