The nature of data Age data Condition data

2 There are two major types of data: 1.Age (event) data: 2.Condition monitoring data:

3 There are two major types of data: 1.Age (event) data: 2.Condition monitoring data: 1.the beginning of a life-cycle, and 2.the ending of a life-cycle:

4 There are two major types of data: 1.Age (event) data: 2.Condition monitoring data: 1.the beginning of a life-cycle, and 2.the ending of a life-cycle: 1.By failure: 2.By suspension, and

5 There are two major types of data: 1.Age (event) data: 2.Condition monitoring data: 1.the beginning of a life-cycle, and 2.the ending of a life-cycle: 1.By failure: 2.By suspension, and 1.Potential 2.Functional

6 There are two major types of data: 1.Age (event) data: 2.Condition monitoring data: 1.the beginning of a life-cycle, and 2.the ending of a life-cycle: 1.By failure: 2.By suspension, and 1.Potential 2.Functional 1.Measurements and inspections 2.Process data:

7 There are two major types of data: 1.Age (event) data: 2.Condition monitoring data: 1.the beginning of a life-cycle, and 2.the ending of a life-cycle: 1.By failure: 2.By suspension, and 1.Potential 2.Functional 1.Measurements and inspections 2.Process data: 3. Non rejuvenating events

8 There are two major types of data: 1.Age (event) data: 2.Condition monitoring data: 1.the beginning of a life-cycle, and 2.the ending of a life-cycle: 1.By failure: 2.By suspension, and 1.Potential 2.Functional 1.Measurements and inspections 2.Process data: 1.External variables 2.Internal variables 3. Non rejuvenating events

9 There are two major types of data: 1.Age (event) data: 2.Condition monitoring data: 1.the beginning of a life-cycle, and 2.the ending of a life-cycle: 1.By failure: 2.By suspension, and 1.Potential 2.Functional 1.Measurements and inspections 2.Process data: 1.External variables 2.Internal variables Maintenance departments focus considerable attention on obtaining this type of data. 3. Non rejuvenating events

10 There are two major types of data: 1.Age (event) data: 2.Condition monitoring data: 1.the beginning of a life-cycle, and 2.the ending of a life-cycle: 1.By failure: 2.By suspension, and 1.Potential 2.Functional 1.Measurements and inspections 2.Process data: 1.External variables 2.Internal variables However, all these forms of age data and condition monitoring data are needed for effective Reliability Analysis. 3. Non rejuvenating events

11 Two types of CBM variables 1. External: CBM measurements that detect abnormal stresses on a system that, if uncorrected, will eventually and predictably provoke a failure that has not yet initiated, and 2. Internal: CBM measurements that detect the accumulated result of (normal and abnormal) stresses – that is, they detect evidence of a failure mode that has already begun, but has not progressed to the point where a required function has been lost. Oil and vibration analysis and other CBM observatons track this type of CBM variable.

12 Maintenance departments focus considerable attention on otaining this type of CBM data. Two types of CBM variables 1. External: CBM measurements that detect abnormal stresses on a system that, if uncorrected, will eventually and predictably provoke a failure that has not yet initiated, and 2. Internal: CBM measurements that detect the result of abnormal stresses – that is, they monitor a failure that has already begun, but has not progressed to the point where a required function has been lost.

13 Two types of CBM variables 1. External: CBM measurements that detect abnormal stresses on a system that, if uncorrected, will eventually and predictably provoke a failure that has not yet initiated, and 2. Internal: CBM measurements that detect the result of abnormal stresses – that is, they monitor a failure that has already begun, but has not progressed to the point where a required function has been lost. However, both types of data should be considered. Maintenance departments focus considerable attention on otaining this type of CBM data.

14 Two types of CBM variables 1. External: CBM measurements that detect abnormal stresses on a system that, if uncorrected, will eventually and predictably provoke a failure that has not yet initiated, and 2. Internal: CBM measurements that detect the result of abnormal stresses – that is, they monitor a failure that has already begun, but has not progressed to the point where a required function has been lost. Sometimes external variables are simple and inexpensive to acquire, and have even greater predictive content.

15 Non-rejuvenating events Fe ppm Working age e.g. Oil changes, alignment, balancing, cleaning, ajustment, calibration, etc. Can affect CM data but do not add life to an asset. To demonstrate the use of non- rejuvenating event data, this chart will track a CBM data trend.

16 The condition of the equipment at point C appears to be improving. Non-rejuvinating events A C B Fe ppm Working age e.g. Oil changes, alignment, balancing, cleaning, ajustment, calibration, etc. Can affect CM data but do not add life to an asset.

17 Non-rejuvinating events A C B Fe ppm Working age e.g. Oil changes, alignment, balancing, cleaning, ajustment, calibration, etc. If we infer that the path is ABC. Can affect CM data but do not add life to an asset.

18 Non-rejuvinating events A C B Fe ppm Working age D Oil change interval e.g. Oil changes, alignment, balancing, cleaning, ajustment, calibration, etc. But assume we are told that an oil change took place at D. Can affect CM data but do not add life to an asset.

19 Non-rejuvinating events A C B Fe ppm Working age D Oil change interval e.g. Oil changes, alignment, balancing, cleaning, ajustment, calibration, etc. Hence the actual path is ABDC. If we ignore non-rejuvenating events we may mislead the analysis into thinking that the asset’s condition has “magically” improved. EXAKT analysis will determine whether and how NR events should be included. Can affect CM data but do not add life to an asset.