How do I inject the reliability of machines into my ANN

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

possible dupe of [What is the system's mean time to failure?](https://cs.stackexchange.com/questions/9526/what-is-the-systems-mean-time-to-failure) — underscore_d, Nov 03 '17 at 17:41
Thanks @underscore_d, in your example, the MTTF is given. Here in my post, I want to find the MTTF. — jou, Nov 03 '17 at 17:48
Which [DBMS](https://en.wikipedia.org/wiki/DBMS) product are you using? Postgres? Oracle? "_SQL_" is just a query language, not the name of a specific database product. — , Nov 03 '17 at 21:49
always add the database type into the question tags please, not a comment — Paul Maxwell, Nov 04 '17 at 00:01
You have changed the question utterly! It looks like it should have been a new question as it make my previous answer appear to be off-topic. — Paul Maxwell, Nov 06 '17 at 22:13
So you want to utilize an **Artificial Neural Network** (ANN) (which is well outside my exerience) This paper may be of some interest: [Prediction of Vehicle Reliability using ANN](http://paris.utdallas.edu/IJPE/Vol08/Issue03/pp.321-329%20Hari%20279.10.pdf) I wonder if stackoverflow is really the right place for your questions. perhaps you should looks through the engineering related stackexchange areas — Paul Maxwell, Nov 06 '17 at 22:26

Paul Maxwell · Answer 1 · 2017-11-04T01:01:38.177

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

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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.

Not yet @used_by_already, but I have updates to make the Q clearer and advanced, should I edit the post. — jou, Nov 16 '17 at 02:29
Consider creating a new question instead, if the nature of the extra information invalidates any previous answer, then it should be a new question, and acceptance should be based on just the original question. Note an answer only has to help you arrive at a solution to be accepted. — Paul Maxwell, Nov 16 '17 at 02:32

How do I inject the reliability of machines into my ANN

1 Answers1

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