Righteous data analysis approach for this industrial process monitoring
The problem you are looking at is a common researched problem in the field of reliability engineering. If I understand you example correctly your goal is to gain an understanding in the following:

What manufacturing conditions cause certain parts (in your situation hoses) to fail sooner than others?

For each variable you mention you will have to establish whether these are constant over the life time of a part or whether they change. Here I measure time as the time that elapses once the part is used by your client in production. In reliability engineering the variables that do not change over time e.g. human operator of the manufacturing process, production batch, etc. are called time independent covariates the ones that change over time, e.g. amount of pressure put on a hose in production (if you have a sensor that reads that kind of information at your client site), are called time dependent covariates. 

You will have to transform your data such that it becomes censored survival data. This type of data means that every dependent variable is amount of time in operation from production. Since some of your hoses will likely never have a defect they will leave your observation period. Note that you do need to take these points into consideration too. When you combine all these data points you get something called right censored survival data. 

With the right censored survival data and the covariates together you can start to build a accelerated failure time model. Note that it is recommended to first start with a Kaplan Meier estimate of your survival rates before diving into AFT, but I just want to give you some pointers.

A book that I can highly recommend on reliability engineering is: 


Practical Reliability Engineering
The New Weibull Handbook


Both don't dive deep into AFT but they do explain the fundamentals of physical systems and how this links to statistics. From experience I know that not many people are working on this field and that you can have a lot of wrong pointers.

In terms of bringing a model like this in production will heavily depend on the quality of your data. Typically sensor data can be of poor quality, moreover, user input tends to be even of worse quality. An article that gives you an idea of what kind of organizational challenges you may face can be found here. Expect a project like this to take several months to years.