How do I inject the reliability of machines into my ANN - sql-server

Question

I would like to predict the Reliability of my physical machines by ANN.
Q1) What is the right metric that measure the reliability for repairable machine.
Q2) In order to calculate the reliability of each machine in each time period or row should I calculate TBF or MTBF, and feed my ANN.
Q3) Is ANN a good machine learning approach to solve my issue
Lets take a look.
In my predictor ANN. One of the input is the current reliability value for my physical machines by applying the right distribution function with right metric MTBF or MTTF. In sample data, there are two machines with some log events.
Time , machine ID, and event_type. event_type = 0 when a machine became available to the cluster, event_type=1 machine failed, and when event_type=2 when a machine available to the cluster had its available resources changed.
For non-repairable product MTTF is preferred to use to measure the reliability, and MTBF is for repairable product.
What is the right metric to get the current reliability value for each time period row , is it TBF or MTBF . Previously I use MTTF= TOTAL UPTIME/TOTAL NUMBER OF FAILURE. To calculate the UPTIME, I subtract the time in event_type = 1 from first previous time in event_type=0, and so on, then divide the total UPTIME by number of failure. Or I need to TBF for each row. Machine events table looks like:
time machine_id event_type R()
0 6640223 0
30382.66466 6640223 1
30399.2805 6640223 0
37315.23415 6640223 1
37321.64514 6640223 0
0 3585557842 0
37067.13354 3585557842 1
37081.0917 3585557842 0
37081.2932 3585557842 2
37321.33633 3585557842 2
37645.77424 3585557842 1
37824.73506 3585557842 0
37824.73506 3585557842 2
41666.42118 3585557842 2
After Preprocessing previous table of machine events to get input_2 (Reliability) to the training data table the expected table should be look like:
start_time machine_id input_x1 input_2_(Relibility) Predicied_output_Relibility
0 111 0.06 xx.xx
1 111 0.04 xx.xx
2 111 0.06 xx.xx
3 111 0.55 xx.xx
0 222 0.06 xx.xx
1 222 0.06 xx.xx
2 222 0.86 xx.xx
3 222 0.06 xx.xx

Answer 1

mean time TO failure
It is (or should be) a predictor of equipment reliability. The TO in that term indicates it's predictive intent.
Mean time to failure (MTTF) is the length of time a device or other
product is expected to last in operation. MTTF is one of many ways to
evaluate the reliability of pieces of hardware or other technology.
https://www.techopedia.com/definition/8281/mean-time-to-failure-mttf
e.g.
take total hours of operation of same equipment items
divided by the number of failures of those items
If there is 100 items, all except one operate for 100 hours.
One failure happens 50 hours.
MTTF = (( 99 items x 100hrs ) + (1 item x 50 hrs)) / 1 failure = 9950 hours
----
I believe you have been calculating MTBF
mean time BETWEEN failures
This measure is based on recorded events.
Mean time between failure (MTBF) refers to the average amount of time
that a device or product functions before failing. This unit of
measurement includes only operational time between failures and does
not include repair times, assuming the item is repaired and begins
functioning again. MTBF figures are often used to project how likely a
single unit is to fail within a certain period of time.
https://www.techopedia.com/definition/2718/mean-time-between-failures-mtbf
the MTBF of a component is the sum of the lengths of the operational
periods divided by the number of observed failures
https://en.wikipedia.org/wiki/Mean_time_between_failures
In short the data you have in that table is suited to MTBF calculation, in the manner you have been doing it. I'm not sure what the lambda reference would be discussing.