I am a R&D operator which is in charge to conduct an evaluation study for implementing a monitoring system for our process of production. I generalized what the software must do but I don't know which is the approach/algorithm to perform what I designed.
Here's the process description:
we produce brake hoses. The hoses are produced in a continuous process. It starts from (1) extruding a plastic tube which get wrapped in reels of about 1000mt. Then (2) these 1000mt of extruded tube get braided with a textile or steel wire. The last phase (3) is coating the braided tube from phase (2) with an extrusor with a "T" head. At the end we get batches of brake hoses in reels of about 1000mt. The variables in this process are detected by sensors and in order are:
(1)-extruder speed - temperature - vacuum pressure
(1)-diameter of the extruded tube
(1) human operator
(2) speed of the braiding machines
(2) temperature and humidity of the braiding room
(2) human operator of the braiding machine
(3) coating extruder speed - temperature - vacuum pressure
(3) diameter of the coated hose (which the brake hose = the process OUTPUT)
(3) human operator
(3) tests performed by the laboratory onto the brake hose
This process is in the time-domain as the three phases are in chronological order and the process is a linear process (it starts from an extrusion of the inner tube and ends with its coating).
The operators of the laboratory will perform tests on the finished product (the process output) which is identified by a univoque batch code. The software must correlate these data and the diameter detected in the phase (3) to the sensors data of the process of that batch of hose.
Everytime we input data into the system the software must learn to recognize patterns and find correlations among the variables.
For example: last year we had a leakage problem on a hose which wasn't determined and disappeared after one month without significant actions made onto the process. If the software had already been implemented we expected a result from its analysis like this (simplifying in a spoken language):
-"I detected that the batch n°3 had a leakage in the test of "pressure holding" same as batch n°2 and batch n°1. In all these batches I detected the coexistence of these three factors:
1) the presence of the operator John Doe which stopped the braiding machine 2 times
2) the humidity of the braiding room was 2% over the average level of the past 3 months
3) there plastic grains for the extrusion of the inner tube (phase 1) has dried only for 20 minutes instead of 40.
Of course, the software next time the process shows the same conditions listed above warns us that the same defect might happen.
Which kind of algorithm (supervised-reinforced-unsupervised) is best suited for this task and which kind of approach (decision three-neural network?
As I'm making a study to be submitted to the top management I would like to be well prepared and also don't depend to what a software house says it is the best solution because they tend to be "smart" when somebody is ignorant on an argument.