استفاده از برآورد آمار باقی مانده عمر یاتاقان ها برای تعیین تاثیر اقدامات تعمیر و نگهداری پیشگیرانه

In this paper it is illustrated how statistical residual life estimates of bearings can be used to justify maintenance practices. Residual life estimates are based on Proportional Intensity Models for non-repairable systems utilising historic failure data and corresponding diagnostic measurements. A case study is presented where failure and diagnostic data obtained from roller bearings operating in the dryer section of a paper machine are used to predict future failure times of bearings. If these predictions are compared to the diagnostic measurements, i.e. vibration and lubrication levels, it becomes evident how changes in these diagnostic measurements influence the residual life of the bearings. From this it is possible to justify maintenance practices.

Preventive maintenance is often initiated on industrial equipment based on observed diagnostic measurements such as vibration levels, temperatures, pressures, etc., that are outside of certain acceptable levels. After performing the preventive maintenance the success of this action is judged on a binary basis. If the said diagnostic measurements have decreased or increased sufficiently to be within specification, the preventive maintenance action is considered to be successful. If not, additional actions are often instigated to get all diagnostic measurements within specification. In this paper we describe a methodology to quantify the success of preventive maintenance more accurately than a simple binary number. The residual life estimation methodology of Vlok [1] is applied but not with the sole purpose to predict future failures. It is shown how these estimates can be used to quantify the success and efficiency of preventive maintenance actions. The paper starts off with an introduction to the residual life estimation concept and describes the data required to perform the calculations. It further discusses how this concept can be used to quantify the success of preventive maintenance actions. The validity of the theory is illustrated with a case study in the last section.

As stated in Section 3.1 (see Fig. 5) one can often observe some general patterns in the failure statistics and at the same time introduce some policy changes (in our case it is the amount of grease)—but are we able to assign any procedural changes as responsible for lower failures rate? And, even more important, can we reduce the number of failures even further by introducing more changes to the maintenance procedure? We tried to answer these questions and provide guidance to quantitative judgement of maintenance procedures. Our simple case based on lubrication procedures illustrates well how the residual life estimation can be utilised as a benchmarking parameter. RLE values allow for direct comparison of the health and performance of machinery. It is obvious that one can study several maintenance tasks at the same time—introducing them as covariates and observe the changes they impose on the resulting RLE values. A requirement for this enough statistical evidence from the past to allow the model to converge. It is attractive that when new maintenance policies/recipies are introduced, it is possible to judge their influence reasonably fast, i.e. after only 2–3 diagnostic measurements. Standard maintenance procedures are often prescribed by the vendors for general operating conditions but are not valid when equipment are subjected to very different environments, e.g. high temperatures, high humidity, corrosive substances, etc. The RLE approach allows to individualise the policies for each piece of equipment—even if technically identical. This statement is illustrated by the case of the two bearings. For Bearing D211b, the second reduction in greasing still resulted in extension of the RLE, i.e. it had positive influence—it is not obvious for Bearing D240b. Here the increase in vibration levels nullified the positive impact resulting from change of the greasing level. The reason is that these bearings operate in different sections on the paper machine under different loads and temperatures. This study illustrate just possibilities given by statistical lifetime estimate as a benchmark tool—since we have to comply with historical data and records of the plant we did not control the whole experimental space. While using RLE as a benchmarking tool one should introduce procedures of monitoring process changes that could impact additionally measured covariates. But since in our case we observed similar behaviour related to greasing change for all the bearings used for analysis [6] and knowing in addition that process did not change one may thus exclude some uncontrolled phenomena that could change the results. This study shows that RLE is a very convenient tool to justify, benchmark and optimise maintenance practices.